Fraud Analytics
The importance of analytics cannot be overstated in today’s high-tech, data-driven world. A Deloitte survey found that 49% of respondents believe analytics is helpful for making better business decisions, 16% say it helps them to achieve key strategic objectives, and 10% believe analytics help them to improve customer relations and business partnerships, regardless of the sector they are in.
The purpose of this article is to discuss fraud in the insurance industry and how analytics can be used to detect and prevent it.
What is Fraud?
As per Wiki, Fraud is intentional deception to secure unfair or unlawful gain, or to deprive a victim of a legal right and when it comes to its implication a significant increase in statistics is observed. As per studies:
- Typical organization loses 5% of its revenue to fraud each year (www.acre.com).
- According to the European insurance committee, fraud takes up to 5–10% of claim amounts paid for non-life insurance.
The risk of fraud will always be present when it comes to business and there will always be some individuals who will look to make gains where there is opportunity, therefore organizations do require robust processes in place to prevent, detect and respond to it.
Frauds in Insurance:
Our day-to-day existence is filled with a variety of fraud, but insurance fraud is by far the most prevalent. It occurs when a claimant attempts to obtain some benefit or advantage they are not entitled to; this could range from staging the incident, misrepresenting the situation like cause of incident by including the relevant actors or by exaggerating the damage caused.
Although detecting insurance frauds is a challenging problem, given the variety of fraud patterns and relatively small ratio of known frauds in typical samples.
In Most of the scenarios, insurance companies utilize the following sub-set of rules to identify suspected claims.
- First and foremost insurance companies analyze claim history and suppose if you had multiple claims in the past for losses then it is an immediate red flag. Moreover, if either of them (claims) was suspected or identified then definitely your new claims are always going to be under scrutiny.
- In auto insurance one of the prevalent scheme by average people is to report their vehicle as missing but insurance companies keeps in depth records of claims and perform an array of analysis to interpret collected data using artificial intelligence technology .They can determine who is most likely to file a claim , when and where accident may occur or the likelihood of a vehicle being overlooked or stolen from a particular location and if your claim doesn’t match the typical pattern you are noticed.
- The insurer uses complex algorithms to create features that represent information like post claims counts within a given timeframe for parties, Vehicle history and user counts , involved parties have N(multiple) occurrences with different insurance companies with different addresses and phone numbers ,Same attorney is used by insured AND Third Party in different claims.
This depicts that insurers have started looking at leveraging capability of analytics and machine learning instead of only adhering to traditional approaches. The intent is to present a variety of data to the algorithm and based on identified frauds to develop a predictive model that can be tested on known frauds through a variety of algorithmic and performance measuring techniques and later serve as an automated tool for predicting frauds in the future.
