Definition of AI and Its Various Applications in Software Development

Artificial Intelligence is one of the oldest concepts, originating from ancient times and myths. However, it was not until the 20th century when old ideas and crude prototypes got practical realization. Many prominent mathematicians, logicians, and computer scientists joined their efforts to define the concepts of “reasoning” and “learning” in the context of computers. The works of Alan Turing, Arthur Samuel, Tom M. Mitchell, and many others created the base for the progress of AI and ML technologies and computer algorithms and programming languages in general.

The generally recognized features that define AI are the ability to solve complex problems and the ability to generate information based on the source parameters. This way, researchers focus on the similarities between “human” thinking and “machine” thinking. Artificial Intelligence also should have the ability to learn, with human supervision or without it.

Thanks to the rapid progress of AI and ML technologies, they have been successfully integrated into many key areas of modern life. For example, the bond between AI and software development turned into two-way beneficial cooperation. This means that Artificial Intelligence has two major layers of interaction with software engineering.

1. It is an immensely useful tool that helps developers with numerous tasks. An AI paired with great computational power and data storage capabilities easily outperforms human abilities.
2. It is an object of software developers’ labor. Software engineers are often tasked to create an AI for specific purposes or to integrate an existing AI into a solution that is being developed.

Let’s take a look at both of these areas of interaction.

The Applications of Artificial Intelligence in Software Engineering

Software developers have mastered the art of training and customizing AI for various purposes. In general, state-of-the-art AI has unparalleled abilities for data mining, analysis, and classification. Those abilities also make Artificial Intelligence a great assistant capable of understanding requests and finding solutions. Here are a few examples of using AI technologies in software development:

Code Generation

Modern AI can write code based on given parameters. The results, on average, are quite satisfactory but not enough to replace a skilled programmer. However, specialized AIs and learning models are getting progressively better. It is just a matter of time until they become on par with junior-level specialists. This pace of progress should be a clear signal for software engineers to keep improving their skills.

The machine-written code still requires comprehensive checking from experienced specialists, especially regarding its security. In the near future, this tendency may change the role of software developers. It is possible that AI will perform the “writing”, and developers will switch to “editing” the code. This way, Artificial Intelligence will do the bulk of hard and mundane work, and software engineers will oversee this process.

Requirements Gathering

Developers often use Artificial Intelligence to compile a set of requirements for a future software product. It optimizes workflow and reduces the flow of documents associated with this task. AI proves to be extremely useful in demanding projects with complex requirements.

For example, Watson AI, developed by IBM, is especially useful for requirements management. In addition, it effectively assists in other stages of the software development life cycle. The use of AI eliminates or at least reduces the need for going through lots of paper and digital documentation manually.

Planning, Estimating, and Designing

Based on the gathered requirements, AI can take over the rest of the software development process. It is more efficient in tasks that require calculations, such as estimating costs and scheduling deadlines. It also can determine the development team setup and the number of required specialists to meet the deadlines within the set budget.

Of course, this process is closely supervised by managers. So, the role of AI here is to make rough drafts rather than final plans. It can also make suggestions regarding workflow optimization. However, humans still have the final word regarding AI advice.

Quality Control and Feedback Management

Artificial Intelligence can also assist developers by running error scans and looking for vulnerabilities in code. Software engineers can train AI using the existing guidelines and checklists for ensuring software security. Then, any particular development company can customize and enhance obtained machine learning models individually. The specialists can turn those models into intellectual property by training them on every software product created by that company.

AI is also invaluable for gathering feedback from users and testers in a convenient automated form. It can analyze reports and divide them into categories depending on the nature of feedback (positive/negative), the severity of a reported error (critical/major/minor), and other parameters. The resulting structured data will be much more useful for the purposes of QA than the bulk of assorted reports.

Areas and Industries That Use AI-powered Solutions

As was described above with reference to software development, specialists use AI for two general purposes. One purpose involves Artificial Intelligence performing tasks automatically with or without human supervision. The other purpose is to improve the productivity of human workers by assisting them and enhancing their abilities. Below are a few illustrative implementations of AI in different areas outside software development.

• Data mining. One of the main purposes of AI nowadays is to gather enormous amounts of raw data on the Internet. It is the task of web crawlers that are used in search engines, including Google, Bing, Baidu, and others. However, those and other interested companies, such as Meta, Twitter, Amazon, etc., are also collecting information about their users. Every user’s activity on the Internet is usually tracked by several companies simultaneously. The resulting data flow would be impossible without the help of Artificial Intelligence.
• Data analysis, classification, and statistics. AI is also responsible for analyzing user data mainly for the benefit of Internet corporations. It defines behavior patterns, habits, and preferences for every user account. The resulting data is used for targeted advertising and other types of personalized content. The latter is especially true in the case of social networks and streaming services.
• AI assistants in smart devices. Artificial Intelligence is often integrated into gadgets to make them more convenient and resourceful. The most common types are, of course, smartphones, tablets, and speakers. This is because they have the necessary hardware to listen to users and respond, preferably using AI-generated voice.
• Autonomous driving. Artificial Intelligence enables AI-assisted driving in the form of an autopilot that can be toggled on and off.  It also allows fully autonomous driverless operation of vehicles in certain environments.
• Gaming. One of the earliest showcases of AI was in the form of chess computers. Specialized versions of AI are also used for such table games as go, backgammon, and poker. Artificial Intelligence is widely used in the video game industry, both in single-player and multiplayer games. It controls enemies, NPCs, and bots in order to react to players’ actions.
• Writing and maintaining a dialogue. ChatGPT and its analogs are the hottest topics in all the tech news these days. They can hold written conversations with users and are often tasked to write letters, articles, and even scientific papers.
• Creating visual art. The combination of AI and ML gave several amazing tools for visual artists and designers. Midjourney, DALL-E 2, and Neural filters in Adobe Photoshop are the most popular examples of such instruments.

The Benefits of Artificial Intelligence for Developers

From the description above, it is clear that Artificial Intelligence and Machine Learning offer many advantages. Most of them can be effectively applied to benefit software development. Here are a few ways how AI improves the work of software engineers:

• Precise planning
• Predictable workflow
• Informed decision-making
• Accurate estimates
• Better security and QA
• Reduced workload

Overall, the main benefits of AI and ML in software development come down to higher efficiency and lower costs. Artificial Intelligence helps to automate many time-consuming processes. This allows managers to switch specialists from boring routine tasks to more creative and responsible assignments.

Additionally, these benefits offered by AI allow engineers to tackle more complex tasks that would otherwise be unfeasible or extremely costly. Ultimately, Artificial Intelligence enables the creation of more enhanced versions of itself. And those better versions will be able to solve even more difficult tasks, thus increasing the benefits.

Challenges of Using AI in Software Development

1. Higher demands for specialists’ skills. AI is a sophisticated tool capable of performing many tasks. However, to fully utilize its potential, it must be operated by a specialist with sufficient skills and experience. The main requirement is the ability to formulate all the input parameters with maximum precision. In other words, users must say exactly what they want to achieve and describe it in full detail.
2. Advanced tools require large computational power, storage space, and energy supply. A software development company that heavily relies on AI technologies has to invest in top-tier hardware. Better equipment usually means that AI would perform its tasks faster. In its turn, this would speed up the work of the development team and reduce the time to release the final product.
3. Extra licensing costs. Professional tools are not cheap, especially in the software development business. Most state-of-the-art AI and ML solutions involve a hefty price for their commercial use. However, the costs are reasonable and more than justified. AI-enhanced tools quickly pay for themselves, especially when they are used by skilled software engineers.

The Future of AI in Software Development

It is safe to admit that AI technologies are the present and future of mankind. They have been successfully integrated into both personal and professional aspects of our lives. This means more work for software developers who create and implement custom AIs.

The combination of Artificial Intelligence, software engineering and Machine Learning is growing more powerful as the technologies improve every day. Moreover, software developers can now use specialized AIs to create other AIs. The circle has come to a close. But it also created the foundation of the constant evolution of Artificial Intelligence, allowing engineers to create better versions of AI with improved abilities.

Though the use of AI simplifies the work of software developers, it also demands high levels of skill and knowledge. This is especially true when engineers need to integrate Artificial Intelligence into custom software solutions. Our team in Intellectsoft has enough know-how and experience to handle complex and challenging AI-related cases. We offer a range of AI development services for various industries. Contact us to bring the advantages of Artificial Intelligence to your software.

