Risk vs. Reward: How to Maximize Your Financial Institution’s Data with AI

Artificial intelligence (AI) has become an increasingly common word in finance as more institutions, including banks, seek to make the most of their data with AI-enabled technology.

AI is a powerful tool, and when used for good, it can enable financial institutions to increase revenue and protect consumers. Earlier this year, the U.S. Department of the Treasury announced that it had recovered over $375 million since the pandemic from implementing an enhanced AI-enabled fraud detection process.

But AI doesn’t come without risks. The same tool that can be used to help consumers can also be cost prohibitive – not to mention the inherent risk of utilizing a technology frequently used by fraudsters and other bad actors. 

Balancing the risk and reward of AI is crucial when overseeing your financial institution’s data strategy. Let’s explore this in more detail. 

Improving customer relationship management through AI

Historically, financial institutions have relied on customer relationship management (CRM) software that uses statistical models and regression analysis—a predictive statistical tool used to discover a relationship between variables—to make predictions based on historical data. While helpful, these methods follow predefined rules and are thus limited.

Enter AI-powered tools, which can adapt and learn from mounds of data (transactions, credit card usage, loan applications, etc.) in real time—but it goes deeper than that. In addition to recognizing patterns and making predictions, the algorithms can analyze unstructured data, such as images and text, for deeper insights.

For example, an AI system that analyzes transactions on a customer's credit card in real-time could be used to predict their financial needs, such as a mortgage, car loan, or student loan. This kind of real-time predictive analytics could significantly enhance a financial institution's marketing efforts, enabling it to offer the right products at the right time.

Improving AI-enhanced fraud protection 

As mentioned earlier, AI-powered data analytics aren't limited to revenue-earning opportunities. They can also protect revenue by flagging suspicious activities, such as fraud and unauthorized transactions. 

However, one of the main challenges with AI automation—and any automated approach to identifying risk —is that it must be balanced with measures to ensure genuine transactions are not incorrectly rejected. If an institution's mistake prevents consumers from accessing their funds, the consumer could be angry enough to complain to the Consumer Financial Protection Bureau (CFPB), close their account, or both. Moreover, institutions that don't properly utilize AI or fail to address risks —such as data privacy and security, potential biases and ethical concerns, regulatory compliance, and the impact on employees and customers —can face regulatory penalties and fines.

Institutions can help avoid the pitfalls of AI technology by utilizing risk and compliance management software like Ncontracts. 

Tips for managing AI-related risk 

Let’s say you realize the power of your institution’s data and want to start utilizing AI tools to make the most of it while monitoring risk effectively. What should you do next? 

• Establish and maintain a governance framework. A robust governance framework involves defining roles and responsibilities for overseeing AI initiatives, ensuring transparency in algorithms, and monitoring AI performance. Oversight is vital in a rapidly advancing field, so consider who on your team will monitor things and raise concerns as needed. 

• Organize a risk management strategy. Effective AI implementation requires a solid risk management framework. Banks should conduct risk assessments for all AI applications to identify potential biases, data security risks, and compliance issues, as well as establish proactive policies and procedures to manage risk. Enterprise risk management in banking is becoming increasingly sought-after as banks look to unify their risk data and gain real-time insights into their most pressing risk concerns.  

• Prioritize consumer data protection. Ensuring consumer data is protected and used ethically is a crucial responsibility of financial institutions. This includes complying with data protection laws and being transparent with consumers about their data use. Transparency in data use builds trust and ensures that consumers feel respected and valued by their financial institution.

• Vendor management. Are your vendors, also called third-party partners, using AI? Financial institutions must identify this and conduct due diligence to ensure that the vendor's AI usage aligns with the institution's AI risk management and governance policies and procedures. Data security is a particularly relevant concern.

AI presents many benefits and challenges for banks and other financial institutions. The data usage opportunities are vast, from improving customer relationships to fraud protection and revenue-generating opportunities, but they don't come without risk. Financial institutions that properly manage risk with a solid foundation and continually monitor for new and ongoing risks can sustainably reap the benefits.