Introduction to Data Analytics in Insurance
The technological landscape changes, and so do the industries. Today a worldwide variety of insurance exists. Still, it is challenging for clients to understand through which insurance company they should start their insurance because many questions come into customers' minds:
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Whether this company is safe or not?
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Will this company give the best offer or not?
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What is the reputation of this company in the market? And more.
Similarly, insurers can also not understand customer behavior, fraud, policy risk, and claim surety, which is mandatory before giving a policy to someone. It took years for insurers to sell directly to their customers and issue policies online while competing on price comparison websites. Many companies still have not achieved it.
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With the prefiltration of data, the use of advanced math and financial theory to analyze and understand the customer behavior and the costs of risks have been the stalwarts of the insurance industry. The analytics performed by actuaries are critically important to an insurer’s continued profitability and stability.
Traditionally companies are just looking for what happened in the past with Descriptive analytics. But now, the industry is demanding more, such as what will happen in the future (predictive analytics) and how actions can change the outcome (Prescriptive analytics).
Big data makes the insurance industry a perfect sphere for data analytics to construct basic patterns, get fundamental insights about the insurance business, and manage the complex relations between agents and clients.
What are the challenges of the Insurance Industry?
Customers find the best company, but there might be a possibility that the client is fraud or life impaired, which will create a huge problem for the insurer. Consistently evolving business environments are increasing competition and risk. Several other challenges, like theft and fraud, are also plaguing the insurance business.
The above challenges force insurers to generate insights from data to enhance pricing mechanisms, understand customers, safeguard fraud, and analyze risks. Data analytics collate more precise information about several transactions, product performance, customer satisfaction, etc.
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How is data analytics transforming the insurance industry?
The digital transformation of insurance companies has been going on for years. It has increased speed, efficiency, and accuracy across every branch of insurance companies. Advanced data and predictive analytics systems help the insurance industry to make data-driven business decisions. AI in Insurance has empowered companies with high-level data and information that is leveraged into improved insurance processes and new opportunities.
Let’s discuss an auto insurance example to understand the effect. A new level of innovation is emerging in all product lines and business functions using advanced data analytics. Rather than just focusing on internal data sources like loss histories, auto insurance started work on behavior-based analytics and credit score from credit bureaus into their analysis. Thus this analysis becomes evidence and generates insights into knowing the people who pay their bills on time are safe drivers. It makes traditional analytics advanced and more productive in that they check claim histories and demographic and physical data.
New sources of external (third-party) data, tools for underwriting risk, and behavior-influencing data monitoring are the primary developments shaping up as game-changers.
Why do we need data analytics in the insurance industry?
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Data analytics create new capabilities that empower insurers to optimize every function in the insurance value chain with the help of data-driven decision-making.
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It can also analyze a customer’s risk and determine which client is trustworthy or may cause great loss.
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It can also detect fraud, through which the greatest frauds happen.
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Customers can use data analytics to know which insurance company gives a minimum price with suitable offers.
Thus, both insurers and customers can make decisions according to data and their understanding, increasing speed, efficiency, and accuracy across every branch of insurance companies. This helps the insurance industry to make data-driven business decisions. It empowers companies with high-level data and information that is leveraged into improved insurance processes and new opportunities.
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Use Cases of Data Analytics in the Insurance Industry
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Both clients of the insurance company and the insurance company owner are end-users of the solution.
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Clients will know which insurance company is best for them to start insurance through the top 5 companies, the price from lowest to highest, and the number of customers. It will help them to choose the best company according to their requirements.
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Insurers can also detect fraud, Undertaking for impaired life customers, and claim development.
1. Insurance Pricing
Many insurance companies see deteriorating underwriting results. Less sophisticated insurance carriers become exposed where they are mispriced to make a sale. Due to comparative ratings in the insurance market, prospects can instantly compare the prices of many companies, often choosing the lowest price. The lowest cost may win the business but maybe underpriced relative to the risk. This results in costing a company potentially exorbitant amounts of money in the end.
End-User Value
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By automating the process of building and comparing models that explore cost versus risk, users can determine whether any risk they consider taking is price appropriate.
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The above dashboards show the top 3 companies with the maximum number of customers, and the top 3 companies offer insurance at minimum cost. So it will be easy for customers to grab the best life insurance for their families.
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As the bar chart shows, the 10 to 20 age group is SBI life insurance.
With the algorithms, users can be confident in the prices they charge, which is a competitive advantage that pushes adverse selection onto competitors, which, over time, will increase growth and profitability.
2. Claim Payment Automation Modeling
Many insurance claims require a manual inspection to assess the damage, leading to a long wait for a payout. It can cause claim amounts to spike out of control, a significant drop in customer satisfaction, and a potential decrease in retention rates.
End-User Value
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The above dashboard shows the top 5 Policies in which customer investment is maximum and the age group from which we can generate maximum revenue.
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So this helps insurance companies to understand which policies are more in demand for a particular age.
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We can increase customer satisfaction through this, and claims are made more quickly and efficiently.
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Hence, users can be confident in how much to reserve for incurred But Not Reported (IBNR) loss amounts.
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The user will build more robust and accurate pricing models Using the predicted developed loss for each claim as the dependent variable.
3. Claim Development Modeling
The claim amount can change drastically from an insurance claim's initial filing to full payment. Hence the ability to predict the final claim amount significantly impacts financial statements, specifically the reserves and IBNR amounts reported in Quarterly Earning statements.
End-User Value
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An extremely accurate and automatic predictive model can be built to understand better how much a claim will ultimately cost.
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The above dashboard shows which policy grabs the maximum number of customers from different age groups.
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It also shows the trend in the number of claims over a year. So this helps them to predict the future and also helps to give the best recommendation according to customers' needs.
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4. Life insurance undertaking for impaired life Customers
Life insurance companies do not underwrite customers who suffer from serious diseases; thus, doing so would require a long and expensive medical assessment process.
End-User Value
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A life reinsurer can use medical history and conditions to predict the risk of underwriting a serious disease survivor accurately.
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The insurer can identify which customers have good health prospects and directly underwrite them without further assessment, leading to more customers and reduced medical costs.
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As we can see above, clients with blood cancer have maximum chances of dying.
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The person on stage 3 or 4 also has a chance of dying soon. But we can compare that the death rate decreases with time, so it will be safe to offer cancer patients.
5. Fraudulent Claim Modeling
Fraudulent claims are too expensive and inefficient to investigate every claim. Moreover, investigating innocent customers could be a bad experience for the insured, leading some to leave the business.
End-User Value
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Predictive modeling in insurance industry can be used to identify and prioritize likely fraudulent activity.
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As shown in the dashboard, we know from which age group maximum frauds are detected.
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By using this particular incident and occupation, maximum fraud happened. It helps in two folds.
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Resources will be deployed where users see the greatest return on their investigative investment.
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Moreover, an insurer can optimize customer satisfaction by not challenging innocent claims.
Conclusion
The use of big insurance data analytics in the insurance industry is rising. Insurance companies invested $3.6 billion in 2021. Companies that invested in big data analytics have seen 30%more efficiency, 40% to 70% cost savings, and a 60%increase in fraud detection rates. Both the customers and companies benefit from these solutions, allowing insurance companies to target their customers more precisely.
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