Duna Platform Cut Financial Compliance Processing Time
The firm integrated deterministic code with AI, helping financial services firms reduce analyst workloads.
Updated on Oct. 2, 2026 in Financial Services
Live Poll
Do you trust financial institutions to manage AI risks using automated oversight systems?
Financial firm SeQura reduced average analyst processing time per case by 16.3-fold after implementing Duna. The system pairs deterministic code with AI agents to handle due diligence, onboarding, and monitoring tasks.
Why it matters
Financial institutions currently struggle with inconsistent AI outputs, as 70% of firms and regulators now cite model hallucinations as a primary operational risk. By grounding AI agents in coded policies, companies can address the reliability issues hindering automated compliance.
Analyst processing time dropped to 14.9 minutes from a baseline of 243 minutes per case. This efficiency gain addresses concerns reported in the 2026 Global AI in Financial Services Report, where 70% of institutions identified AI model hallucinations as a top risk.
The players
Duna
A technology provider that converts bank policies into code to automate compliance, due diligence, and monitoring tasks.
SeQura
A financial services institution that serves as the site of documented system performance improvement.
The details
Duna operates by converting bank policies into fixed code that orchestrates AI agents across screening, adverse media, PEP checks, and ID verification. The engine calls an AI agent when evidence is required, then uses its deterministic logic to evaluate whether the evidence satisfies the policy. The system maintains a complete audit trail, allowing firms to retrospectively identify past cases affected by outdated policy instructions.
Timeline
2026: Release of the Global AI in Financial Services Report.
Market Landscape
The Duna integration follows the findings of the 2026 Global AI in Financial Services Report, which highlighted widespread industry concern regarding unreliable AI outputs. It marks a shift from experimental AI adoption toward deterministic frameworks that prioritize compliance auditability.
Operators should review their compliance tech stacks for deterministic failsafes to mitigate the risks of AI model hallucinations. Managers must prioritize systems that document the specific logic behind every automated decision for regulatory accountability.
The takeaway
Reliable automation requires pairing AI capabilities with rigid, code-based governance to ensure consistent compliance. Audit your current AI workflows to confirm that every automated decision is linked to a transparent, traceable policy record.
Further reading
For broader trends in industry technology, visit the Financial Services section.
Source note: This article includes information reported by FinTech Global.
Live Poll
Do you trust financial institutions to manage AI risks using automated oversight systems?
