AI and Predictive Analytics in Risk Management

This advanced training course empowers professionals to integrate artificial intelligence (AI) and predictive analytics into their risk management strategies. As organisations face rising complexity and volatility, traditional risk assessment methods are no longer sufficient. Leveraging data-driven technologies enables proactive identification, quantification, and mitigation of risk—creating more agile, resilient, and competitive enterprises.

Participants will explore AI models, machine learning algorithms, and predictive tools that support dynamic risk forecasting. The course combines technical understanding with strategic thinking, offering a hands-on framework for operationalising intelligent risk management across governance, compliance, finance, cybersecurity, and operations.

Section 1: Foundations of Risk Analytics and Artificial Intelligence

• The evolution of risk management in the digital age.
• Understanding AI, machine learning, and predictive analytics.
• Risk management frameworks and AI integration points.
• Traditional vs. intelligent risk management models.
• Organisational readiness for AI-based risk systems.

Section 2: Predictive Modelling Techniques for Risk Identification

• Data types, quality, and sources for risk analytics.
• Building predictive models using supervised and unsupervised learning.
• Identifying patterns in operational, financial, and reputational risk.
• Scenario analysis and forecasting tools.
• Key risk indicators (KRIs) and early warning systems powered by AI.

Section 3: AI in Operational and Enterprise Risk Management

• Using AI to detect anomalies, fraud, and cybersecurity threats.
• Integrating AI with ERM platforms and GRC systems.
• Automated risk scoring, profiling, and reporting.
• Case studies: AI in supply chain, credit risk, and insurance.
• The role of generative AI in dynamic risk documentation and analysis.

Section 4: Strategic, Regulatory, and Ethical Considerations

• Regulatory frameworks (EU AI Act, GDPR, ISO 31000) and their implications.
• Bias, transparency, and fairness in predictive decision-making.
• Risk governance in AI model deployment.
• Accountability and explainability in AI-driven insights.
• Risk culture and change management for intelligent systems.

Section 5: Building and Scaling AI-Driven Risk Strategies

• Designing an AI-enabled risk strategy aligned with business goals.
• Cross-functional collaboration between risk, data science, and IT.
• Key success factors in AI implementation.
• Building a roadmap for predictive risk maturity.
• Final group activity: Developing an AI-based risk response plan.

Upon successful completion of this training course, delegates will be awarded a Holistique Training Certificate of Completion. For those who attend and complete the online training course, a Holistique Training e-Certificate will be provided.

Holistique Training Certificates are accredited by the British Accreditation Council (BAC) and The CPD Certification Service (CPD), and are certified under ISO 9001, ISO 21001, and ISO 29993 standards.

CPD credits for this course are granted by our Certificates and will be reflected on the Holistique Training Certificate of Completion. In accordance with the standards of The CPD Certification Service, one CPD credit is awarded per hour of course attendance. A maximum of 50 CPD credits can be claimed for any single course we currently offer.