OUR UNIQUE VALUE PROPOSITION: INSURERS CAN EVALUATE CLAIM OVERCHARGE AND ELIMINATE NON-CONFORMING SUPPLIERS THROUGH AN ONLINE PLATFORM (PLUG AND PLAY).
In our pilot, we found that:
Traditionally, insurance companies use statistical models to identify fraudulent claims. These models have their own disadvantages.
First, they use sampling methods to analyse data, which leads to one or more frauds going undetected. There is a penalty for not analysing all the data.
Second, these methods rely on the previously existing fraud cases, so every time a new fraud occurs, insurance companies must bear the consequences of the first one.
Finally, the traditional method works in silos and is incapable of handling the ever-growing sources of data from different channels and different functions in an integrated way.
A statistical law – Benford’s Law (BL) – states that in many naturally occurring collections of numbers, the leading significant digit is likely to be small:
We apply this rule to the insurance claims dataset to find strange digit patterns and narrow the list of possible anomalous items, making the entire audit process more manageable. We also pursue other digit patterns besides the ones in BL.
Data XL’s platform contains the following modules:
To run a pilot, Data XL needs the following information:
For maximum pilot success, we recommend that you:
Other objectives are equally applicable, including analysis of:
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