How Banking Leaders Can Enhance Risk and Compliance With AI

Artificial intelligence (AI) is a technology likely to become adopted across your enterprise to some degree, whether you know it or not, and for risk and compliance leaders at banks and credit unions, now is the time for you to take the reins. We must become knowledgeable of this technology’s opportunities, its limitations, and its risks. Beginning now, we all should be preparing to guide our organizations towards success, and steer them clear of troubled waters.

On one hand, AI can reduce risk exposure while making regulatory compliance more efficient. AI can also enhance fraud and cybersecurity detection. On the other hand, the complexity of AI models, coupled with concerns around data privacy and algorithmic transparency, requires careful oversight to avoid regulatory pitfalls and maintain customer or member trust.

How the industry moves forward will largely depend on pending regulations and the leaps AI science may take, but for now, here is where the current state of affairs lies.

Artificial Intelligence Can Enhance Risk Management

One of the key benefits of AI for risk management is its ability to process large datasets in real time, identifying patterns and anomalies that could indicate potential risks. According to Alkami’s 2024 AI Market Study, 44% of financial institutions are prioritizing AI investments in areas like fraud detection and security. AI’s capacity to analyze vast amounts of data at high speed makes it a valuable tool for monitoring suspicious transactions, detecting fraud, and ensuring compliance with evolving regulations.

Key Applications for AI in Risk Management:

• Fraud detection: AI models can quickly detect unusual behavior across multiple channels (e.g., mobile, online, ATM) and flag potential fraudulent transactions in real time.
• Transaction monitoring: Machine learning algorithms can continuously scan account activity to identify patterns that may indicate money laundering or other illicit activities.
• Credit risk assessments: AI can provide more accurate credit scoring models by analyzing a broader range of data points, reducing the risk of defaults.

For risk and compliance leaders, AI offers powerful tools to help mitigate risks before they escalate. However, deploying these models requires a deep understanding of how they operate to ensure compliance with existing regulations.

Artificial Intelligence Can Reduce Fraud and Cybersecurity Risks

One of the biggest advantages of AI is its ability to enhance fraud detection and cybersecurity measures, providing a secure banking experience. Nearly half of financial institutions believe that using AI to protect account holders from fraud or threats would have an immediate positive impact on their business.

With the rapid rise of cyberattacks on financial institutions, AI’s ability to monitor large datasets and identify security threats is a powerful extension to the fraud/cybersecurity team.

How to leverage AI to reduce fraud and enhance cybersecurity measures:

Detect anomalies in real-time: AI algorithms can continuously monitor transactions and identify deviations from normal patterns that could indicate fraud.

Predict potential future attacks: Machine learning models can predict potential cyber threats based on historical data, allowing institutions to take proactive measures to mitigate risks.

Strengthen authentication: AI can enhance identity verification processes by analyzing behavioral biometrics, such as typing patterns or geolocation data, to detect suspicious activity during online banking sessions.

Investing in AI-powered fraud detection and cybersecurity tools can not only help reduce the institution’s exposure to fraud but also provide a more secure banking environment for account holders. However, when onboarding and training artificial intelligence tools at your financial institution, there are conditions to be mindful of.

Artificial Intelligence Brings Its Own Risk To The Table

While AI holds immense potential, its adoption hinges on maintaining account holder confidence. One of the most common concerns expressed by both financial institutions and their account holders is around transparency in AI decision-making. While 73% of financial institutions are convinced that AI can significantly enhance digital account holder experiences, apprehensions about AI’s impact on account holder trust are significant, with 54% expressing concerns over potential negative effects. The concern seems valid, as less than half of consumers feel comfortable with their financial data being processed by AI, even if it gives them a better digital banking experience.

This is a crucial consideration for risk and compliance leaders who must ensure that AI technologies are deployed in ways that meet strict regulatory standards and underscores the need for clear communication and trust-building measures.

To address issues around trust and transparency, financial institutions need to focus on:

Model transparency: Regulatory bodies may require financial institutions to explain how AI models make decisions, especially in areas like credit scoring and loan approvals. Ensuring that AI systems are interpretable and explainable is essential for maintaining compliance.

Data privacy and security: AI systems must comply with data privacy regulations such as General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). Financial institutions must ensure that personal data used by AI is protected and processed lawfully, particularly in regions with strict data protection laws.

Implement a strong AI Governance process to include:

• A risk assessment process based on industry leading risk management framework
• Oversight by the existing Risk Officer, or staff a new role dedicated to AI compliance leadership
• Strong data governance frameworks that meet both regulatory requirements and user expectations around privacy
• A governance committee to manage approvals and usage monitoring

Auditable processes: Implement procedures that allow internal and external audits of AI systems to ensure compliance with regulations and institutional policies

AI is a powerful tool, but it also introduces ethical challenges, particularly around bias and fairness. As AI systems are trained on historical data, they can unintentionally perpetuate existing biases, leading to discriminatory outcomes. For risk and compliance leaders, ensuring the AI is used ethically, and tested regularly, can prevent potential reputational damage and/or legal consequences.

Key concerns around ethics and bias include:

Algorithmic bias and fairness: AI algorithms are susceptible to bias if they are trained on biased data and they can inadvertently make discriminatory decisions, particularly in areas like credit scoring and loan approvals. Ensuring that AI models are fair and do not disadvantage certain groups based on race, gender, or socio-economic status is a key compliance issue. Regulatory scrutiny is increasing around AI models that unintentionally discriminate against protected classes.

Explainable AI (XAI): Implement explainable AI models which allow for transparency into how decisions are made, whether it’s a loan approval or a flagged transaction. This is critical not only for account holder trust but also for regulatory compliance.

Ethical AI frameworks: Implementing ethical AI frameworks that ensure AI systems are developed and deployed with fairness, accountability, and transparency.

Risk and compliance leaders should work closely with AI developers and data scientists to ensure AI models are transparent, explainable, and auditable.

Artificial intelligence in banking’s expansive use cases will continue to challenge risk and compliance leaders to ensure that these technologies are deployed safely, ethically, and in full compliance with regulatory requirements. AI offers enormous potential for enhancing fraud detection, improving operational efficiencies, and reducing risks, but these benefits come with significant responsibilities.