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The Current Situation and Main Tendencies in the IT Industry in 2023

Recent years were full of historical events, and the latest software development trends reflect how the IT field adapts to those phenomena. Technological breakthroughs, climate changes, and local conflicts are among the many reasons that constantly change the preferences of the coding community. Let us review the most popular trends, observe their dynamics and find the reasons behind their current popularity.

The Revolution in Artificial Intelligence.

While Artificial Intelligence was one of the top trends in software development over the last few years, the situation changed completely in December 2022. When Open AI launched its ChatGPT, the digital world experienced one of the biggest revolutions in its history. The immense scope of abilities of this chatbot is shocking and slightly frightening. It can maintain a meaningful dialog with the user, write an article or even a thesis on a given topic, and much more. For example, GhatGPT can write computer code based on a user’s request.

Naturally, program developers around the world started to investigate and exploit this opportunity. This feature allows a person with little to no knowledge of programming to create a basic-level software product or module. The more this chatbot learns, the more advanced its skills become. It seems inevitable that before long the AI will both write programs and teach programming languages to junior developers. Such industry giants as Microsoft and Baidu have already integrated AI chatbots in their search engines, browsers, and other solutions.

Other brilliant examples of interactive AI are Midjourney and DALL-E 2. These tools use deep learning to generate images based on the written description. The rise and evolution of such self-developing models have already caused concern among professional designers. As a related trend, Tensorflow is still the dominant platform for machine learning, thus retaining its popularity over the last few years.

New Horizons for Big Data Analysis.

All modern IT “whales” are deeply involved in Big Data and successfully turn it into immense profits and influence. Google, Meta, Twitter, Amazon, and other huge companies gather tremendous amounts of information and analyze it to create user behavior patterns. This way, they can provide personalized services, such as advertising, selected content, services, and so on by predicting customers’ needs.

The related trend that has appeared just recently is called the Internet of Behavior (IoB). It combines the Internet of Things (IoT), predictive analytics, and behavioral research. This concept involves gathering data from a variety of smart devices, including wearable gadgets and household appliances. Then this data is combined with various information regarding online activities, such as search history, watched content, played games, ordered wares, and much more.

Ultimately, an IoB platform can create a concise database that summarizes the preferences and behavior of a particular person. This extensive structured information can be used for providing a variety of personalized online content, including ads, news, articles, videos, and much more. The increasing popularity of IoT devices greatly speeds up the development of IoB solutions. Overall, the Internet of Behavior will certainly remain one of the dominant trends in the foreseeable future.

Alternative Approaches to Programming

The preferences regarding programming languages among software developers generally remain unchanged in relation to the last few years. According to the TIOBE index, Python is still number one in the world, and C is still the runner-up. Rust and Scratch are gaining popularity and rose by 4 points in the index, while Swift has dropped by 3 points over the last year.

While the developers keep using the same programming languages, the situation around coding approaches has changed drastically. A few new ways of creating software have sparked massive interest among specialists. One of the hottest trends in this industry nowadays is no-code development. A step forward from visual programming languages and low-code development platforms, this concept reduces the requirements for developers’ skills even further. Such services as Bubble, Webflow, or Adalo offer advanced functionality and various templates to create mobile and web applications without writing any code.

Naturally, the no-code approach will not seize the whole software development industry. However, it allows more people to express their ideas and create program products in an extremely easy fashion. The same purpose is one of the main reasons why AI-assisted programming also gets increasingly popular. Nowadays, virtually anyone can ask an AI bot like ChatGPT to write a fragment of code to solve a given problem. Of course, the no-code and AI-assisted methods do not meet many quality and security requirements. This way, they are not suitable for creating software for startups and businesses.

As an example of an ongoing software development trend, microservices remain very popular. Amazon Web Services, Microsoft Azure, and Google Cloud are still the top three players in the field of cloud computing. Their reputation and demand have not declined over the years, and they remain trusty platforms for providing a large variety of computing services and resources.

High Demand for Security and Decentralization

One of the new trends in software development is the novel concept of the World Wide Web called Web3. The idea involves making the Internet more decentralized and secure, mainly through the use of blockchain technologies. While nowadays blockchains are generally associated with cryptocurrency and non-fungible tokens (NFTs), tech specialists believe that this situation will change soon.

Decentralization is believed to be the potential solution to one of the biggest concerns in the IT industry. Some corporations have gained too much influence over the digital world and have almost monopolized several essential services and environments by suppressing their rivals. The software development community is actively looking for effective ways to minimize the dependence on those corporations and their products.

In the field of software development, the decentralization trend also refers to the structure and operation of an application. This approach focuses on ensuring the autonomous and uninterrupted work of software. For example, if one software module malfunctions, the rest of the platform should work. If several servers go down due to a sudden power shortage, the remaining part of the infrastructure should remain operational.

IT Services Outsourcing

The situation with human resources in the software development industry is extremely volatile. It seems just a few months ago, there was a dire shortage of tech specialists, and nowadays, the situation has reversed. There were massive layoffs by major IT companies, including Amazon, Alphabet, Meta, Twitter, and several others. The numbers are shocking: more than 70000 employees of the largest tech enterprises have lost their jobs, and new waves of layoffs are expected.

These measures show the new tendency when more and more companies resolve to outsourcing rather than having massive development departments. Businesses are desperately looking for ways to minimize their losses, and outsourcing is a very viable and cost-effective way of doing this. That’s why the rapidly rising demand for outsourcing services is the latest trend these days.

Why It Is Important to Monitor Trends

Modern software development trends represent the current needs and changes in the industry. If a company keeps a close eye on them, it understands and predicts the desires of its customers more accurately. That’s why Intellectsoft realizes the importance of researching and following the latest tendencies.

Knowledge of trends allows us to learn and use advanced software development tools even before they become mainstream. To maximize the efficiency of our work, we combine cutting-edge tools with time-proven technologies, thus uniting modern trends and conventional means. This allows us to create secure and effective custom software solutions for businesses operating in various industries.

For example, our knowledge of the IoT and security trends allowed us to create a solution for smart fridges with RFID tags and other peripherals. The recent trend regarding cryptocurrencies also did not catch us off-guard. We have developed a secure yet simple blockchain wallet to manage operations with Bitcoin and Ethereum. Overall, the information about modern trends allows our company to remain competitive and able to deliver effective solutions. It also challenges our specialists to master new tech and skills.

Last But Not Least

The developers in Intellectsoft take a keen interest in various new trends in software development. Learning about new tendencies, tools, and methods allows us to improve our skills and remain in demand as tech professionals. If you want to implement the latest trends in a custom-built software solution, contact us. Our specialists will find efficient ways to embody your idea using modern tendencies in programming.

The post Top Software Development Trends in 2023 appeared first on Intellectsoft Blog.

Therefore, society is getting used to the presence of technologies in everyday life and is learning how to benefit from them. On the other side, the medical facilities should have all the necessary software at their disposal to get the most out of the gathered healthcare databases.

The digital transformation of the healthcare industry results in enhanced performance of medical facilities and better patient experience.

Over the past year, organizations have acquired the latest technologies to support their patients virtually and provide real-life expertise.

As a result, technical devices and software gather volumes of healthcare data that companies can use for various purposes. Thus, big data analytics in healthcare is essential for the industry.

What Is Big Data?

Gartner defines “big data” as “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation”.

Big data in healthcare is a term that defines big quantities of information regarding the processes in a certain healthcare facility.

These technologies collect various patients’ records and help to manage hospital performance, as previously it was challenging to analyze high and complex volumes of the information.

Big data analytics in healthcare is driving significant changes in the industry. Proper data analysis will lead to valuable insights vital for healthcare function improvements, develop cutting-edge solutions, and overcome industry inefficiencies.

Types of Healthcare Data

Healthcare data collection and appraisal are divided into many different categories. Big data analytics in healthcare often uses most of the following types of healthcare data. That is why they are a very important element of this industry.

Administrative Data

When providing various medical and clinical services, medical institutions and organizations may collect information about the most frequently used and the most profitable services for a particular organization/institution.

Usually, such information is collected from each patient separately based on appointments, registration in certain programs, and more. Such administrative data may consist of the frequency and duration of visits.

With this information, healthcare organizations can more effectively allocate their resources and understand the specific needs of people. That’s why healthcare ERP software become more and more popular in this industry. 

