Accountability vital when financial crime compliance employs AI

When turning to AI for financial crime compliance, FIs must make auditability and responsibility key features of the system. “Make accountability and evidence part of the design," Madhu Nadig , co-founder and chief technology officer at tech firm Flagright , told FinAi News . "Every use case needs an accountable owner, clear limits on what the system can do and records that allow a decision to be reconstructed." [caption id="attachment_57390804" align="alignright" width="300"] (AI generated)[/caption] Auditability is vital for an FI implementing AI across anti-money laundering (AML), fraud and sanctions work, according to the 800-page guide titled "The Global Handbook for AI in Financial Crime Compliance," released this week by Flagright . Prior to publication, the handbook was reviewed by compliance leaders at institutions including Western Union , UniCredit Poland and Betfred , according to Flagright. "Reviewers need to understand the output and have the authority to challenge it," Nadig said. "An institution should be able to demonstrate why its AI-assisted decisions are reliable and how it responds when something goes wrong." This clarity into an AI's workflow helps humans make informed decisions and holds them accountable. "You can't sue an agent; you can't hold an agent accountable for anything," Andrew Bud , chief executive of biometric identity verification company iProov , told FinAi News . "You're going to have to have human accountability for everything that happens in the agentic environment," he said. 'Meaningful oversight' While AI can gather information, identify patterns and prepare reports, humans still need to assess those results, challenge gaps or inconsistencies and take responsibility for decisions, Nadig said. "An AI-generated investigation summary should help an analyst reach the underlying evidence faster," he said. "The analyst still needs to verify material facts, consider alternative explanations and decide whether escalation is justified." All AI work must be auditable to promote informed decisions and to enable humans to figure out what went wrong in case of error. "Meaningful oversight requires access to evidence, appropriate training, enough time to review and authority to override or escalate," Nadig said. "A human approval button provides little protection if the person cannot understand or challenge what they are approving," he said. Look skeptically at fewer alerts Although AI can help FIs reduce false positives in AML processes, fewer alerts don’t necessarily mean fewer problems, according to the handbook. “The danger comes when a lower alert count is treated as proof that detection has improved,” Nadig said. “Alerts can fall because a system distinguishes suspicious activity more accurately. They can also fall because thresholds or suppression rules remove activity that should still be investigated. “Institutions therefore need to examine what no longer reaches investigators," he said. This includes sampling suppressed and low-priority alerts to ensure the AI is not dismissing alerts that should be escalated to an investigator. "Institutions should also be honest about measurement limits," Nadig said. "Sampling and scenario testing provide evidence about missed risk; they do not establish the complete number of suspicious activities missed in live operations." Register here for early-bird pricing for the FinAi Banking Summit, taking place March 1-2 in Denver. View the full event agenda here .

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Accountability vital when financial crime compliance employs AI

Flagright CTO: Fewer alerts could mean more problems are slipping past AI detectors

Microsoft says it’s working to fix Outlook, Copilot outage

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