Financial Fraud Detection & Prevention Analytics Strategies

With 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 over 8 billion USD per year
• Insurance policy holders have to pay higher premium up to 5%
• Total fraud Losses are estimated over 30 billion USD per year

Frauds cane be classified into various categories as below:

• Credit/Debit/Charge card fraud
• Check fraud
• Internet transaction / wire transfer fraud –
• Insurance or healthcare or warranty claim fraud – over payments, false claims
• Subscription fraud – use of telecom services with false credentials
• Money laundering
• Identity theft or account takeover

Analytics approaches to detect & prevent Frauds:

• 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

• Increased number of identification of fraud cases
• Dollar savings from fraud prevention adds to bottom line
• Protect the customer base from financial loss or identity theft
• Improvement in service helps to differentiate in highly competitive market

• 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