As new technologies forge their way into diverse sectors, it comes as no surprise that the insurance sector is leveraging them for different reasons. Predictive analytics has found an important place in the insurance landscape for its ability to make data-based predictions. While predictive analytics helps the insurance industry gain great insights into customer activity and behavior, it also plays a massive role in preventing fraudulent claims and minimizing risks.
As per the Insurance Fraud Bureau, there has been one insurance scam every minute during the U.K.’s pandemic. Things are equally bad in the United States, where insurance fraud doubled to $100 billion last year.
The insurance industry is now moving quickly to mine data and track new rackets quickly. Explains Zurich’s head of claims fraud Scott Clayton, “By deploying the proper analytical tools, you can extract and interrogate the data, and use algorithms to highlight these links. By joining all the dots, you can soon identify persistent and prolific offenders.”
The pandemic’s unprecedented nature has set the tone for intelligent business practices that can shield them from fraud and help them strike back. Thankfully, predictive analytics, in tandem with big data, have answers to most of the problems insurers face.
Here’s how it’s helping the insurance sector prevent fraud and minimize risks.
Understanding predictive analytics
To determine its role, we need first to understand that predictive analytics is an analytical tool that studies historical data to predict upcoming events and ensure business practices’ effectiveness. It gives organizations a competitive advantage and helps them stay abreast of changing trends. It looks into the data collected from different communication channels to analyze client interactions, agent feedback, customer behavior, etc., to build a more intelligent, data-driven ecosystem for all.
It’s no secret that insight-driven insurers are always better positioned to strengthen their capabilities in all five areas, namely, people, process, data, technology, and strategy. Predictive analytics helps them excel on all these fronts. 67% of those who recently participated in a Willis Towers Watson survey reported a reduction in expenses and a 60% increase in sales due to predictive analytics. Most importantly, it helps prevent insurance fraud.
The role of predictive analytics from a fraud prevention perspective
Insurance fraud has a significant bearing on the entire business, specifically on underwritings and also causes a negative social impact. While undetected frauds drain finances and lead to many more scams in the future, those detected damage market reputation, and trust. Not to mention the legal issues that arise from them and the subsequent impact on future policies, procedures, and guidelines.
Predictive analytics helps insurers in the following areas to prevent frauds:
- Pricing and risk mitigation — Offer insights that facilitate decision-making and estimate the level of risk that the insurance company has to assume while calculating the premium. For instance, those who go to the gym regularly may be eligible for a discount on health insurance.
- Trends tracking — Helps insurers create new products, design new customer experiences, and deploy new technologies by keeping an eye on what’s trending in the world of insurance. This also gives insurance companies a competitive edge.
- Fraud prevention — Helps insurers prevent fraud at different levels of the insurance cycle, including application, premiums, claims, etc. It offers a sneak-peek into public records such as criminal records, medical history, and bankruptcy declarations to review data for detecting inconsistencies and preventing frauds.
Dealing with insurance fraud
What’s frustrating is the fact that insurance frauds today are highly organized and occur digitally. Insurance companies have realized that the only way to fight and prevent insurance fraud is through data mining, analytics, and algorithms based on patterns in fraudster behavior.
Digital algorithms that have been hugely helping in timely scam detection are based on data pertaining to:
Referral history — Experts have created algorithmic models to estimate the probability of a claim going beyond a threshold level referred to Special Investigation Units or SIUs. This model typically uses the historical claims data referred to as the SIU to determine the probability value. Investigation scores are then calculated using investigation scoring automation techniques to distinguish between good risk and bad risk claims.
Historically rejected claims records — Based on the belief that claims that have been historically rejected stand a greater chance of being denied for doubtful potential frauds, digital algorithms automatically scan through the claims using several parameters. Claim Risk Indicators such as a customer’s SSN, address, contact number, etc., are carefully scrutinized using clustering-based data mining techniques. Claims are then categorized as ‘clusters with high claim frequency’, specifying the level of risk.
Individuals/groups — Digital algorithms, in this case, are based on data about individuals or groups that make fraudulent claims repeatedly. Flags are triggered every time fraudulent entities are detected, and these flags help identify fraudulent patterns.
Social media profiles — Algorithms, in this case, take into account social media profiles and interaction patterns of individuals along with other details such as lifestyle, attitude, etc. It takes into account mismatches between actual profiles of individuals on social media versus their claims. For instance, if an individual has forwarded an accident claim but their social media shows them partying with friends, there is certainly a mismatch that needs to be investigated. Algorithms based on social media posts are also useful in demarcating networks or groups of fraudsters.
Hurdles on the way to insure the future
Fraud detection is no longer static, limited to place or time. Every time a new detail is added, insurers now run predictive analytics at multiple touchpoints to enhance their fraud detection capabilities. Their efforts are now proactive instead of reactive and a great deal of effort is being put into fruitful collaborations with brokers and third-party vendors to build clear channels of communication and information exchange. But the quality of data still remains a big challenge.
Going forward, the focus should be on reducing data volumes and increasing data quality while ensuring that it is readily available as needed. The funnel needs to be narrowed in a way that competent individuals carefully review the results from machine analytics.
There are legislative barriers, too, concerning data sharing and individual privacy that sometimes stand in the way of data collection. Predictive analytics, however, is helping insurers make the best of what they have by sifting through information pools to help them produce intelligent products for the future.
Empower your business with Trigent
Embark on a whole new journey with Trigent with predictive analytics at the helm. We can help you redefine your strategies to enhance risk management and ensure your future. Our disruptive suite of tools and solutions can transform your insurance business into a data-driven, efficient, and secure ecosystem.