Resources

“These capabilities address critical needs for bank and credit union compliance teams,” said Todd Cooper, CEO and co-founder of NuComply. “Automated compliance testing alone delivers hundreds of thousands or millions in hard savings while giving compliance officers a faster and more complete understanding of regulatory compliance performance.”

For decades, bank compliance testing has been constrained by a simple reality: there are more transactions, files, disclosures, and regulatory requirements than compliance teams have time to review. Banks and credit unions have addressed that constraint through risk-based sampling—reviewing a subset of records and using the results to assess the effectiveness of the underlying compliance process.

AI is moving from experimentation to board-level scrutiny in financial institutions. This article explains why black-box AI fails in regulated environments and outlines the governance, explainability, and sourcing controls required to make AI trustworthy for compliance, audit, and regulatory exams.

In this American Bankers Association interview, NuComply CEO Todd Cooper explains how generative AI and large language models can transform banking compliance. He discusses why compliance is the ideal starting point for AI adoption, how banks can meet regulatory expectations, and how AI-powered tools can improve efficiency, accuracy, and responsiveness for compliance teams.

Large Language Models are transforming banking compliance by automating the heavy reading: comparing regulations to policies, flagging misalignment, and delivering cited, explainable insights. Built for regulated environments, compliance-grade AI accelerates reviews, reduces risk, and keeps decision-making firmly in human hands.

Compliance has remained the last major manual function in banking—until now. This article explains why compliance resisted automation, how domain-specific AI and LLMs can safely interpret regulations, identify policy gaps, and support defensible, audit-ready decision-making for banks and credit unions.

In this American Bankers Association podcast, NuComply CEO Todd Cooper discusses how vertical large language models (LLMs) can make AI accurate, auditable, and defensible for banking compliance. The conversation explores why horizontal AI tools create trust challenges for financial institutions and how curated regulatory data, transparent audit trails, and domain-specific AI models enable banks to safely deploy AI within compliance programs.

NuComply Marketing Review embeds compliance directly into your campaign workflow, helping banking and financial services teams launch faster with confidence. By flagging risks early, suggesting citation-backed edits, and enforcing consistent standards, NuComply reduces approval delays, eliminates last-minute rewrites, and keeps every asset audit-ready from draft to launch.

Building compliance-grade AI requires more than a generic LLM. This article explains how curated regulatory content, governed retrieval, lifecycle management, and human-in-the-loop oversight create explainable, auditable AI aligned with banking regulations and institutional risk frameworks.