Fraud Detection & Prevention

Retail Analytics

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Fraud Detection & Prevention

Fraud detection is a critical issue for retailers determined to prevent losses and preserve consumer trust. Fraud can originate from customers or people masquerading as customers, store associates, or external criminals or hackers. The most prominent recent frauds have involved stolen credit card information and fraudulent merchandise returns. Analysis of transactions and activities such as purchasing, accounts payable, POS, sales projections, warehouse movements, employee shift records, returns, and store level video and audio recordings can help you to identify fraudulent activity and develop appropriate priorities for case management and investigation.

  • Ethical Cultural Asessment: Assessements and surveys can derive insights into your ethical culture and help to quantify the internal impact of your fraud and ethical risk of management activities.
  • Monitoring & Analyzing Loss Prevention Metrics: To monitor loss prevention efforts across today?'s complex, omni-channel, and potentially global retail operations, a dashboard of up-to-date information is mandatory. Many companies, however, find themselves data rich and information poor. Analytics can be used with markdown systems, price optimization systems, business intelligence reporting, and modeling deal prices to help control cash losses.  
  • Root Cause Analysis: Identify the root causes of shrink by using risk diagnostics that reach beyond store-level operations and include a holistic view of factors that are highly correlated with and believed to be causing loss. 
  • Profiling
    • ?Types of products can be profiled; stocktake variances, and stocktake adjustments taking place in various subsidiaries can be analyzed.
    • Profile Customers separating Payers from Non-Payers
    • Create profiles of suspicious payments and payers for ongoing fraud detection
  • Monitoring Vendor/Supplier Related Issues: Monitor and analyze trends such as number of invoices from suppliers over time, unusual invoice number sequencing, and the amount of money spent for goods and services purchased from a particular vendor, to alert management when unusual items are being processed.
  • Predictive Modeling: Build a model for a predicted number of product returns per shift; when numbers exceed a set threshold for returns by product or by individual, manager verification can be invoked. 
  • Retail Shrinkage: It is essential to distinguish among shrink caused by theft versus other types of shrink, such as data integrity and operational execution gaps, which involve operational consideration. Each is important in understanding the total picture of loss throughout the organization. Point-of-service data can be analyzed in conjunction with various data streams to identify losses resulting from process-related errors. Watch product and inventory movement for unusual patterns that may indicate shrink or store employee theft before it becomes significant.
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Works at Pricewaterhouse Coopers
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