ARGO Blog | Banking Technology and Healthcare IT Trends

Detecting Fraud with Analytical Technology

Written by ARGO | Jul 20, 2023 1:51:02 PM

Today’s automated fraud detection solutions use analytical technology to automate fraud detection. These solutions include:

  • Decision Tree Automation – A form of multiple variable analysis used to predict, explain, discover, describe, or classify outcomes. It goes far beyond a simple one-cause, one-effect relationship. Decision Trees can encode complicated relationships between inputs that affect response values, becoming valuable to predictive decisions as a data gathering, exploration, and decision-making tool.
  • Predictive Analytics (Machine learning) – This integrates probability theory, combinatorial optimization, search, and statistics, which are the basis of several analytical predictive and pattern recognition software solutions in both financial services and healthcare.
  • Probabilistic Determination - Provides a statement about the certainty of an event, usually expressed in terms of probability. Often business leaders want to know three things about their operation: what happened, why it happened, and what will likely happen in the future.

How do these analytics methods reduce fraud?

Predictive analytics provide a method and foundation for computing the probability analytical computation processes and the outcome results used by fraud analysts when adjudicating suspect items. The process is executed in four stages:

  1. Stage 1—Applying individual fraud analytic tests
  2. Stage 2—Determining the Suspicion Level (DSL) score
  3. Stage 3—Determining of the Financial Risk Exposure (FRE) score
  4. Stage 4—Routing to a work queue for adjudication

The ARGO Fraud solution, OASIS™ (Optimized Assessment of Suspicious Items), provides cross-channel, multi-fund analytics and adjudication workflow to detect fraudulent transactions and suspicious items. ARGO’s competency in Decision Trees, Random Forests, Bayesian graphical models, logistic regression, and others allow a deep understanding of the complex, non‑parametric, and often non-linear relationships that affect prediction accuracy.

For more information, download the “The Role of Analytics in Fraud Prevention” interview with David Engebos, President and COO of ARGO.