Jack Henry & Associates Inc.

11/18/2024 | News release | Distributed by Public on 11/18/2024 09:13

7 Fundamentals for Building Your AI Risk Management Framework

AI is changing the financial world - from reducing costs to improving accountholder experiences.

But for every advantage, there seems to be a potential risk … and navigating this isn't just a checkbox exercise. It's about protecting what matters most: your financial institution's integrity, reputation, and trust.

So, are AI risks a reason to avoid it? Absolutely not.

In fact, avoiding AI altogether could put your organization at a disadvantage in today's fast-paced industry. Instead, the path forward is to embrace this technology with a proactive AI risk management framework that minimizes downsides while amplifying benefits. While there are many frameworks from organizations - the International Organization of Standardization (ISO), National Institute of Science and Technology (NIST), Center for Internet Security (CIS) - your financial institution should create a customized framework that fits within your strategy.

Let's walk through the risks banks and credit unions need to understand (and mitigate) and seven fundamentals every financial institution should incorporate into a resilient AI strategy.

Key AI Risks for Financial Institutions and How to Manage Them

Here's a closer look at the key AI risks your organization needs to be ready for:

  • Cybersecurity Threats

    AI systems are prime targets for cyberattacks, which can lead to data breaches, ransomware incidents, and unauthorized access to sensitive information. This isn't just a technical issue; it's about protecting the trust your accountholders place in you. Proactive cybersecurity measures, such as identity and access management, continuous monitoring, and encryption, are essential to keeping your systems resilient. An effective AI risk management framework can help mitigate these threats by ensuring robust security protocols are in place.

  • Addressing Bias and Compliance

    AI systems can reinforce biases in their training data, leading to unintended discrimination, especially critical in financial services where fair treatment is paramount. Regularly auditing AI models helps detect and correct biases early, safeguarding your institution from reputational and legal risks. Additionally, staying compliant with evolving regulations is vital to avoid fines and sanctions. Collaborate with your compliance team to ensure your AI systems meet all requirements, forming a key part of your AI risk management framework.

A proactive, structured approach to AI risk is your best defense against these challenges. By creating a robust AI risk management framework, your organization can confidently leverage AI's benefits while getting ahead of potential downsides.

Here are the seven fundamentals every AI risk management framework should include:

1. Governance and Oversight

Establishing a governance structure is the backbone of responsible AI use. Form an AI risk management committee with representatives from IT, compliance, legal, and business units to create a balanced oversight. This committee will set policies, track AI initiatives, and ensure that AI applications align with your overall organizational and risk strategy. Defining roles clearly also boosts accountability, helping your institution comply with legal standards like GDPR or CCPA.

2. Risk Identification and Assessment

To protect sensitive accountholder information, start with a comprehensive risk assessment. Evaluate all AI applications for potential operational, compliance, reputational, and cybersecurity risks. By regularly assessing these risks, you can better understand their scope and prioritize actions to mitigate them.

3. Risk Mitigation Strategies

Once risks are identified, implement strategies to manage them effectively. Data quality is key here - establish rigorous data governance practices to ensure that your AI models work with accurate, secure data. Consider validating models before deployment and monitoring them continuously to prevent issues like bias. An AI-specific incident response plan can also help you address problems swiftly if they arise.

4. Regulatory Compliance

Regulatory compliance isn't optional - it's essential for protecting your financial institution and maintaining accountholder trust. Work closely with your compliance leaders to stay updated on evolving laws and conduct regular audits. By doing so, you not only avoid potential fines but also reassure stakeholders that your AI practices are sound.

5. Ethical Considerations

Transparency and fairness should be embedded in all your AI applications. Introduce human oversight to review AI decisions, ensuring they align with your institution's values. When AI is used responsibly, it can strengthen trust with accountholders and demonstrate your commitment to ethical practices.

6. Training and Awareness

Equip your team with the knowledge to use AI responsibly. Regular training sessions on AI benefits, best practices, and risk management help employees make informed decisions, reducing potential risks and promoting a culture of accountability.

7. Continuous Improvement

AI risk management isn't static - it requires ongoing refinement. Establish feedback channels to learn from each experience and update your framework to reflect new challenges or technological advancements. This adaptive approach keeps your institution resilient and aligned with industry best practices.

Want to Broaden Your AI Knowledge?

AI is here to stay, and managing its risks effectively will ensure it strengthens - rather than compromises - your financial institution. For more insights on integrating AI responsibly, check out: