How to Use AI in Financial Crime Compliance

You’re hired as a GRC Analyst at a fast-growing fintech company that just integrated AI-powered fraud detection. The AI flags transactions as “suspicious,” but customers start complaining that their accounts are being unfairly locked. Regulators begin investigating for potential bias and unfair decision-making. How you would tackle this? 1. Assess AI Bias Risks • Start by reviewing how the AI model makes decisions. Does it disproportionately flag certain demographics or behaviors? • Check historical false positive rates—how often has the AI mistakenly flagged legitimate transactions? • Work with data science teams to audit the training data. Was it diverse and representative, or could it have inherited biases? 2. Ensure Compliance with Regulations • Look at GDPR, CPRA, and the EU AI Act—these all have requirements for fairness, transparency, and explainability in AI models. • Review internal policies to see if the company already has AI ethics guidelines in place. If not, this may be a gap that needs urgent attention. • Prepare for potential regulatory inquiries by documenting how decisions are made and if customers were given clear explanations when their transactions were flagged. 3. Improve AI Transparency & Governance • Require “explainability” features—customers should be able to understand why their transaction was flagged. • Implement human-in-the-loop review for high-risk decisions to prevent automatic account freezes. • Set up regular fairness audits on the AI system to monitor its impact and make necessary adjustments. AI can improve security, but without proper governance, it can create more problems than it solves. If you’re working towards #GRC, understanding AI-related risks will make you stand out.

Today we’re presenting the findings from our clients using AI agents, in production for 3 months. We can cut customer wait times stuck in a KYC/sanctions queue from 20 days to 2 minutes. This is a huge unlock for anyone onboarding customers. “Compliance Officer” is the 5th fastest growing occupation in the United States! Major banks average 307 employees just for KYC alone, yet can't hire more compliance officers fast enough. More than headcount, this costs customers and revenue. We deployed AI agents in production environments at multiple financial institutions for 3+ months and show AI Agents can meaningfully improve KPIs: - For one FI, the daily backlog was 14 hours and they couldn't keep up with it.  - So the backlog kept growing  - As did the average customer wait time stuck in a queue, to 20 days. Using Agentic AI, we were able to  - Automate majority (95%) of the cases and  - bring down daily backlog to 41 minutes (from 14 hours).  - Most importantly, avg customer wait time went down drastically to 2 minutes. Perhaps the most counterintuitive finding. Agentic AI when trained and deployed according to our framework, can be more accurate than humans. We found AI agents follow operating procedures in 100% of cases vs <95% for humans. Humans never follow SOP to the minute details and with rote work, they are more error prone. FI's rightly worry, what about hallucination? What about data privacy? Will the regulator allow it These live, production data points are all within existing regulatory frameworks (SR 11-7 compliant). Our Agentic Oversight Framework maintains complete human accountability while delivering: - Alignment to Standard Operating Procedures (SoPs) - A full audit trail of every data element accessed - A full, explained decision rationale, reviewed before every case is progressed - Continuous learning from expert reviewers - Automated drift detection and safeguards The white paper is a playbook for how financial institutions can safely implement agentic AI while fully complying with regulatory requirements. Real results. Real institutions. Real transformation. You might ask: what is AI about all of this and how's it different from ML and rules based systems. In short, rules systems are rigid but Agentic AI can adapt. All those details in the white paper:

AI agents are not just another model. They are a forcing function. Most compliance teams are drowning in alerts, manual checks, and backlogs that stretch onboarding from days to weeks. More headcount is no longer the solution. Enter the Agentic Oversight Framework. (I’m attaching Sardine’s whitepaper, which outlines how financial institutions can safely deploy agentic AI within compliance while maintaining control, auditability, and regulatory alignment.) At one financial institution, the KYC backlog dropped from 14 hours to 41 minutes. Customer wait times collapsed from 20 days to 2 minutes. And with 100 percent precision on approved onboardings, the risk posture actually improved. The secret? AI agents paired with human oversight in a structured, auditable loop. Forget static rules and brittle workflows. Agentic AI learns your SOPs, applies them in context, and escalates only what matters. It does not just automate. It adapts, reasons, and refines. It forces you to rethink where human judgment is actually needed. The result? Faster revenue, stronger compliance, and a team that can focus on true positives instead of sifting endless false alarms. Agentic AI is not a replacement for compliance officers. It is a multiplier for their judgment. If your compliance workflows look the same after AI, you are using it wrong. 𝐀𝐈 𝐬𝐡𝐨𝐮𝐥𝐝 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐲𝐨𝐮𝐫 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐚𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞 𝐭𝐡𝐞𝐦. H/T: Peter Slattery, PhD (give him a follow).