How AI Can Fix Your Due Diligence Bottlenecks

Global compliance spending has reached $206 billion annually, with 98% of institutions reporting rising costs. In the US and Canada alone, compliance costs total $61 billion per year. Yet despite this massive investment, enforcement actions continue to escalate.

In 2024, Commerzbank was fined €1.45 million for late CDD/KYC refreshes — a task that should be automated. The penalty wasn't for missing fraud; it was for not keeping up with paperwork. That's the compliance paradox: enormous spending, still falling behind.

The root cause is clear. Traditional due diligence processes were designed for a world with fewer regulations, fewer data sources, and slower-moving threats. They cannot scale cost-effectively.

Agentic AI differs from static rule engines by running autonomous, context-aware workflows that adapt to the complexity of each case. Five transformative capabilities emerge:

• Accelerated triage: Flags high-risk entities early, enabling analysts to prioritise genuinely suspicious cases rather than processing everything sequentially.
• Entity resolution and UBO discovery: Automatically reconciles company names, shareholders, and beneficial owners across multiple registries with continuous sanctions screening.
• Adverse media aggregation: Pulls news, regulatory filings, and social mentions to contextualise risk — across languages and jurisdictions.
• Contract-level risk analysis: Scans agreements for exposure risks related to clauses, obligations, and counterparty profiles.
• Continuous monitoring: Post-onboarding reassessment alerts teams to changes in ownership, sanctions status, or regulatory filings.

Critically, human-in-the-loop design keeps analysts in control. AI handles the mechanical research; analysts make the judgement calls.

A mid-sized US regional bank processing 200-300 vendor onboardings per quarter illustrates the transformation. Each vendor previously required 6-8 hours of analyst time for comprehensive due diligence.

After deploying agentic AI:

• Vendor profiles triaged and reconciled in under one hour
• Fewer false positives — analysts focus only on genuinely high-risk cases
• Expanded regulatory coverage from multiple global data sources
• Significant cost savings through reduced overtime and headcount needs

The potential time reduction across due diligence reviews: up to 70%. That's not optimisation — it's a structural change in how compliance work gets done.

Deploying AI in compliance requires deliberate governance. Six best-practice phases:

• Start small with a pilot: Choose a narrow, well-defined use case to test workflows and validate accuracy without disrupting operations.
• Enforce data security and privacy controls: Implement encryption, access controls, and GDPR/CCPA compliance. Restrict agent access to necessary data only.
• Integrate seamlessly with existing systems: Connect to CRM, GRC, ERP, and document management systems to reduce manual handling.
• Maintain auditable trails and explainability: Every decision the AI makes should be traceable, so analysts can defend risk decisions during regulatory reviews.
• Establish feedback loops: Incorporate analyst feedback, monitor performance, and update models regularly to prevent drift.
• Scale with governance: Establish policies for model risk management, oversight, and compliance as operations expand.