Leading with Intelligence: Members Driving AI Adoption

• Government Focus on Skills & Capability: AI is recognised as a strategic priority, with emphasis on building the right skillsets—not just technical coding skills, but also business acumen to apply AI effectively.

• Balancing Innovation with ROI & Risk: Successful AI adoption requires careful governance, especially when aiming for value creation. It’s most effective in low-risk scenarios where return on investment can be clearly measured.

• Human Capital as the Foundation: AI success depends on diverse talent—drawing from data science and mathematics, not just traditional finance backgrounds. This mirrors past shifts like the rise of credit cards.

• Explainability & Fairness in Credit Decisioning: Ensuring fairness in AI models is critical. Bias can be mitigated by comparing historical data trends, but human oversight remains essential to interpret and validate decisions.

• Designing Ethical AI Models: Models should reflect values, not replicate past human biases. Explainability must be built into model design, using transparent techniques like regression coefficients where possible.

• AI’s Business Value in Fraud Analytics: AI has already delivered tangible benefits in fraud detection, showcasing its potential to enhance operational efficiency and generate real business value.