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.
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.
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.
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.
Additional sources of fraud data come from law enforcement; insurers often collaborate with law enforcement and identify both both individual and organized criminals.
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.
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.
IBM MarketScan Research Databases provides one of the oldest continually-updated collection of health claims data in the USA. Organizations use this data to prove their value to healthcare professionals, insurers, and private individuals.
The data includes drug claims, dental claims, lab results, hospital discharges, and EMR data for millions of people in the country. It also contains workplace productivity data, telling institutions how many workplaces absences they suffer and how many of their healthcare workers suffer disability due to their work.
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