Organizations are exposed to varying types and degrees of frauds perpetrated by customers, employees, vendors, third parties and others. Every year, companies lose billions of dollars to financial crimes and fraud. The expansion of online channels has led to increased instances of fraud, especially for e-commerce entities, airlines and banks, among others. The typical approach to combat fraud is to strengthen process controls through either system-based or manual controls. Another approach used extensively in proactive fraud detection is leveraging analytical models with predictive capabilities to detect vulnerable transactions at the outset.

WNS offers comprehensive fraud management services across the value chain to support leading global organizations. These services cut across analytics, technology and people support. Our predictive fraud model engine buckets transactions based on fraud propensity scores. The model analyzes current transactions and pattern-matches its characteristics with the previous instances of fraud through multiple predictive modeling techniques. The suspect cases once detected, are examined by a team of experienced fraud investigators.



  • Fraud identification

    • Building and validating predictive models for fraud identification
    • Building analytical platforms
    • Exploring machine learning and natural language processing techniques to predict fraud


  • Fraud detection

    • Assessing fraud triggers to identify potential frauds and eliminate false positives
    • Conducting customer / third-party calls to assess potential frauds
    • Gathering supporting information from public data sources


  • Fraud investigation

    • Investigating fraudulent transactions against information available and conducting root cause analysis
    • Documenting the results to facilitate suitable corrective actions
    • Supporting regulatory reporting

WNS set up a 24x7 team for monitoring 350,000 transactions annually to identify vulnerable cases
WNS deployed a team of fraud investigators to conduct third-party checks for authenticating transactions
WNS helped the client build a fraud detection model based on machine learning algorithms

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