AI is helping revolutionise risk management

AI is revolutionising risk management from manual, point‑in‑time assessments to forward‑looking, real‑time decisioning across the risk lifecycle. The latest global KPMG thought leadership explores how AI, generative AI and advanced analytics are helping organisations strengthen risk identification, assessment, monitoring and reporting.

Building on the KPMG Future of Risk Survey, it shows why AI‑enabled risk functions are becoming a strategic advantage — not just a compliance necessity – where integrated, data‑driven risk management is becoming central to resilience and decision‑making.

With the regulators here in Aotearoa New Zealand signalling stronger expectations around forward‑looking risk frameworks, governance and data‑driven monitoring, this paper outlines how organisations both globally and here in New Zealand can adopt AI in risk management in a way that is trusted and explainable.

Leverage AI in your risk management ecosystem

Explore today’s AI maturity landscape, discover standout use cases, and chart a fast path to an AI‑enabled future of risk.

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The five stages of AI maturity in risk management

Findings from our research

Risk identification

AI can generate process flows, detect emerging risks, and recommend mappings to risk taxonomies, processes, and controls. This helps organisations identify risks more accurately and earlier.

Risk assessment

AI can recommend risk ratings, generate and monitor key risk indicators, and calculate residual risk. By making risk assessment more probabilistic, AI can enhance the precision and consistency of risk ratings.

Risk mitigation

AI tools support decision making around risk response strategies and automate or optimise mitigation actions. For instance, they can identify issues and root causes, review and design control inventories, and monitor alerts more efficiently and precisely.

Risk monitoring

AI enables real-time or continuous monitoring of risk indicators, producing aggregated reporting and moving from point-in-time reporting to more dynamic, real-time capabilities.

Risk review and reporting

AI can help improve the efficiency and quality of risk reporting by automating the generation of reports, thematic analysis, and standardised risk and control report outputs.

Testing and validation

AI can automate control testing activities, validate control effectiveness, and detect anomalies across large data sets. By continuously learning from historical patterns and outcomes, AI enhances the accuracy, efficiency, and coverage of testing activities — helping reduce manual effort and enabling faster identification of control weaknesses.