Summary of Aiding Humans in Financial Fraud Decision Making: Toward An Xai-visualization Framework, by Angelos Chatzimparmpas and Evanthia Dimara
Aiding Humans in Financial Fraud Decision Making: Toward an XAI-Visualization Framework
by Angelos Chatzimparmpas, Evanthia Dimara
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework integrates Visual Analytics (VA) with financial fraud investigation to support decision makers throughout all stages of the process. It tackles the challenge of manually synthesizing vast amounts of unstructured information, including AI alerts, transaction histories, social media insights, and governmental laws. The system explains binary AI alerts and visualizes transaction patterns while ensuring human judgment remains in control, minimizing potential biases and labor-intensive tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI helps detect financial fraud, but regulators require humans to make final decisions due to bias concerns. Investigators face a massive challenge: manually combining unstructured data like AI alerts, transactions, social media insights, and laws. Current Visual Analytics (VA) systems only help with isolated parts of this process. This paper proposes a VA framework that supports decision makers throughout the investigation process, including data collection, information synthesis, and human criteria iteration. The goal is to keep human judgment in control while minimizing biases and labor-intensive tasks. |