Electronic Health Records

Electronic health records (EHRs) contain all information about a person’s medical history. That also includes their previous illnesses, treatment, and treatment results.

This healthcare data is created and kept up to date by doctors and other staff. This type of big data in healthcare helps patients heal faster. Healthcare providers can use this big data analytics in healthcare to exchange information so that everyone has an understanding of the patient’s needs.

Clinical Data

There are certain healthcare facilities, such as nursing homes, that are required to provide standardized healthcare data.

This data is used by various medical institutions to understand how to properly allocate resources between different institutions. Big data analytics in healthcare also provides an assessment of the performance of a facility.

Insurance Claims Data

Various healthcare organizations can study big data analytics in healthcare to understand the types of services that patients are looking for.

This information can be used in a variety of ways, for instance, to identify the best and the most cost-effective ways to treat a particular health condition.

Patient Surveys

Medical institutions also conduct surveys of their patients to find out what health care services they were satisfied with and what services they didn’t like.

This big data analytics in healthcare can provide physicians and administrators with opportunities to improve their work processes.

From now on, medical organizations have wide access to big data in healthcare. They can view and analyze patient data from mobile health programs. It is vital to improve the surveillance of chronic diseases.

Benefits of Big Data in Healthcare

Here is how big data in healthcare can help hospitals to cope with all these data types more effectively and improve their daily tasks and treatment outcomes.

Improves Patient Healthcare

The vast amount of knowledge and information that has been gained from the interaction of big data and healthcare allows various healthcare providers to gain deeper knowledge and clinical understanding.

Data sets in healthcare help improve patient care. Everything is explained by the fact that with the help of healthcare databases, doctors can prescribe effective treatment, as well as make more accurate clinical decisions, additionally eliminate any ambiguity.

With big data in healthcare, the industry is moving closer to improving patient outcomes as data is used to determine which methods are most effective for patients.

Predicts Patients at Higher Risk Efficiently

With aggregate data in healthcare, you can easily identify the segment of patients in a particular region or city that are at higher risk for diseases. It also provides early intervention tips to protect patients.

This kind of prediction is also very effective for describing certain chronic diseases.

Easy Patient Diagnostics with EHRs

This is one of the most common applications for data sets in healthcare. When each patient has their electronic health records (EHRs), efficient patient diagnosis can be easily achieved.

This data includes medical history, patient allergies, a treatment plan of previous illnesses and diagnostics, and many other details.

Doctors can easily access patient files and add updated information about illness or treatment plan details to them. In addition, with this system, you can notify people about an appointment with a doctor.

Ensures to Reduce Overall Healthcare Costs

Healthcare providers who are using aggregate data in healthcare can identify patterns that lead to a better and deeper understanding of the patient’s health and body.

As a result, it will lead to cost savings and resources optimization, because funds will not be spent on unnecessary services or hospitalization.

These data can also indicate clinicians how to better and more effectively treat patients, in some cases to fewer hospitalizations or readmissions. That helps to reduce treatment costs not only for medical institutions but also for patients.

Moreover, with the help of the interaction of big data and healthcare, it is possible to estimate the individual costs of patients for treatment, helping to greatly increase the efficiency of healthcare, through detailed treatment planning.

Delivers Greater Patient Insights

By analyzing big data in healthcare, you can better understand which categories of the population are at greater risk to suffer from various diseases. It also allows to quickly take appropriate preventive measures.

Interestingly, such data can be used to inform and educate patients to take responsibility for their well-being. What’s more, combining clinical data into one big data helps improve the effectiveness of patient treatment plans, delivering better outcomes for patients.

Big Data Use Cases in Healthcare

There are many use cases of big data and healthcare. It can help healthcare institutions make financial resources planning more effective, conduct research easily, and even find new ways of treating diseases. Now, let’s take a look at the most successful big data use cases in healthcare.

Big Data and EHRs

Electronic health records (EHRs) are one of the most important use cases for big data in healthcare. The use of the EHR is governed by law and HIPAA standards. Through the use of EHRs, it can multiply efficiency and improve coordination of care, as well as reduce health care costs.

This is because duplication of tests and prescriptions between doctors and providers can be avoided. Another equally important aspect that can lead to more targeted therapy is predictive modeling.

This method is based on the analysis of data from other patients with similar diseases, genetic factors, and lifestyles.

Based on information from real-life clinical appointments, physicians and other healthcare professionals can find connections between different diseases and improve therapies thanks to big data in healthcare.

Real-Time Alerts

Real-time alerts are another important use case of big data analytics in healthcare. Such warnings can prevent the occurrence of various acute diseases in patients.

For example, a joint project by IBM and OhioHealth, which uses healthcare data analytics, installed sensors that measured handwashing practices at a hospital in the United States.

The sensors recorded each time the hand washing machine was used and then relayed this information for reporting and research. As a result of the experiment, it was revealed that the number of people who washed their hands increased by as much as 20%.

Predictive Analytics

Another use case is predictive healthcare data analytics. For example, hospitals use data sets in healthcare to predict admissions rates. The system operates with data that has been accumulated over 10 years.

This includes all hospitalization records for the past 10 years. It helps to predict the number of patients expected by day and hour using time series analysis techniques. One of the biggest benefits of this use of big data in healthcare is more efficient resource allocation and better patient treatment outcomes.

Research for Cost-Effective Treatment

Another healthcare sector that is getting tremendous value from healthcare databases is Pharma. Here healthcare data analytics is used to explore the most cost-effective drugs. Many drug companies are conducting various studies to find the most effective and cost-effective treatments for chronic diseases.

Conclusion

The digital transformation of the industries is going to accelerate in the near future. Thus, it is crucial to keep up with the latest technology trends and use them to enhance the performance of the organizations.

Big data analytics in healthcare bring valuable insights that change the way hospitals deliver their services to patients, improve their functionality, and develop new solutions that can bring benefits in the future.

Companies related to the healthcare industry should consider the advantages of big data in healthcare and make the most of them.

Technologies help us gather valuable data that can be analyzed and used to optimize service delivery, increase patient engagement, improve the industry’s supply chain, etc.

If you want to utilize the power of big data in healthcare to take your organization to a new level, reach out to Intellectsoft.

We have vast experience implementing big data solutions in the healthcare niche and would be glad to work with you on your project so don’t hesitate to contact us!

The post Big Data in Healthcare: Usage and Core Benefits   appeared first on Intellectsoft Blog.

With the spread of COVID-19, insurance companies all around the globe started working remotely and thanks to the well-developed technologies nowadays, employees and employers can benefit from the productivity going up and costs going down.

It has added convenience to customers who embrace the new opportunity of purchasing insurance online as they can rely on digitally enhanced services that give this chance.

A socially distant world with modern trends such as remote working, digital payments, and online data exchange has developed numerous digital transformations, which have become a necessity.

In this article, we will discuss all the benefits of machine learning in insurance industry and how it helps automate insurance business. So stay tuned!

Importance of Automation in Business

There are two ways businesses have always managed costs and reduced uncertainty during difficult times. It has been possible by adopting automation and changing work mechanisms.

To reduce physical proximity in the workplace during pandemics, adopting automation and Artificial Intelligence (AI) takes place.

According to a recent McKinsey survey of 800 senior executives, more than 500 of them admitted that they are in the process of implementing automation and AI.

It is possible to notice that work areas with a lot of human interactions are the first ones to adopt automation. Some other factors that can demonstrate the need for automation are the high volume of tasks and places where many people are required to do the jobs.

To reduce the number of workers and deal with growth in demands, many corporations set up AI in call centers, supermarkets, and factories.

In those areas it is easy to see the boost of productivity, the work processes become more clear and transparent, as well as the number of errors that occur because of a human factor diminish significantly.

Importance of Automation in the Insurance Industry

The insurance industry, like a big number of other industries, is overwhelmed with data from numerous sources such as social media information, paper documents, online activity, wearable devices, etc.

They call for machines to work on that information and find some analytical ideas. Anyway, it is not the easiest task to maximize the advantages of machine learning.

The majority of insurance companies work only with the smallest part of data they have access to as most of the classified data is stored somewhere in traditional databases, usually manual paper-based systems.

Also, it is complicated to work with unstructured data, so it is often left out. This way insurance companies are unsuccessful not only in getting value from their structured data but also can miss some important information in their unstructured data.

Foremost science techniques are needed for evaluating the unstructured data and using the results for making better professional decisions.

