Digital development in the insurance industry has become greater and more successful in the past ten years. Recently because of pandemics it has gained even more value and has undergone some important changes.
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!
