Regulated financial institutions require AI technology designed for data protection, auditability, and human involvement. Here’s Abrigo’s AI development approach.
Financial institutions cannot afford mystery in their technology. They need tools that protect data, support auditability, and help teams understand how outputs are produced. As artificial intelligence becomes part of more banking workflows, those expectations should guide how AI is developed, deployed, and reviewed.
I recently spoke with Danny Piangerelli, Abrigo’s Senior Vice President of Technology, on Abrigo’s “Ahead of the curve” podcast for bankers, and we discussed responsible AI in banking and what it takes to build AI tools for regulated institutions.
Piangerelli leads data and AI platform engineering at Abrigo, and his approach starts with the environment in which banks and credit unions operate. Financial institutions are regulated and audited. Their vendors have to account for those realities from the beginning.
As Piangerelli put it, “Nothing can be a black box.”
This idea is central to how Abrigo develops responsible AI in a regulated environment. AI can help users find information faster, generate drafts, review patterns, and interact with complex data in more natural ways. For those tools to be useful in banking, they also need to be secure, explainable, and transparent.
AI can feel new, but Abrigo’s approach to new technology is grounded in long-standing company principles: protect customer data, protect access to that data, and give users visibility into how systems work.
During our discussion, Piangerelli described Abrigo’s starting point as consistent with other technology decisions the company has made over the past 20 years. Each unique financial institution must be in the driver’s seat and able to defend its software to auditors and examiners.
For AI development, the practical test is straightforward, with focus areas like.
Responsible AI in banking must address those questions before a tool can be trusted for real work inside a financial institution.
One of the clearest risks with AI in financial services is data exposure. Banks and credit unions often want to use AI to search policies, summarize documents, answer questions, or support staff. They also have sensitive data that cannot be treated casually.
Abrigo heard this directly from customers, Piangerelli said. Financial institutions wanted the usefulness of a ChatGPT-like experience, but they could not upload private documentation into public tools.
Abrigo’s response with AskAbrigo, our AI-powered banking agent, was to provide a place where customers could upload and interact with their own documentation. The goal was to give users access to AI-powered knowledge assistance while keeping their information within a controlled environment.
He described it as giving customers “a place within the defined and secure Abrigo-hosted environment, where all their other applications and data have been hosted, to upload safely and to be able to interact with those documents safely through this knowledge agent.”
Secure AI for financial institutions also requires boundaries between private data and public research. Customers may want internet-enabled capabilities for public information, while their internal documents and customer data remain protected.
“[Financial institutions’] data is still kept private, and it's not sent out to the internet in any of these searches,” Piangerelli said. He added that Abrigo prevents the agent from accessing internally uploaded data when the user is interacting with the internet.
For banks and credit unions, those controls make AI more usable. Teams get flexibility without sending sensitive information outside the appropriate environment.
Responsible AI in banking also requires explainable AI. Financial institutions need to know where answers, alerts, summaries, and narratives come from. Piangerelli says Abrigo tools are built with “the ability for the systems to be able to explain and audit what they're doing and decisions they're making.”
The form of explainability depends on the use case.
For Abrigo’s anti-fraud models in Abrigo Fraud Detection, the models are designed to produce an explanation of how they arrive at an answer, including which values were weighted more heavily. In AI-generated narratives or assistant-style tools, explainability may come through documentation and source visibility. When AskAbrigo answers questions using documentation or data, Abrigo provides references that show where the answer came from. Visibility into model behavior helps users understand why a model is surfacing a result. It also supports stronger review, escalation, and documentation.
Anyone who has used AI tools for research or drafting knows how useful source visibility can be. A polished answer can still be wrong, incomplete, or unsupported. In banking, users need a way to verify the answer and decide whether it is usable.
Transparency also means making clear what role the AI plays. Abrigo’s approach keeps people involved in review and decision-making. Piangerelli described the narrative use case, which Abrigo incorporates as a draft-and-edit workflow in solutions for financial-crime fighting, credit-memo generation, loan review, and allowance for credit losses reporting. The AI can generate text. The user, he said, “can agree or disagree or edit it or delete it or do whatever they want.”
Human review is essential for regulated workflows. AI can help users move faster, but the user still brings judgment, institutional knowledge, borrower context, and accountability.
I thought about this in the context of my own work with transcripts. I may use AI to help summarize a long discussion or draft content from a webinar, but I still need to review the result closely. I need to know where the content came from, whether the quotes are exact, and whether the draft reflects the speaker’s meaning.
The same principle applies inside banking workflows. A model or assistant may surface information, create a first draft, or help a user explore data. The person using the tool still needs confidence in the source and control over the final output.
Transparent AI helps create confidence by showing the user what the system used, how the output was created, and where human review belongs.
One of the parts of our conversation on Abrigo’s approach that stood out was Piangerelli’s explanation of how Abrigo thinks about AI and team development. He described Abrigo’s approach as the “Iron Man approach.”
“Instead of building a robot that goes and does your job, what if we built an Iron Man suit?” he said. “You're still in control, but now you are empowered to do some big-time stuff that you couldn't do in the past.”
That is a useful way to think about responsible AI in banking. The strongest applications of AI help skilled people at banks and credit unions do more with the knowledge, judgment, and experience they already have.
For developers, AI may help generate tests, review code, create documentation, or support product workflows. For product managers, it may help create prototypes or documentation that communicate an idea earlier in the process. For bankers, it may help users find information faster, draft narratives, review data, or reduce repetitive work that slows down customer-facing activity.
The common thread is control. The user remains responsible for reviewing the work and deciding how to apply it.
We also discussed an important point about pace. AI is developing quickly, and the pressure to react can be intense. Abrigo’s approach for its financial institution customers is to build from a strong foundation and keep customer trust at the center of the work.
“We've built a business on top of a really secure, very resilient underlying data system, structure that passes all of the regulations, passes all of the audits,” Piangerelli said. “On top of that, we've built software that our customers are pleased with. It is growing, it is getting better, it's getting stronger.”
Abrigo uses that foundation to evaluate AI opportunities. The goal is to bring useful AI into financial institution workflows at a pace customers can depend on, allowing banks and credit unions to adopt AI as they’re comfortable doing so.
In other words, Abrigo is focusing on “the latest and greatest” while incorporating it “at a pace that our customers can really depend on, and trust, and still be out in front,” he said.
Responsible AI in banking requires innovation and continuity. Financial institutions need tools that help them adapt, along with confidence that the systems supporting their work remain secure, explainable, and auditable. The track record Abrigo already has of doing each of these with some 2,400 financial institutions should support confidence in the vendor partnership as banks and credit unions move more into using AI.
For Abrigo, responsible AI in a regulated environment comes down to several practical commitments. It means:
Those principles are critical as AI becomes more embedded in banking workflows. The technology will keep changing, so financial institutions using AI successfully will need systems they can trust, vendors that understand regulation, and tools that support human judgment.
Responsible AI in banking requires discipline. At Abrigo, it also starts with a simple expectation: no black boxes allowed.
Abrigo enables U.S. financial institutions to support their communities through technology that fights financial crime, grows loans and deposits, and optimizes risk. Abrigo's platform centralizes the institution's data, creates a digital user experience, ensures compliance, and delivers efficiency for scale and profitable growth.
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