The biggest gain from machine learning in insurance field is that it can work with structured, semi-structured, and unstructured data. It can provide exceptional accuracy to asset customer behavior, risks, and demands.

How Machine Learning Enables Automation

Intelligent Document Processing

For many years insurance organizations have been using Optical Character Recognition (OCR), which can work with physical documents, adapting typed as well as handwritten text into machine-encoded text.

OCR uses standard templates, and there sometimes appear tiny mistakes in critical information, for example, someone’s name, price, or date.

Those small blunders can be a reason for big problems, that is why files converted by OCR require a manual check, and this whole process can’t be called automated.

A modernized alternative to OCR is Intelligent Document Processing, which applies computer vision, language transformation, and deep analysis to structure files, make document quality higher, and sort data to make it appropriate for usage.

Robotic Process Automation

To digitize rule-based organizational tasks insurers apply Robotic Process Automation (RPA). It is often used for conversational process automation (CPA), which is needed for chatbots, interactive voice response, and virtual customer assistants that are handy for routine tasks and business dealings.

Such automated insurance systems make it possible for customers to get help with basic account questions 24/7, while customer service providers have more time to tackle more complicated matters.

5 Ways Machine Learning is Applied for Insurance Automation

#1 Insurance Advising

Computers can serve as great helpers in customer service. Automatically generated insurance advice is normally determined in the way that it precisely corresponds to the customer’s needs.

Computers give passing personalized solutions as machine learning algorithms analyze the profiles and suggest the most appropriate products.

#2 Claims Processing

Insurance machine learning improves processing efficiency beginning with claims registration up to claims settlement. Numerous carriers have taken this idea into action as automatization decreases the processing time, which improves customer experience.

Automation in insurance industry can also supply insurers with quicker information about claims costs.

#3 Fraud Detection

It is estimated that due to insurance fraud, the companies in this branch lose more than US $40 billion per year. Common schemes for fraud include Premium Diversion, where insurance agents don’t send money to the underwriter and keep the funds for personal use, and Workers’ Compensation Fraud when the entities try to sell workers’ compensation insurances at a reduced cost, leave money to themselves, and never take out real insurance.

Thanks to machine learning, identifying fraudulent claims becomes faster and easier and AI provides high accuracy in it.  Machine learning in insurance is exceedingly better than traditional models as it can analyze structured and not-structured data to recognize the danger.

#4 Risk Identification

Machine learning helps insurers to foresee the advantages and disadvantages of their program.  Assessing and controlling threats early provides them with big competitive benefits and allows them to manage the underwriter’s time wiser.

#5 Healthcare Insurance Automation

The Healthcare system always uses a lot of documents which makes this branch perfect for applying machine learning.

Smart process automation and machine learning in health insurance industry simplifies the work with unstructured data, reduces costs, and advances patients’ experience.

Difficulties in Implementing Machine Learning

The value of machine learning implementation in insurance is difficult to overestimate. Yet, there are some challenges and things to consider before the development process.

Predicting Gains Is Complicated

It is difficult to estimate the progress machine learning can bring to the program. The necessary funding may change in the process of investigating new findings, so it is not always simple to plan the budget.

Moreover, it is too hard to evaluate return on investment and find people who would want to invest in it.

Elaborate Training Is Required

Intellectual systems, run by Artificial Intelligence, have to be trained in a certain sphere, for example, claims for an insurer.

For every separate purpose, there must be a different training system, which is not easy to install. To cover all possible schemes and strategies, the model must be trained with tons of documents.

Right Data Must Be Provided

In the situation with machine learning, the quality of data is of the same importance as its quantity. The data has to be representative in order to give a clear picture and be able to train predictive models. Finding appropriate data usually requires great effort.

Data Security

A lot of data used for training machine learning algorithms has built new dangers for insurance companies. All applications are connected and data collecting increases, so the risk of data leaks appears.

When such security incidents take place, personal data can become publicly open. This doesn’t let insurers fully trust machine learning.

Conclusion

Machine learning in insurance can help your company advance and thrive. Custom insurance software development services can adapt to your project demands and professional needs to conduct planning, designing, and other tasks for you, and support you on your way to success

In case you are interested in implementing machine learning in order to automate processes in your insurance business, make sure to contact Intellectsoft!

We have years of experience developing innovative solutions for this industry and would help you take your business to the next level!

The post Positive Impact of Machine Learning in the Insurance Industry appeared first on Intellectsoft Blog.

Basically, that’s because it has high effectiveness, provides valuable benefits, and enhances the processes automation to ensure better performance for any industry.

According to McKinsey, one of the industries that need ML implementation the most is the finance and banking industry.

As the sector operates tons of personal data, and billions of critical transactions every second, it becomes especially vulnerable to fraud activity. Scammers are always seeking its weak points to crack the servers or get the valuable data for the blackmailing.

However, because of its innovative characteristics, machine learning can greatly benefit financial industries by detecting and preventing such fraudulent activity, thus improving the entire system security.

In this article, we will discover how this process works and the main ways machine learning in finance can be used.

Let’s start right now!

What to Know About Machine Learning in Finance & Banking

Machine learning in finance is now considered to be one of the anchor points of different fields of finance and banking services as it performs critical tasks which include transaction processing and calculation, risk evaluation, and even behavior prediction.

Being a subset of data science, machine learning has also the ability to learn and improve from the experience without being programmed, which means the technology will be improving systematically.

Machine learning along with the entire artificial intelligence (AI) development, now radically changes and improves the traditional processes of financial services.

One of the critical aspects is detecting the fraud activities, providing the instant response to any suspicious actions, and getting plenty of valuable insights to learn from for fighting further cases of such activities. Here are some examples of how it works:

• Learns from the payment data to perform the services automatically. For example, ML technology can process the payment details for providing the analytics services.
• Detects the fraud activity and minimizes the risks of the intrusion into the payment system. For instance, machine learning can analyze the accidental false positives in fraud detection, thus preventing cost loss.
• Respond to the unusual aspects of payments. For example, ask for double authentication to confirm and complete the sale transaction.

Thus, thanks to the process automation and deep learning features, fraud detection machine learning can greatly improve the quality of the financial industry services and minimize the risks of the third parties’ intervention to protect the most critical and vulnerable processes.

Fraud Detection Using Machine Learning: Main Tasks

The finance and banking industry consider machine learning to be extremely helpful in financial fraud detection. Unlike the other innovative technologies, it will never be outdated, instead, it becomes even more valuable after being able to analyze more complex cases.

Furthermore, the services which have been established on the hand-crafted rules, which are also known as “if-then” statements, with the ML technology will be performed automatically.

According to Deloitte this greatly reduces the risks of missing suspicious transactions, detects similar cases, and prevents new ones.

So, let’s now cover which tasks can perform machine learning for finance.

Decreasing the Cases of Security Breach

Fraud is one of the most common issues the finance industry usually faces with. By operating with billions of dollars, tons of personal accounts, and large corporations, it becomes one of the primary targets for fraud.

And, when technologies become more and more developed and powerful, the number of fraud attacks also increases.

In addition to that, most financial units tend to store the big data online, which only introduces additional risks to the security breaches.

With the machine learning systems implementation, financial institutions can get various technologies to combat the frauds, create a set of rules that will be automatically improved with the times and provide the highest level of security to it.

Basically, it works by comparing every new transaction with the previous (personal information, data, IP address, location, etc) and detects the suspicious cases which should be checked additionally.

As a result, financial units can conduct credit card fraud detection using machine learning.

Analyzing the Credit Scoring

Nevertheless, machine learning and finance are not only about credit card transaction checking. It can also greatly benefit credit scoring automation, as it implies complex analysis of various data, such as personal information, payment history, current income, and even the credit history from other financial units before making the final decision about each case.

Machine learning can easily resolve this issue by involving various algorithms that are constantly improving to automate the process and enhance the quality of the credit scoring process, which means large investment savings and workflow enhancement for the financial units.

Besides, it can process not only the personal information of a debtor but also detailed processing of a person’s social media accounts to issue the quality report of the data analysis.

And, fraud detection using machine learning makes it nearly impossible for scammers to influence the credit scoring process and make a profit from it.

Suggesting the Best Investment Strategies

Financial fraud detection is also critical while choosing the best time and areas for investing. Before making any investment it is significantly important to evaluate all the risks and benefits you can get from this process.

Furthermore, the fraudulent cases in the investment at present practically always lead to the irreparable loss of finances, and that is where machine learning in fraud detection can really make sense.

