Insurance Fraud

What Is Insurance Fraud?

Both buyer and seller can commit insurance fraud.

The seller, for example, may offer policies from non-existent companies, fail to submit premiums, or churn policies to create more commissions. The buyer, on the other hand, has more options: they may exaggerate claims, falsify medical history, post-date policies, sell their policy to others for cash when they are diagnosed with a terminal disease, or fake their death or kidnapping.

Why Is It Important to Have a Good Insurance Fraud Model?

Every year, insurance companies lose billions through fraudulent claims. However, use of a good fraud-detection model will help them detect these false claims before they can cause damage. Thankfully, there are common patterns of behavior that machine leaning programs can recognize at a speed beyond human capabilities.

What Internal Data Should I Have for a Good Insurance Fraud Model?

Insurers should have a database of both past and current claims and policies. They should also have client data on record, to lower the risk that someone would make a claim in a client’s stead.

What External Data is Essential for a Good Model?

Insurance companies have relied on the internal data noted above to identify and fight claims for decades. However, this is often not enough to recognize suspicious activities. Therefore, capitalizing on existing external data has become crucial. The first external data source are industry consortiums that aggregate and share historical claims to validate new claims. Other sources include fraud watch lists, public records, medical billing data, underwriting information, and auto estimates.

What External Data May Prove Useful for a Good Model?

Additional sources of fraud data come from law enforcement; insurers often collaborate with law enforcement and identify both both individual and organized criminals.

What Are the Main Challenges of this Use Case?

There are several challenges of creating a good AI fraud model. First are laws that limit the amount and kinds of information that insurers can share which naturally hinders industry attempts to identify fraudsters.

Secondly, whether or not fraudulent claims succeed in receiving payouts, loyal customers still end up paying higher premiums and suffering additional reviews before receiving payments for legitimate claims.

Interesting Case Studies and Blogs to Look Into

Schemeserve: 7 Incredible Cases of Insurance Fraud
Insurance Nexus: The Role of Data and Analytics in Insurance Fraud Detection

Tangible Examples of Impact

Insurance fraud models have already succeeded in preventing and uncovering fraud on a massive scale.

…Aksigorta Insurance has used [a predictive] model and has “managed to increase its fraud detection rate by 66% and prevent fraud in real time.”

[Meanwhile,] In the UK, a large P&C insurer made £7 million savings per annum by uncovering groups of collaborating fraudsters using network analytics.

Combating Insurance Fraud With Machine Learning