The customer is a public multinational transaction processor for retail B2B and B2C industry, with subsidiaries and partners in over 25 countries. Facing an increase in fraudulent activities and dispute calls, the customer turned to Lore IO to investigate root cause and perfect its internal machine learning model.
Lore IO blended order, transaction, demographic, merchant location, and purchase catalog data and detected patterns of fraudulent credit cards in just five days. It developed a profile with unique attributes of reported fraud activities.
The customer realized cost savings in the tens of millions of dollars by singling out all gift cards that match fraud patterns and their potential dollar impact. The customer also identified historical transactions that matched its optimized machine learning model. The customer aggregated card behavior over time and verified a list of hypotheses that indicated fraud. Machine learning teams used virtualization to build complex set of features. And business users built out complex journey patterns for repeated use.