Artificial Intelligence in Banking Operations: Mitigating or Creating Risks
Artificial Intelligence in Banking Operations: Mitigating or Creating Risks

Author(s): Nikol Nikolova
Subject(s): Economy, Business Economy / Management, Financial Markets, ICT Information and Communications Technologies
Published by: Университет за национално и световно стопанство (УНСС)
Keywords: Artificial Intelligence; Banking Operations; Risk management; Algorithmic bias; Credit scoring; Fraud detection; AML Monitoring; European Banking Authority; Financial regulations; Operational risk
Summary/Abstract: This paper provides an in-depth look into the impact of artificial intelligence (AI) I banking operations, as well as the evolving risks withing the financial sector. As banks increasingly integrate their artificial intelligence into their processes, like fraud detection, credit scoring, customer due diligence and AML monitoring (anti-money laundering), this type of technology offers substantial advantages including improved accuracy, faster processing, reduced operational costs as well as enhanced regulatory compliance capabilities. At the same time, the adoption of artificial intelligence introduces some complex and often some unprecedented risks. These include things like algorithmic bias, model opacity, data-quality deficiencies, vulnerabilities to cyber-attacks, concentration risk also arises due to resilience on third-party technology providers as well as challenges in ensuring auditability and explainability of machine-learning models. The paper below further examines regulatory perspectives from the Basel Committee, the European Banking Authority (EBA) as well as the EU AI Act, outlining hoe supervisors expect banking institutions to implement effective governance, independent validation and continuous monitoring in order to safeguard financial stability. By delving into and evaluating both the opportunities and risks, the paper gives an insight on whether AI can significantly strengthen the resilience of the banking sector, but only when deployed withing a robust framework, which is able to prioritize transparency, ethical use and strong risk-management controls.

• Details
• Contents