Predictive analytics in proactive insurance fraud detection
The primary purpose of extending insurance to individuals, companies, assets, or properties is to protect them from potential losses. Unlike the popular corporate myth around insurance frauds that they are victimless, insurance frauds are very much real. They affect individuals and businesses tremendously at economic as well as psychological levels.
In fact, insurance frauds have become one of the greatest headaches of all millennial Insurance companies. Insurance companies work hard to amalgamate and analyze with painstaking metrics, all possible risks before arriving at a suitable insurance product for their customers.
The bearing insurance frauds have on the entire business is very much gruesome than what it normally and falsely seem so as petty frauds. It far, multi-fold affects the entire business ecosystem:
- Bearing on insurance under writings: Depreciating insurance risk pool metrics, underwriting and claims fraud disturbances are most dreadful, impacting insurance policies, procedures, and guidelines
- Negative social impact: Insurance and claims frauds augment insurance costs to a great extent
- Concealed insurance scams boost further scams and frauds: Whenever such frauds go undetected, fraudsters are encouraged to pursue them more and more
- Damage to market reputation, trust, and customer relationships: When incidences of fraudulent claims occur and recur, there is a definitive loss of reputation and trust on the insurance company. This negatively impacts their patronages just among existing customers alone, but also prospective customer
- Legal impact: Frequent claim and related frauds definitively create legal and regulatory issues for the insurance company, making it impossible to pursue business operations
The Challenges Ahead
Technological and digital advancements across industrial domains, fraudsters too are getting proactively tech-savvy. Swindlers have started operating using sophisticated digital and technology algorithms. This scenario is even more perplexing because it is humanly tough to handle digitally undertaken frauds. Furthering to this, the number of such frauds to have augmented multifold.
Leveraging high-tech predictive analytics to crack insurance frauds
Insurance enterprises have thus started realizing the importance and utility of high-tech solutions to deftly confront such digitally undertaken insurance frauds in the current times and future. Today’s organized, digital frauds require advanced, data mining, analytics, and customized fraudster behavioral pattern-based algorithms to be programmed for proactive, timely scam detection.
Key parameters used by digital algorithms to detect frauds
1.Digital model to leverage referral history to Special Investigation Units (SIU):
- Advanced digital technology experts build algorithmic models for calculating the probability of a claim being above a threshold level that can be referred to SIU. This algorithm uses the historical data of claims referred to SIU and calculates the probability value
- For instance, in investigation scoring automation techniques, and Artificial Neutral Networks (ANN), investigation scores more than a certain threshold level can be detected. Based on the investigation scores, the claims can be categorized and judged as good risk or bad risk claims.
2.Digital algorithms, analytics, and data mining for detecting historically rejected claims records:
- This is based on the principle that claims that have historically been rejected will have a great probability of being denied for doubtful potential frauds. Experts put in place digital algorithms that automatically scans through the Claims parameter patterns. This includes a conciliation pattern for the Claim, Claim Risk Indicators such as an individual’s SSN, phone numbers, Address etc
- Such algorithms use advanced Clustering-based Data Mining techniques. These algorithms categorize ‘clusters with high claim frequency’ based on sensitive fields such as SSN, phone number, Address etc. Such claims having ‘clusters with high claim frequency’ can be filtered and classified into ‘bins’ of various degrees. These ‘bins’ indicate the level of risk and notice that the claims within them require
3.Fraud pattern identification algorithm for specific Network group/individuals:
- This is again based on the principle of detecting individuals or a group, that repeatedly perform fraudulent claims
- Digital algorithms are built to detect fraudulent entities indication and a flag is triggered around the same
- Based on these flags, ‘fraudulent patterns’ can be typified. These ‘fraudulent patterns’ can be identified using automated algorithms to detect individual or group records that have an iterative ‘fraudulent pattern’
4.Advanced Analytics models detecting possible frauds based on individual Social media profile:
- Latest algorithmic models built to detect proactively, potential insurance frauds are based on the social media profile and interaction patterns of individuals
- These algorithms detect the lifestyle, attitude, and other personal profiles of individuals registered in social media platforms
- These algorithms detect possible mismatches in the actual profile of the person on social media as compared to his/her claims (such as accident claim)
- For instance, if a person has been flaunting his/her lifestyle recently on social media, is likely to have a mismatch if he/she has forwarded an accident claim
5.Analytics algorithms for fraud detection using Text mining techniques:
- Based on textual posts by individuals on social media, algorithms are built to detect the Network group (refer point 3 above) of possible fraudsters, based on similar text communication patterns of other individuals
- These algorithms use innovative techniques such as Logistic Regression to extract precise information and clearly demarcate the network/group of fraudsters
The advanced analytics, data mining and statistical techniques discussed in this blog will substantially pave way in early, proactive fraud detection. Such digital tools facilitate Insurance companies to improve expenses on claim adjustments, claims leakage reduction etc. Insurance companies can augment their productivity and efficiency with better loss ratios as well as reduced stakeholder risks.
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