AI adoption rises in compliance, but measurement still lags
Finance teams are rolling AI into risk and compliance at pace. Over half of compliance professionals say they're using or trialling AI (53%), up from 30% in 2023, and awareness is near universal at 91%. Yet impact is murky. Too few leaders can prove value.
Adoption is high. Proof is thin.
Fewer than a third report a significant impact from AI so far. Among large firms (10,000+ employees), almost a quarter say they can't assess the impact at all. Over a third aren't tracking performance metrics-rising to 41% in banks. Without measurement, budgets are exposed and boards stay unconvinced.
Where AI is working now
The clearest returns sit in high-volume, rules-heavy workflows where speed, pattern detection, and consistency matter. These are also the areas where better data and active oversight drive results.
- Fraud detection and transaction monitoring
- Customer screening and KYC
- Automation of repetitive compliance tasks
Firms seeing real gains tend to have structured, accessible data and keep a human in the loop. Siloed or messy data weakens outputs, no matter how sophisticated the tool.
Fintechs lead-larger institutions are catching up
Fintech professionals report the highest adoption (74%), ahead of banks (50%) and corporates (35%). But larger, traditional players are moving from pilots to scale in specific lines: wealth management (73%) and professional services (60%). The opportunity is clear; the test is turning usage into measurable value.
Compliance is changing-humans still make the call
Most professionals expect their roles to evolve with AI (96%). Only 5% are comfortable with fully autonomous decisions. The direction is a hybrid model: technology for scale and detection; people for judgment, escalation, and accountability. Time shifts from repetitive work to complex, higher-risk scenarios.
A practical measurement playbook for finance leaders
If you can't measure it, you can't manage it-or defend it. Stand up a simple, auditable framework that proves value quarter by quarter.
- Define target outcomes: Fewer false positives, faster case resolution, lower unit cost, stronger detection, improved customer pass rates where appropriate.
- Baseline first: Capture current false positive rate (FPR), precision/recall, time-to-decision, cases per analyst, SAR conversion, customer onboarding time, and cost per investigation.
- Data readiness: Map sources, fix quality issues, standardize fields, document lineage, and set access controls. No clean data, no clean outputs.
- Human-in-the-loop: Set review thresholds, escalation paths, and sampling rules. Track agreement rates between analysts and model outputs.
- Shadow and A/B: Run AI in shadow mode, then A/B against current process. Report weekly deltas on core KPIs.
- Quality metrics that matter: Precision, recall, F1, FPR, alert-to-case conversion rate, SAR hit rate, model drift, and stability across segments.
- Productivity and cost: Alerts per FTE, investigator throughput, average handle time, cost per alert/case, rework rate.
- Risk and control: Override rate, reason codes, explainability availability, policy alignment, and audit trail completeness.
- Governance cadence: Monthly model review, quarterly performance reporting to risk and audit, change logs for datasets and features.
- Iterate with intent: Tune thresholds, improve features, retrain with hard negatives, and recalibrate where bias or drift appears.
Data you must get right
Prioritize a usable data layer before scaling models. That means a living data catalog, clear ownership, reconciled identifiers, reference data control, and event timestamps you can trust. Document assumptions and exclusions so auditors and model risk teams can follow the thread end to end.
For structure, align with recognized practices such as the NIST AI Risk Management Framework. It gives a common language for mapping risks, controls, and monitoring across teams and regulators. NIST AI RMF
90-day plan to prove value
- Days 0-30: Baseline KPIs, data quality fixes, control design, and shadow-mode deployment in one use case (e.g., KYC screening).
- Days 31-60: A/B test vs. current process. Weekly reporting on FPR, precision/recall, time-to-resolution, and analyst agreement rates.
- Days 61-90: Roll out guardrails, finalize SOPs, train teams, and present a board-ready pack with quantified benefits and risks.
What "good" looks like by year-end
- False positives reduced 20-40% in targeted workflows without weakening detection.
- Time-to-decision down 25-50% in onboarding and monitoring.
- Investigations per analyst up 15-30% with stable quality and fewer overrides.
- Model cards, change logs, and audit trails ready for internal audit and regulators.
- Quarterly governance reviews with clear narrative: what AI is doing, why it helps, and where it needs improvement.
Key takeaways for Finance
- Adoption is no longer the hurdle. Proof is.
- Data quality and active supervision separate winners from the pack.
- Pick specific, high-volume use cases and measure relentlessly.
- Treat governance and explainability as an asset. It builds trust and unlocks budgets.
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