In 1992, FICO introduced Falcon, a neural network-based fraud detection system that detects fraudulent payment card transactions in real time, which led to a dramatic reduction in payment card fraud across the globe. To assist analysts taking action on the scores generated by the model, FICO introduced the Reason Reporter, which provides reasons associated with the neural network scores—one of the first commercially available instances of explainable AI (XAI).
Scott Zoldi explores the algorithm and explains how it provides the explanations. Scott also offers a comparative study with local interpretable model-agnostic explanations (LIME)—a perturbation-based approach—to demonstrate the quality of the explanations as evaluated by human domain expert and outlines the theoretical underpinnings of why perturbation-based approaches fail to work well in many scenarios. Scott then digs deeper to show why looking for global score support in the historical data is a superior method for providing explanations. Understanding causality while providing model explanations have been elusive so far, as it requires ability to understand and parse latent features that actually drive the score. Scott concludes by briefly exploring LENNS (latent explanations neural network scoring), a patent-pending architecture that exposes more of what’s driving the score.
Scott Zoldi is chief analytics officer at FICO, where he is responsible for the analytic development of FICO’s product and technology solutions, including the FICO Falcon Fraud Manager, which protects about two-thirds of the world’s payment card transactions from fraud. While at FICO, Scott has authored 80 analytic patents (40 granted and 40 in process). He is actively involved in the development of new analytic products utilizing artificial intelligence and machine learning technologies, many of which leverage new streaming artificial intelligence innovations such as adaptive analytics, collaborative profiling, deep learning, and self-learning models, and has recently been focused on the applications of streaming self-learning analytics for real-time detection of cybersecurity attacks and money laundering. Scott serves on the boards of directors of Tech San Diego and the Cyber Center of Excellence. He holds a PhD in theoretical physics from Duke University.
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