Obviously, as a consequence, the number of fraudulent applications and transactions is also rapidly growing.
With Digital Transformation new payment channels like prepaid cards, e-payments & now mobile-payments, fresh opportunities for frauds are emerging.
Some of the industry research shows that:
- Credit card frauds losses are over 8 billion USD per year
- Insurance policyholders have to pay a higher premium up to 5%
- Total fraud Losses are estimated at over 30 billion USD per year
- Credit/Debit/Charge card fraud
- Check fraud
- Internet transaction/wire transfer fraud -
- Insurance or healthcare or warranty claim fraud – overpayments, false claims
- Subscription fraud – use of telecom services with false credentials
- Money laundering
- Identity theft or account takeover
- Combine historical fraud data with industry knowledge & external market data
- Create a proof of concept to test the history data to determine fraud cases
- If historical data is not available then anomaly detection or outlier detection is used
- Apply the statistical model for fraud detection
- Models are based on past spending patterns, demographic information
- Further text mining & link analysis for probable associations to find deeper frauds
Benefits:
- Increased number of identification of fraud cases
- Dollar savings from fraud prevention adds to the bottom line
- Protect the customer base from financial loss or identity theft
- Improvement in service helps to differentiate in the highly competitive market
How companies are using it:
- Financial institutions using it to identify frauds in leasing contracts
- Banks are using it to detect credit card, wire transfers, check frauds
- Insurers are using it to detect fraudulent claims to save the losses
- Healthcare provider can optimize the medical loss ratio by detecting claims frauds