The financial advisory systems provide you with the critical data where the possible risks and benefits are analyzed using the machine learning algorithms.

So, people usually consider that specific technology while leveraging the large investments, like different retirement or student loans, trading, investing, etc.

Machine learning for fraud detection helps people make sound decisions for their high-value financial transactions.

Why Do You NeedMachine Learning in Finance?

As you can see, ML technology can also bring great benefits to the financial industry. Along with the most important benefit of financial fraud detection, it can also provide the banking and finance units with the following gains:

Assists in Processing Big Data

Because of the necessity to work with huge amounts of personal and corporate accounts, machine learning can save lots of time, costs, and effort by automating the process of data analysis.

Besides, the financial institutions will be also able to ensure the data is secure, and can’t be obtained by third parties.

Reduces the Possibility of Human Error

In the middle of the last century, human error was the #1 issue of why financial institutions experience losses. Machine learning can replace human work by automating the traditional process in this industry, which means a lower error rate.

Is Transparent and Bias-Free

The decisions made by the machine learning systems in some cases might produce better and more transparent results than human judgments, but make sure the application has gained enough knowledge for more quality biases.

Makes Valuable Predictions

By using the specific algorithms and previous experience analysis, machine learning can provide a list of suggestions that might prevent and alert the financial units about the possible outcomes in different areas, like fraud detection, investment predictions, and so on.

Thus, the finance and banking industry will be able to apply preventive measures or change the current state of affairs, thus, getting more benefit from it.

Saves Operational Costs

With automating processes using machine learning, you can effectively perform different tasks, improve their accuracy and save huge investments of this process that are traditionally analyzed manually.

Thus, along with applying machine learning for fraud detection, financial institutions will also get the profit from its use in automating the data processing, making transparent and bias-free decisions, predicting various issues, and eliminating human error.

Being aware of these benefits, it is now one of the most popular and important technologies for the finance and banking industry.

Conclusion

As you can see, financial fraud and machine learning are practically inseparable at present times. By applying various rules and synthetic algorithms, it becomes just a perfect technology for automated financial fraudulent detection.

Unlike the traditional system of analysis, which is mostly performed by human decisions, it allows covering much more information and processes the big data in shorter periods of time, thus saving lots of investments, resources, and time for the financial units.

Fraud detection using machine learning allows creating new rules and more complex algorithms for analyzing various transactions and suspicious financial behavior thus minimizing the risks of financial loss.

That is why financial software development is now the major priority for the finance industry.

Our best expert teams at Intellectsoft, empower different companies and their workforces with various innovative solutions and approaches to develop the exact products they need for improving their performance.

Do you and your organization need one to enhance your business development?

Feel free to talk to our experts to learn more about machine learning technology and how your business can start getting profit from it today!

The post Machine Learning in Finance: How it Helps with Financial Fraud Detection appeared first on Intellectsoft Blog.

Both AI and blockchain have the power to revolutionalize traditional industries, and it’s common for key executives to examine these technologies with interest and fear.

At the same time, the zest of insurance companies in AI and blockchain is weaker than on average, comparing to other market sectors.

As Deloitte states, only 1.33% of insurance vendors actually bought AI tools; it’s more common for them to pay attention to the internet and software — the indicator here is 32%. When it comes to blockchain, the technology itself is mostly associated with decentralized finances, digital currencies, and the banking sector.

Nevertheless, it’s applied in insurance too: out of 200 blockchain solutions registered by McKinsey Panorama FinTech 20 were insurance-related. And their number is increasing with the growing interest and better understanding of these technologies.

In general, blockchain and AI still demonstrate relatively weak positions in insurance, comparing to the other industries. Due to these frequency lagging and additional implementation problems, we’ll show in which technologies you can invest today to gain an immediate competitive advantage.

What’s the Main Application of AI in Insurance

The ways AI improves the regular processes in insurance are numerous, but all of them are closely connected with data management.

In the current age of “datafication” of businesses and the growing power of Big Data, AI provides sophisticated yet intuitive tools for processing the information and drawing relevant insights. You can use them for your own good in many ways.

In the insurance sector, AI can help change the approach in data management from primitive data gathering to its effective processing.

For example, you can get working software to provide custom insurance packages to the clients based on the personal data collected during the entry surveys. It can advise car insurance based on the number and characteristics of clients’ automobiles. Or you can offer a particular health insurance package linked to the lifestyle specifics of each customer.

And all these advantages and personalization — without the need to spend time and human resources doing all the paperwork, linking a CRM software, learning your personnel how to work with it, and gathering the team of analysts to provide the insights.

AI can do everything by itself to give you concrete answers to the question “what to do with terabytes of data we’ve been collecting for years”. And an important next step: the technology can transform all these insights into repetitive tasks.

Just imagine how much time it saves you on doing some other important business tasks! That’s the competitive advantage essence of AI in insurance.

How Blockchain in Insurance is Applied

The short answer to the question “what is blockchain insurance?” is smarter financing. Its perspectives go far beyond money management and include higher customer engagement, better scaling, and effective digitalization.

In short, blockchain for insurance means the greater power of customer-controlled personalized data.

Thus, the main good the technology brings is to empower the traditional models with highly innovative and personalized solutions. In particular, you can manage records and process payments faster.

Another set of advantages includes better budget management, risk mitigation, improvements in customer service, and building mutual trust in communication with key stakeholders.

In practice, blockchain provides insurers with the effective tools to prevent frauds, simplify the application process, improve directory accuracy, and create records with full customer profiles.

Top 5 AI Insurance Examples

Here’s the list of key AI insurance services and innovations you can invest in these days to gain a competitive advantage in the market.

AI in Auto Insurance

In the automotive industry, an AI insurance company can offer a customized approach. As a special service, you can adapt your current process and prepare to switch to self-driving cars in the near future — as the Coronavirus global lockdown in 2020 showed, it’s important to be ready to implement new working models immediately.

In general, AI technology can assist in getting new revenue streams and being in touch with your clients through chatbots and customized communication templates.

AI in Life Insurance

When it comes to your personal life, AI can provide relevant insights with automated input management. This tool collects information about clients’ previous life events, along with their frequency, to offer personalized services and packages that really matter.

AI in Insurance Startups

InsurTech is a highly prospective direction of AI insurance development. Small firms like Clover, Fabric, GetSafe, and Trov offer selective software services on a decent level to cover the growing interest of insurance executives in the technology.

Among the solutions offered, you can request claims-handling chatbots, smart consulting on insurance packages, and quotes generation.

Lead Generation

AI insurance can help in attracting new clients and turning them into sales appointments. The software can scan social media, forums, and email databases to search for potential leads and offer them insurance services.

What’s more, the intelligence component in the technology can reveal itself in making a highly personalized message with the pre-designed template.

For example, it can offer travel insurance to a person who announced on Facebook he’s about to visit a foreign country. All the processes will happen automatically.

Fraud Management

AI can prevent severe fraud-connected problems in insurance companies. In this case, the technology will investigate the possible factors and threats while handling claims to get an objective picture of the previous events.

If a person claims he became a victim of an earthquake somewhere in Mexico, AI will scan the news from this country to check whether an earthquake actually took place and who were the victims according to the hospital reports.

This way, investing in AI technology will save money on paying investigators and covering frauds.

Blockchain in Insurance: Use Cases

Blockchain and insurance together create several interesting examples. Here are the exact things insurance blockchain startups can do.

Blockchain in Life Insurance

Like AI, blockchain can facilitate dealing with life insurance. In the case of this technology, its main strength is providing extra cost-effectiveness.

Smart contracts effectively track and deal with insurance on the micro-level, thanks to its regulated decentralization principle. As another dimension, the devices from the Internet of Things (IoT) world can assist insurers in tracking threats and preventing frauds.

Customer Engagement

One of the main problems at the stage of data collection is the unwillingness of clients to give sensitive answers to insurance agents. Blockchain can assist you here.

By guaranteeing higher protection and greater control over personal data, the technology encourages clients of insurance companies to share their information freely. You can request it as Know-Your-Customer (KYC) data service, where clients not only share identity information on a smart contract basis but also re-use it for faster and safer verification in other companies.

Blockchain in Auto Insurance

The technology can bring the payouts on auto insurance to the next level. For example, in the case of a car accident, both sides will get the necessary information to build trust: a client will get an immediate payout, while the insurer will see that the car is repaired by a licensed service provider.

