AI in Banking Brings Efficiency—and Three Growing Security Risks

Banks are rapidly adopting AI across key operations like fraud detection, underwriting, and customer support. The technology is now embedded in financial workflows, but this integration brings new risks. Regulators and institutions must address growing concerns over security and reliability. As AI takes a central role in finance, three major risks have emerged. Integrity risk involves manipulated inputs or prompt injection attacks that produce flawed yet plausible results. Dependency risk stems from banks relying on interconnected systems, including cloud providers and external data sources, which broaden the attack surface.

Trust risk reflects consumer unease about data security. Surveys show 56% have moderate or low confidence in mobile app security, while 35% hold companies responsible for breaches. The Bank for International Settlements has warned that deeper AI integration could heighten financial vulnerabilities, even if attackers target APIs or data rather than the models themselves.

Banks and regulators must move beyond traditional vulnerability checks. Continuous evaluation of AI-connected systems is now essential, with a focus on exploitability rather than sheer volume of weaknesses. The next phase involves agentic AI, capable of making autonomous decisions with minimal human oversight. AI in banking offers efficiency and personalisation but introduces new challenges. Institutions must strengthen defences against integrity, dependency, and trust risks. Without proactive measures, the financial system could face amplified vulnerabilities as AI adoption grows.