Explainable AI for Financial Risk Mitigation: Governance, Compliance, and Customer Protection in the U.S. Economy
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I1P138Keywords:
Explainable Artificial Intelligence (XAI), Financial Risk Management, Credit Risk Assessment, Fraud Detection, Machine Learning (ML), Deep Learning (DL), U.S. Financial System, AI GovernanceAbstract
The growing complexity of the U.S. financial system alongside quick digital transformation has exacerbated the demand to have smart and transparent risk management systems. This paper discusses the application of Explainable Artificial Intelligence (XAI) to improve financial risk management, specifically credit risk, detecting fraud, and compliance with regulations. Although the state-of-the-art machine learning (ML) and deep learning (DL) models, including Random Forest, LSTM, Graph Neural Networks and predictive analytics, considerably enhance the predictive accuracy and real-time risk monitoring, their black-box character causes some concerns about the transparency, accountability and compliance with regulations. To overcome this issue, the paper differentiates between interpretability and explainability and combines post-hoc explanation methods, such as LIME to understand model local interpretability and SHAP to understand the model global feature attribution to augment the model transparency. The study also examines AI-based risk identification, analysis, evaluation, and continuous monitoring frameworks in banking institutions. It also assesses the U.S. financial system governance and regulatory structure, as well as their adherence to the Dodd-Frank, Basel III regulations, AML, KYC, and CDD regulations. The results demonstrate that integrating sophisticated AI models with a well-developed explainability will increase institutional resilience, enhance the quality of decision-making, increase regulatory compliance, and foster systemic stability. The research is relevant in conceptualizing transparent AI systems, accountable AI systems, and governance-oriented AI systems to manage financial risk sustainably in the U.S. economy.
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