By PaymentsJournal
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Banks aren’t struggling to find artificial intelligence use cases—they’re struggling to govern them.
From fraud investigations to dispute resolution, AI is becoming part of the day-to-day work of financial institutions. At the same time, new governance laws in states like Colorado and Texas are introducing new expectations around transparency, documentation, and oversight.
In a recent PaymentsJournal webinar, David Chmielewski, Chief Product Officer and Co-Founder at Quavo and Hailey Windham, Community Lead for Banking at Sardine.ai, along with Jennifer Pitt, Senior Fraud Analyst at Javelin Strategy & Research, explored where these new requirements intersect with existing workflows, and what organizations can do now to build AI programs that are both effective and compliant.
The key is transparency—not just to satisfy regulators, but to maintain customer trust.
Implementing Continuous Governance
One of the main ways AI is currently being leveraged is to help institutions manage the high volume of cases, alerts, disputes, and documentation they are often inundated. These use cases play to AI’s strengths, such as its ability to summarize cases and detect anomalies across vast amounts of data.
In short, AI is helping organizations cut through the noise so they can spend more time on critical tasks. But it isn’t fully autonomous yet.
“We’re using AI today for some of the things that we thought were never possible to automate,” Chmielewski said. “Things like looking at representment documents with lots of evidence and summarizing them; things like customer communication and making sure we can quickly sort through what we’re receiving.”
“We’re also using it for the investigation process, but not without a human in the loop,” he said. “The dream where we’re just handing everything off to AI and it’s doing everything for us is not quite yet realized, but we’ll see if we can get there eventually.”
The challenge is no longer simply implementing AI—it’s governing it. As regulators begin formalizing expectations around AI oversight, organizations must ensure their processes can support transparency and accountability.
For example, the Texas Responsible Artificial Intelligence Governance Act (TRAIGA), which took effect this year, prohibits harmful and intentionally unlawful uses of AI while encouraging organizations to adopt established risk management practices. It also creates a regulatory sandbox where companies can test AI systems under state oversight.
Colorado has taken a similar approach. After enacting the nation’s first broad AI law, lawmakers replaced it with the Automated Decision-Making Technology Act, a more practical framework scheduled to take effect next year.
“I compare this to the recent Nacha explainability requirement,” Windham said. “Those rules weren’t saying don’t use machine learning, they were saying if you’re making a decision that affects customers or your institution’s risk, you need to be able to explain it. We already understand that concept in payments and fraud.”