In contrast to traditional models, blockchain can do it faster and through a credible network, which significantly increases speed and customer satisfaction.

P2P Insurance Operations

Smart contracts can increase customer satisfaction with insurance services when it comes to immediate payouts and claims handling. This use case is implemented by P2P flight insurance from InsureETH.

The startup offers software that scans flight information from verified sources and provides automated insurance coverage for a delayed or canceled flight.

This way, the insurance company and the client can save time and nerves during appointments and handling extra paperwork to get compensation.

Greater Cost-Effectiveness

Blockchain can save insurers money on various stages of the working process: while identity verification, checking contract terms, reporting claims, and collecting data from third parties.

In this context, it can cover the tasks normally handled by several executives in a firm and reduce the negative effect of a possible human mistake to a minimum.

Main Challenges of AI and Blockchain in Insurance

In practice, AI and blockchain complement each other: while blockchain offers advanced tools to gather information, AI can process and analyze it effectively. Nevertheless, many companies face severe implementation problems while trying to apply them both simultaneously.

In theory, the technologies sound like a brilliant idea — but it’s not easy to apply them smartly. Thus, first and foremost, it’s important to find a reliable vendor who can introduce AI and blockchain skillfully.

AI Limitations and Implementation Problems

To work in a satisfactory manner, AI needs lots of data. If your insurance company is not accustomed to collecting masses of information from clients, AI tools will have nothing to analyze and provide insights from.

In this context, it’s important that all this data is closely linked to both organizational and customer needs, meaning that information should be both relevant to insurance company activity and be about your client.

If you offer life insurance, it’s important to clarify all the risky things your client does. But checking the breakfast preferences is not a relevant question in the survey.

Blockchain Limitations and Implementation Problems

Blockchain needs certain pre-defined conditions too. Like AI, blockchain reveals its best in complicated transactions that involve numerous parties and complex reporting.

If your insurance company relies on direct transactions and has a trusted intermediary you’re totally satisfied with, you don’t need this technology. Moreover, blockchain can bring you problems in terms of scalability, safety, and standardization.

In particular, it’s not recommended to implement it for all your financial operations at once — as its consensus-based validation is not adapted yet to fast growth.

Besides, specific cyberattacks and the need to develop standards from scratch also call for taking a wise approach and switch to blockchain step-by-step and at a moderate pace.

Insurance Industry Limitations and Implementation Problems

In addition to technological complexity, the insurance industry itself is not totally ready to switch to AI and blockchain. Here, the main problems refer to data collection, the power of traditional approaches, and prejudices towards new “unknown” technologies.

Considering the essential need for cooperation during the early implementation stage, these factors can become a severe obstacle and the main reason for failure.

Among the stakeholders whose involvement is critical, consider C-level executives, tech startups, regulators, and customers. To facilitate the process, attract a software development consultant from a reputable company.

How to Implement Blockchain and AI in Insurance

If you need consultation on the need and perspectives to implement AI and blockchain in your business, feel free to contact us! We, at Intellectsoft, can recommend and build for you the requested solution and offer a wide range of extra custom insurance software development services.

You can choose the one you need by yourself or after talking to our consultant. Contact our experts and find out more about AI and blockchain technology in insurance and how your insurance company can start benefiting from them today!

The post Top Services Empowered by AI and Blockchain in Insurance: Tools You Can Request in 2021 appeared first on Intellectsoft Blog.

With having a high competition level in this field, the hospitality industry is certainly not an exception.

To beat the most serious opponents, they apply such technologies as cloud computing, artificial intelligence, machine learning, and more. Nevertheless, all these innovations can’t be developed without big data.

Today, we will cover the basic aspects you should know about big data in hospitality industry, the main functions, and the benefits of its utilization in this area. So, let’s start right now!

Big Data Analytics in Hospitality Industry: Definition and Types

Generally, big data refers to the large and complex datasets that play a key role in the functioning of various innovative digital processes and tools to provide convenient access to the data and its efficient management.

The transactions in different spheres, telecom, banking, travel, e-commerce, network, and social media platforms — and that’s not a full list of the spheres where the big data is used.

According to Deloitte, the main reason for its vast implementation is the need of industries to get the competitive advantages of it. So, needless to say, big data in hospitality has already become a standard.

The chatbots are used for enhancing the communication between the clients and staff, the smart devices for the guests’ comfortable staying, different mobile applications for booking, check-in, room managing, and service processes, etc. All these are examples of big data practical utilization.

Since big data needs constant structuring and analysis, there are several effective methods of how this can be performed. Let’s have a look at each of them.

Descriptive Analytics

A basic method of data analysis is descriptive analysis. It is considered to be a traditional form of analytics and is now widely used for detecting the changes altered in diachronic shifts.

To make clear, that’s the conduction of the past trends based on the groups of data.

Usually, the descriptive analysis is used for standard operations in hospitality, such as performance reports or pick-up reports.

Predictive Analytics

Another complex method of data analytics is predictive analytics. It implies a deep understanding of the past trends of different groups of data to prognosticate their future tendencies.

However, predictive analytics can never be 100% accurate, as it only gives expert opinions of what might happen with the data in the future.

As a rule, this type of analysis can provide some useful concepts for the hotel development strategy. For instance, you can analyze the booking history at different times to forecast the probable room bookings for the future, which is really cost-effective for planning the hotel management and developing strategy.

Prescriptive Analytics

Last but not least is prescriptive analytics, which includes complex processes of structured and unstructured data computing, based on machine learning algorithms.

And, though it has a similar structure as predictive analytics, this type goes further by consolidating different groups of data to issue the results.

For example, this data can use the location, holiday calendars, weather, traffic, and even traveling trends for getting the most probable prediction about the future market tendencies.

The Big Data Challenges for the Hospitality Industry

According to the McKinsey Global Institute, data and analytics could create value worth between $9.5 trillion and $15.4 trillion a year if embedded at scale — and a considerable number of that is given to the hospitality industry.

Nevertheless, big data implementation still has to resolve various challenges in this area. From security and applications’ management to encryption and server downtime, companies that offer hospitality services need to constantly combat any issues.

The most critical options that require regular tracking are as follows:

Data Security

One of the weak points of hotels is that they always need to conduct various transactions and operate with personal data. If there is any fraud attack, they can get a huge financial loss for addressing the consequences and restore their reputation for years!

Having realized their responsibility, hospitality institutions now have to invest huge costs in launching secure systems for receiving and storing important personal data about their clients — credit card numbers, full names, addresses, passport numbers, etc.

All these can help ensure the data is protected from third parties. Of course, it’s impossible to predict all the issues that can occur, but it is critical for the hotels to always utilize the systems with the best defense rates.

Data Backup

The next point is to ensure that the data stored can never be lost, as it leads to serious issues in the entire hotel’s workflow. This issue is extremely important for the hospitality institutions which are, for example, located near the waterfront. So, more and more hotels apply cloud servers for big data storage and operation.

Thus, even if there are some external factors that can cause data loss, hotels can easily restore it. Besides, unlike the early days, there are lots of top cloud systems with years-long track records of being reliable and safe.

Data Opportunities

The need for creating a larger and more robust database according to the latest trends and demands of the hospitality industry.

The main opportunities for creating innovative solutions for data secure storage and managing can greatly enhance the evolution of this industry.

How Big Data And Analytics Are Changing Hotels and the Hospitality Industry

In fact, there are plenty of benefits from utilizing big data in the hospitality industry. Now, let’s discover the most effective options it brings to hospitality.

Segment Your Clients & Select the Site

Big data in hospitality industry can benefit from targeting different groups of clients. According to Oberlo, up to 50% of U.S. marketers claim that the method of personalizing the websites and apps for different groups of customers, they have got 10% to their revenue or even more.

It does not only benefit the hotel’s revenue but also helps to build better relationships with the customers and identify both the strong and weak points of your business.

Include Prognostication

As it was mentioned above, big data in hospitality industry also implies the usage of different analytics for creating a strong and efficient strategy for hospitality units’ development.

Analysis of bookings, reviews of the most profitable reports, and the main trends’ detailed analytics – all these help to predict the future tendencies for creating the cost-effective plan.

Create Clients Profiles & Customer Relationship Administration

Another great advantage you can get with big data in hospitality is creating customer profiles. It goes without saying, that this method allows hotels to define the specific preferences of the guests and target them more accurately.

