Fraud

Credit Risk Management

Credit Risk Management Consulting covers the entire spectrum, including risk identification through diagnostic review analysis, risk assessment through corporate and retail scoring model development, risk measurement through estimation of Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD) and Credit VaR models, as well as overall risk management, including collateral management, risk-based pricing, and reporting frameworks.

• Governance, Risk, and Compliance
• Model Development and Validation
• Credit VaR Estimation Framework
• Risk Monitoring and Reporting
• Risk-Based Decision Making

Retail Fraud Detection                                

Analysis of transactions and activities such as purchasing, accounts payable, POS, sales projections, warehouse movements, employee shift records, returns, store level video and audio recordings, and other data across your company can help you to identify fraudulent activity and develop appropriate priorities for case management and investigation.

• Ethical Cultural Assessment
• Monitoring & Analyzing Loss Prevention Metrics
• Profiling
• Monitoring Vendor/Supplier Related Issues
• Predictive Modeling
• Retail Shrinkage

Insurance Fraud Detection

Fraudulent claims that are a serious financial burden on insurers cause higher overall insurance costs. Here are a few examples of the way data analysis can be applied to fight fraud in the insurance industry:

• Medical Billing Fraud
  • Identify excessive billing — same diagnosis, same procedure
  • Identify excessive number of procedures, per day or place of service/day
  • Identify multiple billing of same procedure, same date of service
  • Locate age inappropriate treatments — too young/old for treatment
  • Identify duplicate charges on patient bills
  • Find doctor and patient with same address
• Claims Fraud
  • Identify duplicate claims
  • Review submission of multiple/inflated claims
  • Find fraudulent family members: i.e., five dependent children born within a two year period.
  • Highlight incorrect gender specific treatments
  • Flag mutually exclusive procedures: e.g. if appendix removed on 01/10/14, then it would be impossible to have appendicitis on 01/02/15.
  • Highlight failure to disclose pre-existing condition (where applicable)
• Life Insurance Fraud
  • Determine patterns of overpayment of premiums
  • Review transaction payments comprising more than one type of payment instrument
  • Report multiple accounts to collect funds or payment to beneficiaries
  • Report purchase of multiple products in a short period of time
  • Review beneficiaries with multiple policies
  • Isolate transactions for follow-up where employees are beneficiaries
  • Determine agents/brokers with statistically high numbers of claim payouts
  • Calculate benefit payments paid for lapsed policies
  • Find policy loans that are greater than face value
  • Report unauthorized policy changes
  • Identify missing, duplicate, void or out-of-sequence check numbers