By Suzanne Allen
for the 2025 Risky Women Writing Competition, presented in partnership with Ocorian
Explore how AI can transform risk management by enhancing detection, control assurance, and decision-making – while also meeting rising regulatory expectations.
In today’s fast-paced financial ecosystem, non-financial risks (NFRs) – including cyber threats, conduct breaches, third-party failures, and data privacy incidents – are no longer peripheral concerns. They are central to business continuity, regulatory compliance, and public trust. Yet, many financial institutions still rely on fragmented, reactive approaches to managing these risks.
To truly safeguard their operations and reputation, firms must embrace a strategic shift: integrating Artificial Intelligence (AI) into a unified, proactive framework for NFR governance. This paper explores how AI can transform risk management by enhancing detection, control assurance, and decision-making – while also meeting rising regulatory expectations.
Why This Matters: The Regulatory Wake-Up Call
Global regulators are intensifying their scrutiny of NFRs. In the UK, EU, and US, new mandates on operational resilience, third-party oversight, and data governance are reshaping the compliance landscape. The EU’s Digital Operational Resilience Act (DORA) and AI Act signal a clear message: financial institutions must evolve their control environments to address emerging risks with precision and agility.
Governance That Drives Accountability
Effective NFR management starts with strong governance. This includes:
- Clearly defined roles and responsibilities (e.g., RACI matrices).
- Cross-functional committees that unite risk, compliance, legal, tech, and business teams.
- Escalation protocols and transparent reporting to senior leadership.
Governance must not be a checkbox – it should be a dynamic mechanism that fosters ownership and responsiveness across the enterprise.
A New Blueprint: Integrated Taxonomy and Control Framework
To break down silos, firms need a common risk taxonomy that categorises NFRs consistently across geographies and business units. This shared language enables better risk identification, assessment, and reporting.
Building on this, a holistic control framework should integrate three critical domains:
- Operational Resilience – controls that ensure the continuity of important business services during disruption. This includes scenario testing, impact tolerance thresholds, and recovery playbooks.
- Third-Party Risk Management – controls that assess and monitor the risk exposure from vendors, cloud providers, and other external partners. This includes due diligence, contract clauses, performance monitoring, and exit strategies.
- Data Privacy & Information Security – controls that safeguard customer and firm data, ensure compliance with data protection laws (e.g., GDPR), and prevent unauthorised access or leakage.
This approach ensures that controls are not only comprehensive but also interconnected – reflecting the complex nature of today’s risk landscape.
From Reactive to Proactive: Embedding Risk into Strategy
Many firms still treat NFRs as afterthoughts – responding only when incidents occur. This reactive posture is no longer viable. Instead, risk must be embedded into strategic
and operational decision-making.
Proactive mechanisms include:
- Real-time Key Risk Indicators (KRIs)
- Risk acceptance protocols
- Policy exception governance
These tools foster a culture of informed risk-taking, where decisions are made with full awareness of potential exposures.
AI as a Game-Changer in NFR Management
AI offers transformative capabilities across the NFR lifecycle:
- Real-Time Detection
Machine learning models can scan vast datasets – emails, logs, chats – for anomalies. NLP tools can flag conduct risks by analysing tone and intent in communications. - Predictive Risk Assessments
AI can forecast operational failures, classify vendors by risk tier, and anticipate control breakdowns using historical data and external signals. - Automated Control Testing
RPA and AI-driven reconciliation tools enable continuous assurance, reducing reliance on manual audits and improving coverage. - Dynamic Risk Inventories
AI can auto-tag risks, suggest control enhancements, and generate tailored dashboards for different stakeholders – streamlining governance. - Data Privacy Protection
AI helps identify sensitive data, monitor unauthorised access, and support consent management – critical for compliance with GDPR and similar regulations. - Third-Party Risk Intelligence
AI can scrape external sources to detect reputational issues, score vendors, and map supply chain dependencies – enhancing resilience. - Scenario Modelling
AI-powered simulations support stress testing, impact tolerance validation, and operational resilience planning.
Final Thoughts: Building a Future-Ready Control Environment
Embedding AI into the GRC ecosystem is not just a technological upgrade – it’s a strategic imperative. It enables financial institutions to move from fragmented, reactive controls to intelligent, adaptive systems that evolve with the risk landscape.
However, this transformation must be underpinned by robust AI governance – ensuring transparency, accountability, and ethical use.
As financial services continue to digitise and globalise, NFRs will grow in complexity. Strengthening internal controls is no longer optional. It’s the foundation for resilience,
trust, and long-term success.
By embracing integrated frameworks, proactive governance, and AI-driven innovation, firms can build a control environment that is not only compliant – but bold, agile, and
future-ready.