Aiding Humans in Financial Fraud Decision Making: Toward an XAI-Visualization Framework
This work addresses the problem of complex decision-making for financial fraud investigators, but it is incremental as it builds on existing visual analytics systems.
The paper tackles the challenge of financial fraud investigators manually synthesizing vast unstructured information by proposing an XAI-visualization framework that supports all stages of investigation, aiming to keep human judgment in control while reducing biases and labor-intensive tasks.
AI prevails in financial fraud detection and decision making. Yet, due to concerns about biased automated decision making or profiling, regulations mandate that final decisions are made by humans. Financial fraud investigators face the challenge of manually synthesizing vast amounts of unstructured information, including AI alerts, transaction histories, social media insights, and governmental laws. Current Visual Analytics (VA) systems primarily support isolated aspects of this process, such as explaining binary AI alerts and visualizing transaction patterns, thus adding yet another layer of information to the overall complexity. In this work, we propose a framework where the VA system supports decision makers throughout all stages of financial fraud investigation, including data collection, information synthesis, and human criteria iteration. We illustrate how VA can claim a central role in AI-aided decision making, ensuring that human judgment remains in control while minimizing potential biases and labor-intensive tasks.