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Monetizing Financial Data with AI: Risks and Opportunities

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4 min read
Jun 18, 2024

Your financial institution is sitting on a goldmine of data, and you’re wondering how to monetize it using artificial intelligence (AI). You’re not the only one.  

With a wealth of data at their disposal, financial institutions are exploring ways to leverage AI to unlock new revenue streams. But it comes with a major caveat: the need to manage risk. 

When it comes to revenue, if your institution doesn’t have a strong grasp on risk and compliance, there is a real chance of getting it wrong – which we all know can lead to consumer harm, reputational damage, and regulatory trouble.  

How do you make the most of the opportunities AI presents while mitigating the potential dangers? Read on to find out.

Related: AI Is Costing Financial Institutions Millions

AI Opportunity: Next-Level CRM

In the past, financial institutions relied on customer relationship management (CRM) software that used statistical models and regression analysis to make predictions based on historical data. These methods required extensive human intervention and predefined rules.  

Today’s AI-powered tools use machine learning to identify patterns and make predictions. These models are built on massive amounts of data (everything from transactions, credit card usage, loan applications, credit reports, and more) and continuously improve without human intervention. It’s not just about numbers. These algorithms can analyze unstructured data such as images and text for much richer insights. 

The potential is huge. Imagine an AI system that analyzes every transaction on a customer’s credit card to predict their financial needs—such as when they might need a mortgage, buy a car, or seek a student loan. This level of predictive analytics can significantly enhance marketing efforts, enabling institutions to offer the right products at the right time with an unprecedented degree of accuracy.

For example, a financial institution that recently accessed a consumer’s Experian data might discover that the consumer doesn’t have a credit card with the institution but does have and regularly uses five airline credit cards. The institution could use this insight to offer a competitive airline credit card to capture their business.

Saving Money with AI: Fraud Detection

AI-powered data analytics does more than find new opportunities for revenue. It can also protect revenue, monitoring transactions in real time and flagging suspicious activities to identify fraud and prevent unauthorized transactions.  

Check fraud has skyrocketed 385% since the pandemic, according to the U.S. Treasury Department. While AI is a playground for fraudsters looking to perpetrate increasingly targeted fraud with deepfakes, it’s also making it faster and easier to identify fraud without manual intervention. For example, the Treasury Department implemented enhanced fraud detection processes using AI in 2023, recovering over $375 million with the tool.  

The challenge with these tools is that any automated approach must be balanced with measures to ensure genuine transactions are not incorrectly rejected. If there’s a mistake that wrongly prevents consumers from accessing their funds, not only might the consumer be angry enough to complain to the CFPB, but they might also close accounts, ending their relationship with the institution forever.

Balancing AI Opportunity and Risk 

Monetizing data with AI is a huge opportunity for financial institutions that go about it smartly. And that means evaluating the AI landscape through the lens of risk management and developing a policy to guide your institution.  

It’s all about balancing AI technologies with robust governance and risk management practices. To make the most of AI’s data analysis capabilities, financial institutions need to understand the risks and have controls to manage them.  

When evaluating AI, consider these factors:

Governance. A robust governance framework is essential for managing AI-related risks. This includes defining clear roles and responsibilities for overseeing AI initiatives, ensuring algorithmic transparency, and monitoring AI performance. Oversight is essential. In a rapidly advancing field, someone must keep their eye on the ball and sound the alarm if something isn’t going well. 

Risk management. Effective AI implementation requires a solid risk management framework. Banks should conduct thorough risk assessments for all AI applications to identify potential biases, data security risks, and compliance issues, among others. 

Policies and procedures. Establish policies and procedures to manage AI risk proactively. This includes regular reviews and updates to keep pace with technological advancements and regulatory changes.

Data privacy. Financial institutions must ensure consumer data is protected and used ethically. This includes complying with data protection laws and being transparent with consumers about how their data is used.  

It’s not just about keeping data safe from prying eyes and complying with regulations – though that is extremely important. It’s also making offers in a way that doesn’t feel intrusive to consumers. It’s one thing to know in the abstract that your data is being used. It’s another thing to feel like your financial institution is nosing around in your confidential business.  

Vendor management. Are your vendors using AI? Financial institutions need to know and then do the proper due diligence to ensure the vendor’s AI uses aligns with the institution’s AI risk management and governance policies and procedures. Data security is an especially relevant concern.  

Related: AI and Risk Management Controls: How to Protect Your Institution

Conclusion 

AI is not a distant future technology. It is here and now. Successfully monetizing data using AI requires a balanced approach that combines innovative AI applications with strong governance and risk management practices and a policy that outlines the institution’s approach.  

It can’t be said enough: Proactive risk management is not a nice-to-have when it comes to monetizing AI. Financial institutions need to continuously monitor and mitigate AI-related risks with robust risk management practices. They need to equip staff with the knowledge and skills to understand any AI applications they are using. 

AI is rapidly advancing, and there will be stumbling blocks along the way. Make sure your institution has guardrails to protect itself from AI missteps, especially when monetizing data.

Want to learn more about AI and risk management? Watch our webinar: "Managing AI Risk: A Primer for Financial Institutions"

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