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GL20, the prudential standard that governs operational risk management in insurance, demands far more than policies and paperwork. It requires a living, breathing control environment—one that continuously identifies, mitigates, and monitors risk in a dynamic business landscape.
But here’s the problem: traditional compliance methods don’t scale.
Insurers dealing with multiple business units, geographies, and technology stacks quickly find themselves drowning in manual processes. Spreadsheets, fragmented risk assessments, and siloed control testing make it nearly impossible to maintain compliance, let alone demonstrate it.
This is where automation and AI step in—not as a shortcut, but as the only viable way to scale GL20 compliance with precision and confidence.
In this blog, we’ll unpack:
- How automation and AI solve the most painful parts of GL20 compliance
- The specific areas where they deliver value
- Best practices for implementation
- And why insurers who don’t modernise their compliance infrastructure are falling behind
GL20 Is Not Just a Framework—It’s a Living Obligation
GL20 requires insurers to demonstrate effective operational risk management. This includes:
- Timely identification of risks
- Appropriate internal controls
- Clear ownership of controls
- Monitoring and reporting mechanisms
- Regular reviews of risk and control effectiveness
The intent is clear: risk management must be embedded into daily operations, not treated as a quarterly afterthought.
The challenge? These processes are deeply people-dependent and error-prone when handled manually—especially in fast-moving, tech-driven insurance ecosystems.
Where Automation and AI Are Changing the Game
Here’s how insurers are using automation and AI to move from static compliance to scalable, dynamic GL20 adherence:
1. Automated Control Mapping and Maintenance
Keeping an up-to-date control library and mapping it to risk categories and regulatory requirements is tedious and resource-intensive.
With automation, controls are:
- Tagged and categorised consistently
- Dynamically updated across business units
- Mapped to relevant GL20 clauses with traceable logic
2. AI-Powered Risk Identification and Prioritisation
AI can scan operational data (e.g., incidents, complaints, audit logs, call transcripts) to surface emerging risks and control gaps. Natural Language Processing (NLP) helps identify thematic risks from unstructured sources like customer complaints or agent notes.
3. Smart Control Testing and Exception Reporting
Instead of manual, point-in-time control testing, automation platforms can perform continuous control monitoring using:
- Pre-built testing scripts
- Integration with systems of record (HR, finance, claims, etc.)
- Threshold-based alerts for breaches or anomalies
4. Automated Evidence Collection and Audit Trail Creation
Preparing for a GL20 audit often involves manually compiling control evidence from emails, folders, and disparate tools. AI-enabled platforms can:
- Auto-capture control execution evidence
- Timestamp actions
- Maintain immutable logs and generate reports on demand
5. AI-Driven Policy Intelligence and Regulatory Mapping
AI tools can scan new or updated regulatory texts and automatically suggest changes to your control library or risk taxonomy.
6. Real-Time Dashboards and Reporting for the Board
GL20 expects senior management and the Board to have visibility into operational risk. Automated dashboards provide:
- Heatmaps of residual risk
- Control effectiveness ratings
- Action plans with ownership and timelines
Challenges in Adopting Automation and AI for GL20
Like any transformation, scaling GL20 compliance through AI and automation isn’t plug-and-play. You need to overcome:
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Data Fragmentation
Control and risk data often live across tools (GRC systems, Excel, emails). Consolidation is key.
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Trust in AI Decisions
AI models must be explainable and auditable. Governance matters—especially when making risk-based recommendations.
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Change Management
Compliance teams must be trained not just on tools but on a new way of working—automated, proactive, and collaborative.
Best Practices for Success
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Start with High-Risk, High-Value Use Cases
Focus on areas with repeatable, data-heavy processes—like control testing or incident classification.
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Embed Governance Early
Ensure your automation and AI tools align with GL20 expectations for traceability, accountability, and transparency.
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Align with the Three Lines of Defence
Ensure that automation supports—not replaces—first-line ownership, second-line oversight, and third-line assurance.
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Use a Scalable Platform
Ad hoc automation may solve a local problem, but to scale GL20 compliance, you need a unified platform that grows with your needs.
Why the Future of GL20 Compliance is Automated
Manual compliance efforts can’t keep up with today’s regulatory and operational complexity. Automation and AI offer:
- Speed: Faster testing, reporting, and remediation
- Accuracy: Reduced human error and clearer audit trails
- Agility: Real-time response to changes in risk or regulation
- Scalability: Consistent controls across geographies, teams, and processes
As GL20 matures and expectations rise, the burden on compliance and risk teams will only increase. Automation and AI aren’t just efficiency tools—they are strategic enablers of trust, accountability, and resilience.
Want to scale GL20 compliance without scaling your headcount?
Talk to ComplyNexus—we help insurers automate risk identification, control testing, and compliance reporting, all while ensuring AI governance and transparency.
Your path to continuous, scalable compliance starts here.
