Financial Fraud Detection & Prevention Analytics Strategies

July 16, 2011
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Financial industry is facing the fiercest competition in current time after the economic meltdown. Banks are using all avenues to grow their customer base considering the survival aspect. This has led to tremendous volume growth in banking accounts applications, credit card applications, and financial transactions. Obviously, as a consequence, the number of fraudulent applications and transactions is also rapidly growing.

Financial industry is facing the fiercest competition in current time after the economic meltdown. Banks are using all avenues to grow their customer base considering the survival aspect. This has led to tremendous volume growth in banking accounts applications, credit card applications, and financial transactions. Obviously, as a consequence, the number of fraudulent applications and transactions is also rapidly growing.

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
Benefits:
  • 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
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