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Fraud Detection: Integrating AI Models in a Cohesive Strategy

Payment fraud is an ideal use case for machine learning and artificial intelligence (AI) and has a long track record of successful use. Recently, however, there has been so much hype around the use of AI and machine learning in fraud detection that it has been difficult for many to distinguish myth from reality. If done properly, machine learning can clearly distinguish legitimate and fraudulent behaviours while adapting over time to new, previously unseen fraud tactics.

Because organized crime schemes are so sophisticated and quick to adapt, defence strategies based on any single, one-size-fits-all analytic technique will produce sub-par results. Each use case should be supported by expertly crafted anomaly detection techniques that are optimal for the problem at hand. As a result, both supervised and unsupervised models play important roles in fraud detection and must be woven into comprehensive, next-generation fraud strategies.

A supervised model, the most common form of machine learning across all disciplines, is a model that is trained on a rich set of properly “tagged” transactions. Each transaction is tagged as either fraud or non-fraud. The models are trained by ingesting massive amounts of tagged transaction details in order to learn patterns that best reflect legitimate behaviours. When developing a supervised model, the amount of clean, relevant training data is directly correlated with model accuracy.

Unsupervised models are designed to spot anomalous behaviour in cases where tagged transaction data is relatively thin or non-existent. In these cases, a form of self-learning must be employed to surface patterns in the data that are invisible to other forms of analytics.

Unsupervised models are designed to discover outliers that represent previously unseen forms of fraud. These AI-based techniques detect behaviour anomalies by identifying transactions that do not conform to the majority. For accuracy, these discrepancies are evaluated at the individual level as well as through sophisticated peer group comparison.

By choosing an optimal blend of supervised and unsupervised AI techniques you can detect previously unseen forms of suspicious behaviour while quickly recognizing the more subtle patterns of fraud that have been previously observed across billions of accounts

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