Also, creating customer profiles helps determine how to allocate the resources to get the most of the big data analytics. Last but not least, by creating an effective ideal customer profile, you can get a 68% higher account win rate.

Additionally, big data analytics drives to creating customer relationship management (CRM) systems to enhance customer services and provide them with a more personalized experience.

Measure Productivity Indexing

In addition, big data analytics in the hospitality industry can also benefit in evaluating the hotel’s specific metrics, analyze the business productivity and create the most effective methods of how these can be improved.

Furthermore, having diachronic records can help to estimate the basic tendencies in the hospitality industry.

Develop Your Navigation Menu & Utilize Mobile Big Data throughout the Hotel

As we have mentioned above, big data analytics submits a detailed analysis of how to make hospitality units more customer-centric.

According to HubSpot, 88% of CEOs are concerned about customer loyalty, realizing that focus on clients can greatly contribute to their conversion rate.

Navigation menu and mobile big data can improve the customer’s experience and encourage your clients to come back later. Thus, this strategy can also help to convert one-time visitors to loyal customers.

Customer Associations and Sequencing

Data mining can provide the most predictive sequence lines between different actions. For instance, if a guest ordered a room for two, they might be also interested in ordering a restaurant at the state of his in-room stay. Data analysis can significantly enhance customer service and feature unique combinations for visitors.

Estimate Customer Value

For the customers, the hotel service is not just a simple visit, it’s a kind of an unforgettable experience of changing their environment.

That’s why effective data analysis is used for detecting the various client needs and impact their behavior as well. As a result, the customer might purchase additional services like sports packages or restaurant tours for improving their traveling experience.

Usually, this process is implemented on the entire customer base first and then overlays on different segments of visitors to get the highest ROI.

Apply Marketing Personalization and Website Optimization

Needless to mention, tracking and processing of the customers’ behavior and actions are extremely important for big data analytics in hospitality. This contributes to understanding customers’ preferences and needs, and also offers unique value propositions for them.

For instance, you can make various blog posts or emails saying “We have noticed your great interest in our restaurants during the previous week, let us offer you a free meal or drink”.

Account the Energy Usage & Manage the Costs

For sure, one of the critical points for hospitality is to reduce the expenses for the internal operations. For instance, energy consumption can be easily controlled by big data!

By providing modern applications that accumulate the data like weather, electricity rates, consumption stats, etc, you can get profitable options of how to improve energy usage and costs management as well.

The Hospitality Industry Big Data Use Cases

To prove the effectiveness of big data in hospitality, we want to introduce the InterContinental Hotel Group development case as an example. This is a huge company that offers 886,036 rooms in 5,964 locations. Here are the main points of theirs you definitely should study:

Competitive Analysis

Having compared the competitor targeting strategies, InterContinental Hotel Group plans to increase its revenue by providing direct offers based on the history of reservations and suggesting a wider range of options. For instance, clients can customize their room types, select the dates available, or set up a budget.

Hospitality Platform Development

The company has also started developing a hospitality system with lots of features for the hotels included: revenue analytics, property management systems, sales catering, and point-of-sale.

Prognostication Analytics

Analyzing the internal and external factors of performance evaluation allows one to understand how to enhance the quality of services. The guests’ surveys regarding their in-stay experience, economic and industry performance data provide the main insights of creating a hotel’s strong development strategy for the future.

So, such a considerable approach can help to operate the latest information to stay on top among the competitors in the area.

Big Data in Hospitality Industry: Summing It Up

To sum up, big data in the hospitality industry is a powerful and effective method of enhancing hotel performance and improving their strategies for staying competitive in this field.

For sure, the custom software development for travel and hospitality can greatly help to operate big data, analyze it, and get insights of improving the quality of the hospitality services.

We, at Intellectsoft, assist hospitality companies and their workforces, providing innovative solutions and approaches to enhance their workflow and increase the overall business performance.

Are you and your organization seeking an expert consultation in this area?

Contact our experts if you want to implement an innovative Big Data solution into your hospitality business and stand out among the competitors.

The post Importance and Impact of Big Data in Hospitality Industry  appeared first on Intellectsoft Blog.

Leading tech companies like IBM, Dell, and Iron Mountain have already been offering this technology, but the increased usage of online tools since the pandemics is about to increase its popularity even more.

In essence, cloud computing is a specifically made data destination that cuts storage costs and serves as the enterprise transformation platform.

When it comes to healthcare, the technology is applied to overcome two major industry challenges: increasing cost-effectiveness and building a self-sufficient health ecosystem.

Given the pandemia crisis, these issues became sharp; but cloud computing’s potential is high enough to overcome them. We’ll reveal the most important facts about this technology below.

Cloud Computing in Healthcare Industry: How It All Started

Cloud computing in healthcare has been used for several years already, evolving together with industry changes. Since 2012, the shift towards consumerism and preventive approaches called this analytical tool to serve these trends too.

Precisely, the demand and supply fluctuations shifted the stability and increased the importance of cost-effectiveness, even though healthcare practitioners didn’t think in such economic terms before.

Personalized medicine has also become more popular, with the US market doubled from $9.2 billion in 2013 to $18.2 billion, as Deloitte reports.

These new tendencies towards caring about customer well-being and bringing true value-for-money called for searching for new ways to serve the industry. And cloud computing completes this task successfully.

How Is Cloud Computing Used in Healthcare Now?

In essence, cloud computing creates shared hardware accessed through a network connection, applicable for various healthcare purposes.

Simply put, this solution is an affordable technology with several superpowers, including an ability to process information in huge sets of data fast and accurately.

That’s why healthcare practitioners mostly apply cloud computing to communication, decision-making, and forecasting. The technology can create an entire IT infrastructure that unites hospitals, patients, insurance companies, and R&D centers inside a sole informative ecosystem.

Types of Cloud Computing in Healthcare

The typology of cloud computing in healthcare can come from 2 perspectives: distribution and deployment.

By Distribution Model (Hardware/Software Perspective)

1. Software as a Service. The provider delivers IT infrastructure, and the client deploys applications and its own operating system.
2. Infrastructure as a Service. The provider delivers IT infrastructure and an operating system, and the client deploys applications.
3. Platform as a Service. The provider delivers IT infrastructure, an operating system, applications, and other components within a ready-to-use platform.

By Deployment Model (Who Owns the Cloud)

1. Private. Only one particular client can use the technology.
2. Community. A particular group with the same interests and objectives can use the technology.
3. Public. The general public can use the technology.
4. Hybrid. The technology combines several clouds of different access options.

Benefits of Cloud Computing in Healthcare

1. Intelligent insights. The great thing about cloud computing is its ability to empower all the stakeholders (doctors and patients alike) with accurate data for proper decision-making.
2. Anticipative service. The recommendations and insights taken from cloud computing are powerful enough to predict the possible risks and threats before they appear.
3. Complex picture. The data sources for the technology are multiple, meaning that each healthcare system participant has access to the widest perspective possible.
4. Working with Big Data. In the cloud computing appearance, analytics can work with huge data sets, including active and extendable environments.
5. Accurate decision-making and treatment. The collection and processing capabilities of cloud computing enhanced analytics can reduce the number of medical errors significantly.
6. Cost-effectiveness. The software comes for a minimum cost, as the huge set of analytical opportunities is provided automatically.
7. Great flexibility. Since its appearance, cloud computing in healthcare has proved its ability to adjust to the industry’s needs fast and accurately.
8. Greater transparency. With cloud computing, patients and doctors share health information more freely.

Cloud Computing Issues in Healthcare

1. Lack of good specialists. The key challenges of cloud computing in healthcare are connected with an inability to get the software company that works with this brand new technology.
2. Limited functionality. Cloud computing alone is not powerful. To establish an effective analytics architecture, you will also need smart connected devices, artificial intelligence, and master data management.
3. Security issues. While adopting cloud computing, healthcare practitioners may meet some privacy challenges. This situation arises in the case of referring to unprofessional software providers, though.

Cloud Computing in Healthcare: Examples

Cloud computing is widely used in various healthcare institutions, facilitating decision-making and communication on various levels. Here, we’ve picked the 2 most illustrative cases to reveal the key pros and cons of adopting cloud computing in healthcare.

Pfizer, or Advantages of Cloud Computing in Healthcare

Pfizer is a great example of managing cloud computing platforms for greater communication and collaboration with all the industry stakeholders.

It involved the technology in Xalkori, a lung cancer drug especially developed for the special patient category with the ALK gene mutation.

