As the financial services landscape accelerates toward digital maturity, Artificial Intelligence (AI) has emerged as a transformative force within banking and insurance. Institutions are rapidly embedding AI into risk scoring, fraud detection, underwriting, customer service, and investment advisory functions. While these innovations deliver enhanced agility and decision intelligence, they also present complex governance challenges. The growing use of black-box models, evolving regulations, and ethical dilemmas now demand a strategic shift in how organizations manage AI-related risks.
Pideya Learning Academy introduces the AI Risk Governance in Banking and Insurance training program—a focused initiative designed to equip financial sector professionals with the expertise to govern AI adoption responsibly and align it with enterprise risk management, regulatory compliance, and ethical integrity. As financial institutions become data-driven, a proactive governance model becomes not just a regulatory requirement but a business imperative.
According to the World Economic Forum’s 2024 Financial Services Outlook, nearly 70% of financial institutions have integrated AI into their operations, yet only 35% have a defined AI governance framework. Furthermore, a Deloitte global survey indicates that 54% of BFSI executives express concern over AI’s regulatory uncertainty and its potential to increase exposure to legal and reputational risk. These statistics underscore the urgency for well-structured AI oversight models that balance innovation with responsible risk management.
This comprehensive course by Pideya Learning Academy bridges the critical knowledge gap between AI innovation and compliance imperatives. It offers a deep dive into global regulatory trends—such as the EU AI Act, U.S. AI Bill of Rights blueprint, and APAC-specific AI policies—and their implications for banking and insurance. Participants will explore the foundations of ethical AI, model risk management, explainability requirements, and how to navigate algorithmic bias and data transparency issues.
Learners will benefit from exposure to real-world scenarios that illustrate how governance failures have led to operational disruptions, legal disputes, and reputational damage. The course also emphasizes proactive AI audit preparation, third-party AI risk evaluation, and model validation frameworks, ensuring that organizations are not only compliant but resilient in the face of emerging technologies.
Throughout the program, participants will build a practical understanding of how to establish AI governance charters, develop risk mitigation controls, and align AI initiatives with broader enterprise values and ESG targets. By engaging in group dialogue, case study analysis, and structured frameworks, attendees will leave empowered to influence their organization’s AI governance strategy with confidence and foresight.
Key highlights woven into this training experience include:
Detailed insight into global AI regulatory ecosystems relevant to BFSI sectors.
Strategic frameworks for identifying, classifying, and mitigating AI-associated risks across data pipelines and operational systems.
Techniques to enhance AI explainability, trust, and transparency within internal governance structures.
Exposure to ethical dilemmas, bias detection methodologies, and model fairness controls.
Readiness strategies to meet internal audit and external supervisory expectations.
Approaches to integrating AI governance into enterprise risk and compliance programs.
Exploration of cross-border risk implications, vendor risk due diligence, and third-party AI tool assessments.
By the end of the training, participants will have the knowledge and tools to lead organizational change, embed responsible AI practices, and anticipate regulatory developments before they become business disruptors. The AI Risk Governance in Banking and Insurance course by Pideya Learning Academy is not just about compliance—it’s about empowering financial professionals to harness AI responsibly and sustainably for the future.

Key Takeaways:

• Detailed insight into global AI regulatory ecosystems relevant to BFSI sectors.
• Strategic frameworks for identifying, classifying, and mitigating AI-associated risks across data pipelines and operational systems.
• Techniques to enhance AI explainability, trust, and transparency within internal governance structures.
• Exposure to ethical dilemmas, bias detection methodologies, and model fairness controls.
• Readiness strategies to meet internal audit and external supervisory expectations.
• Approaches to integrating AI governance into enterprise risk and compliance programs.
• Exploration of cross-border risk implications, vendor risk due diligence, and third-party AI tool assessments.

• Detailed insight into global AI regulatory ecosystems relevant to BFSI sectors.
• Strategic frameworks for identifying, classifying, and mitigating AI-associated risks across data pipelines and operational systems.
• Techniques to enhance AI explainability, trust, and transparency within internal governance structures.
• Exposure to ethical dilemmas, bias detection methodologies, and model fairness controls.
• Readiness strategies to meet internal audit and external supervisory expectations.
• Approaches to integrating AI governance into enterprise risk and compliance programs.
• Exploration of cross-border risk implications, vendor risk due diligence, and third-party AI tool assessments.