Navigating the Risks of Third-Party AI Solutions in Banking: A Comprehensive Guide

Artificial intelligence (AI) is everywhere, and it’s changing the way we do business. From summarizing social media comments to responding to Google searches, AI is becoming an integral part of our daily lives. But what about third-party solutions? With the rapid adoption of generative AI, it’s more important than ever for risk and compliance professionals to understand how third parties are implementing AI in their solutions to ensure compliance programs remain effective.

Banks and financial institutions are investing heavily in AI capabilities, particularly in areas such as KYC, due diligence, fraud detection, customer service, privacy, and cybersecurity. These advancements in AI are revolutionizing the way organizations operate, but they also come with risks.

One of the key risks associated with AI is bias. AI models can inadvertently perpetuate biases in training data, leading to unequal or discriminatory outcomes. To mitigate this risk, organizations must audit their data, ensure diversity in training datasets, and monitor outputs regularly to ensure compliance with regulatory requirements.

Another risk is the lack of transparency in AI decision-making processes. Many advanced AI models operate in complex and opaque ways, making it difficult to understand how decisions are made. This lack of explainability can undermine trust among stakeholders and complicate compliance with regulations. To address this risk, organizations should prioritize interpretable models and use explainability tools to ensure transparency in decision-making processes.

Over-reliance on AI is another risk that organizations must consider. While AI tools excel at automating processes and identifying patterns, they are not infallible and may fail to account for context or nuances that require human judgment. Without human oversight, errors can go unchecked, leading to compliance violations or customer dissatisfaction. Organizations must strike a balance between AI efficiency and human expertise to mitigate this risk.

Performance risks are also a concern when implementing AI solutions. Issues such as false positives and negatives can disrupt legitimate transactions and expose organizations to financial and reputational harm. Models require regular retraining and updates to maintain reliability, and continuous monitoring is needed to detect issues such as performance degradation or changes in data patterns.

As the regulatory environment around AI continues to evolve, organizations must stay informed about emerging laws and regulations. Themes such as governance, transparency, and individual rights are emerging as key considerations for organizations using AI. By understanding these themes and aligning with regulatory guidance, organizations can effectively manage the risks associated with AI.

When evaluating a third party’s AI capabilities, organizations must gather information about the third party’s current or potential use of AI, establish a clear definition of AI, and assemble a skilled team to lead the assessment process. Technical considerations, transparency, explainability, and performance indicators are all important factors to consider when evaluating a third party’s AI capabilities.

Contract and monitoring considerations are also crucial when engaging with third parties using AI. Organizations should ensure that third parties are staying abreast of emerging legal and regulatory guidance, have good data governance and security practices, and provide transparency and explainability in their AI solutions. Quality oversight and control mechanisms, documentation on algorithms, and access to ongoing monitoring reports should also be included in contracts with third parties.

Overall, AI is revolutionizing the banking industry, and organizations must adapt to stay competitive. By understanding the risks associated with AI, staying informed about emerging laws and regulations, and effectively managing third-party relationships, organizations can harness the power of AI while mitigating potential risks.