The very appearance of this treatment was impossible without prior analysis made by cloud computing. Moreover, the technology accelerated the project — the timeframe took 3 years only, including the time needed for FDA approval.

Beaumont Health, or Disadvantages of Cloud Computing in Healthcare

Beaumont Health has experienced one of the greatest data breaches after cloud computing introduction.

Since this Michigan-based hospital didn’t establish the necessary system protection, the health and personal data of around 114,000 patients was hacked through a couple of corporate emails.

In particular, the cyberattack reached their contact information, diagnoses, account numbers, driver licenses, and other types of sensitive data.

What Are the Major Security and Privacy Challenges of Cloud Computing in Healthcare

The top security issues in cloud computing healthcare are connected with remote access usage. For cloud computing safety in healthcare institutions, the general weakness of wireless systems is a critical concern.

In the given circumstances, the healthcare entities should create detailed provisions to raise the importance of the risks and liabilities and prepare the project launch. With clear regulations, the movement of health-related data will be secure.

If you’ve picked the right provider, these challenges of cloud computing in healthcare shouldn’t arise. In its initial design, the technology offers multi-level privacy, data encryption, and sophisticated access control.

It meets all the legal, regulatory, and accreditation requirements. All this demonstrates an ability to work with information easily and securely, even in the circumstances of rapid innovation.

Importance of Cloud Computing in Healthcare

In addition to the numerous advantages cloud computing in healthcare possesses, the technology serves a greater mission: to establish a smart health care community, a network that enables targeted interventions (according to Deloitte report).

For the industry, it’s a huge step forward to treating lifestyle-related non-communicable diseases.

An ability to build a virtual health care system is beneficial for all the industry stakeholders, reaching from individual patients who want to track their daily habits to government agencies working with programs for the entire nation.

What Is the Main Reason that Cloud Computing Is Not Widely Accepted in Healthcare

In a broad sense, the adoption of cloud computing requires time and effort from numerous industry actors. Among all, smart technology usage calls for a unified vision from diverse stakeholders on the industry’s future.

When it comes to the implementation issues on the lower levels, the lack of thoughtful methodology is what prevents healthcare institutions from getting the maximum benefits from cloud computing.

The industry actors tend to consider technology as the treatment for all the diseases, while the reality calls for identifying clear high-value targets and crafting a well-thought-out business plan.

How to Incorporate and Overcome Most Risks of Cloud Computing in Healthcare Management

Since the technology is fresh, it’s important to get a software development professional who has skills and experience in working with traditional and experimental tools alike.

For assistance, don’t hesitate to Intellectsoft! In our everyday practice, we empower healthcare practitioners with innovative solutions and approaches. We can incorporate cloud computing too!

If you’re interested in the service, please reach our experts here. Let us know the details and craft the appropriate software for your business needs!

The post Cloud Computing in Healthcare as the Future of the Industry appeared first on Intellectsoft Blog.

Even though the financial sector is steadily reviving after the pandemic, 2020 has serious consequences today.

Among all, they determine the preferability of digital solutions along with double-checking of all the possible risks and unexpected spendings before investment.

Predictive analytics addresses all these threats. If you’re interested in this solution, here are the answers to the most common question about this technology.

Q1: What is Predictive Analytics in Finance and Accounting?

The most common application of predictive analytics in finance is detecting possible wrongdoings and neutralizing them before they bring significant harm to your business.

This risk management technology provides the tools for advanced monitoring and continuous improvement.

Q2: How Is Predictive Analytics Used in Finance?

Applications of predictive analytics in finance have a wide range — from routine activities we do every day to strategic corporate decision-making.

For example, it can assist in strengthening the security of using personal credit cards and prevent a fraud attack on an entire banking system.

Q3: Are There Any Predictive Analytics Examples?

Here are 3 high-potential financial spheres where the application of predictive analytics in financial services can bring a significant competitive advantage and greater business results.

Predictive Analytics in Corporate Finance

Companies can experience the full set of advantages provided by predictive analytics. As a CFO, you can use this technology in everyday work — from general strategic planning to concrete decision making.

Predictive analytics has answers to all the future-related questions including sales, needed spendings, and possible changes in customer behavior after the decision is made.

Predictive Analytics in Healthcare Finance

When it comes to healthcare, the greatest power of predictive analytics is providing better control over the finances in hospitals. The improvements include savings anticipation, debt collection, and better budget regulation.

For instance, one Alabama hospital used predictive analytics to launch a subsidization program to improve the cash flow, as IBM reports.

Predictive Analytics in Auto Finance

The automobile industry experiences the positive impact of predictive analytics in terms of forecasting, data mining, simulation, and optimization.

In particular, it enables smart loans and better control over pricing and personalization of the service. Here, analytics enables a better match between customer expectations and pricing scenarios.

Q4: Is There a Concrete Use Case for Predictive Analytics in Financial Services?

In practice, predictive analytics addresses numerous aspects of personal and corporate finances.

One of the most illustrative cases is working with false positives, or alarms that are determined as suspicious but really don’t bring any harm. They illustrate the problems with the classification and identification of harm in the financial system.

Detecting this problem manually when it arises takes a lot of time and is not effective. But with predictive analytics, you get access to regular testing and constant monitoring that eliminate the problem more effectively.

Deloitte offers this solution to global banks since 2013, providing continuous annual monitoring of transactions in real-time to fine-tune the existing detection scenarios and eliminate the appearance of false positives.

Q5: What Are the Benefits of Predictive Analytics in Banking?

Here are 5 top advantages of predictive analytics, relevant for all industries and business of any size:

1. More accurate monitoring. The technology tracks the coherence between expectations and reality regularly and can warn you of possible gaps in advance.
2. Risk mitigation and fraud management. With analytics, you can understand your business more accurately and detect the upcoming threats in advance.
3. Better user experience. Predictive analytics determines the strengths of your business and lets you know what to invest in to maximize customer satisfaction.
4. Product improvement. Analytics describes your product in detail, enabling you to detect and work on more accurate appearance, pricing, and distribution.
5. Informed decision-making. With the broad picture, the management board can address the issues more accurately and match the product with its customers better.

Q6: Why Predictive Analytics is Important in Financial Services?

Of course, the great predictive potential of analytics brings significant advantages to the corporate structure. But its importance goes far beyond the limits of the banking sector and reaches a better quality of customer service.

Predictive analytics can track the specifics of customer behavior and provide all the information needed for launching a product-oriented approach in your business.

This is possible because the technology shares customer feedback and preferences across the organization, providing each employee with the relevant information for the needed insights and relevant product enhancements.

Q7: What Is the Future of Predictive Analytics in Financial Services?

As Deloitte’s report on the banking sector states, analytics steadily becomes “the major part of any business initiative,” meaning its ability to mitigate serious risks connected with accounting and workforce management. And so, the more companies apply this technology, the safer environment they create.

In contemporary volatile times, the ability of predictive analytics in financial services to guarantee stability and foreseeability is what most businesses are searching for.

In terms of customer service, the analytics will develop in 4 key areas:

• Customer acquisition. The tool can provide the needed data to track individual preferences to improve the selling tactics. It will be effective if mixed with marketing and analytical capabilities.
• Service. The analytics provides the answers to important questions of how to serve each particular customer well and which communication channels to pick.
• Relationship development. By giving customers what they need, the financial institution can increase the user experience from neutrally OK to outstanding WOW-effect.
• Retention. The technology enables proper classification for launching initiatives each customer group will like.

Q8: What Is Predictive Visual Analytics in Financial Services?

Visual analytics is the technology that not only classifies but also visualizes insights. This type of predictive analytics is especially helpful for tracking customer behavior and working with Big Data.

That’s because its greatest advantage is the proper classification of customer sub-segments, especially helpful for customer retention strategies.

Q9: How to Integrate Predictive Analytics in Financial Services in Your Business?

If you’re interested in adding predictive analytics to your business processes, request the service of financial software development from Intellectsoft! In this set of solutions, we’ll improve the customer experience and the overall corporate efficiency with custom software relevant to your company.

We at Intellectsoft offer a set of various FinTech solutions, compatible with healthcare, insurance, mobile banking, digital wallets, and many other industries and types of know-how.

Talk to our experts to find out more about the topic and learn how fintech solutions might help your business grow exponentially!

The post 9 Top Questions about the Future of Predictive Analytics in Finance (in 2021) appeared first on Intellectsoft Blog.