Aiding Humans in Financial Fraud Decision Making: Toward an XAI-Visualization Framework

TL;DR

This paper proposes a comprehensive visual analytics framework to assist humans in all stages of financial fraud investigation, integrating AI alerts, transaction data, and social insights to support decision making while reducing bias and workload.

Contribution

It introduces a novel VA framework that supports end-to-end financial fraud investigation, emphasizing human control and bias mitigation in AI-assisted decision processes.

Findings

Framework supports all investigation stages

Enhances human decision control

Reduces bias and manual effort

Abstract

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.