Model risk management and explainability for regulatory compliance in AI/ML projects
The future of banking will be defined not by if institutions use AI, but by how wisely and responsibly they do so. Compliance in financial services is no longer a static, manual checklist; it has become the most complex challenge in modern banking due to the opacity of AI and mounting regulatory pressure. Right now, […]
5 Proven AI Strategies to Stop Customer Attrition in Banking Before It Happens
What if you could predict when a customer is about to leave before they even realize it themselves? In today’s fiercely competitive banking landscape, financial institutions spend millions acquiring new customers. Yet, the quiet leak in their deposit base through customer attrition continues to drain revenue faster than new acquisition can replenish it. It’s not […]
How Open Data Accelerators Are Redefining the AI Journey?
All organisations are dealing with more data than ever before. Yet, getting that data to deliver meaningful results is often a slow and messy process, and sometimes frustrating. Teams switch between tools, pipelines break, model development takes six months to confirm what we already knew, and by the time the insights reach the hands of […]
The Empathetic Edge: How AI-Driven Smart Collection Agents Are Reshaping Debt Recovery
Debt collection has never had an easy reputation. For decades, it has been associated with rigid scripts, high-pressure conversations, and strained customer relationships. But as we discussed in the recent iTuring.ai Smart Collections Agent webinar, that narrative is changing fast. In the webinar, I had the opportunity to speak with Milind Joshi and Suman Kumar […]
Why ML Ops Is the Backbone of AI Success in Banking
TL;DR Most banking AI fails in the gap between building a model and running it in production, where data science hands models to operations and the two lose sync. ML Ops closes that gap by making models versioned, deployed, monitored, and governed as one continuous practice across data science, IT, and the business. In banking, […]
Why Model Risk Management Matters
Artificial intelligence has moved from experimentation to boardroom priority in a remarkably short time. Most large enterprises now run multiple AI initiatives — fraud detection systems, predictive analytics platforms, generative AI copilots, and customer intelligence tools. Yet one pattern has become increasingly clear to me over the past decade. Many organizations can build AI models. […]
Financial Inclusion Is Failing. The Problem Isn’t Access
TL;DR Financial inclusion has stalled because the industry measured access and treated function as if it would follow. Millions hold accounts yet still cannot get credit, build assets, or take part in the economy, included in form and excluded in function. The barrier is the decision-making architecture: systems built for clean signals like credit scores […]
The Rise of Autonomous Banking
Banks spent decades getting smarter, and intelligence is no longer the constraint. Execution is. Predictive and generative AI mostly produce insights that sit in dashboards waiting for a human to act. Agentic AI changes that: given a business objective, an AI agent investigates, communicates within guardrails, triggers workflows, and updates systems until the objective is […]
Basel III Model Validation for South African Banks: Meeting Prudential Authority Standards for AI Credit Risk and Collections Models
TL;DR On 24 November 2025, the Prudential Authority of the South African Reserve Bank and the Financial Sector Conduct Authority jointly published a report on artificial intelligence in South Africa’s financial sector. The report drew on analysis conducted throughout 2024 and set out, for the first time in a formal joint publication, the regulators’ view […]
TCF Outcome Measurement for Collections: How South African Banks Demonstrate Fair Treatment Under FSCA’s Six Outcomes Framework
TL;DR There is a well-known gap in how South African financial institutions handle the TCF framework. Every regulated bank and insurer has a TCF policy. Most have TCF training programmes. A significant number have TCF committees. What far fewer have is TCF outcome measurement for collections specifically: the operational data, the management information reports, and […]
