By Rajarshi Banerjee | Banking Transformation Advisor | Ex-Axis, Exim Bank, First Capital
In my 25+ years of working across India and Africa—from flagship branches in Kolkata to the boardroom in Kampala—I've seen the face of risk management evolve from manual reports and gut instinct to dashboards and predictive analytics. But what we’re seeing now with AI and ML isn’t just evolution—it’s revolution.
Artificial intelligence (AI) and machine learning (ML) are not just buzzwords. They’re becoming central to how we assess credit, detect fraud, and protect our financial systems. And as someone who’s spent a fair part of their life navigating credit risk and reducing NPAs, I can tell you—this shift is both exciting and deeply necessary.
The Upside: Where AI Is Making a Difference
🔹 Spotting trouble early when we brought NPA levels down from 14% to under 5% at EXIM Uganda, we did it with tight monitoring, weekly credit reviews, and sheer operational discipline. Today, AI models can detect those red flags months in advance—anomalies in repayment patterns, sector-wide stress indicators, or even social media sentiment—before a loan turns bad. Imagine the power of that in hands-on credit management.
🔹 Serving the underserved One of the biggest blockers in MSME and microfinance lending has always been: “But where’s the documentation?” AI doesn’t need a traditional balance sheet. It can look at transaction history, phone usage, and repayment on digital wallets—and deliver credit scores for people who’ve never had a bank statement. In parts of India and East Africa, this is already opening doors for small merchants and informal entrepreneurs.
🔹 Smarter fraud detection Fraudsters keep innovating. So do algorithms. AI can now flag unusual patterns in real time—a login from two different countries, a sudden spike in transfers, or behavior that deviates from the customer’s norm. That kind of agility was unthinkable even five years ago.
But Let’s Be Clear—AI Isn’t Risk-Free
Like any powerful tool, AI needs guardrails. I’ve worked on enough risk audits and compliance frameworks to know that if we don’t design this technology responsibly, it could do more harm than good.
🔸 Bias and unfair decisions: If your data is biased, your model will be too. An AI that penalizes borrowers based on zip codes or misinterprets financial behavior can end up reinforcing exclusion.
🔸 Privacy concerns: AI thrives on data. But with great data comes great responsibility. Financial institutions must have rock-solid data governance—especially in a world of rising cyber threats and stricter privacy laws.
🔸 Explaining the black box: If a customer is denied a loan, and the only answer is “the algorithm said so,” that’s not good enough. Transparency is non-negotiable. We need to make AI explainable, auditable, and accountable.
So, What Should Banks and FinTechs Be Doing Now?
✅ Start with a clear strategy: AI in risk management isn’t just an IT project—it’s a business imperative. Link it directly to your goals: reducing NPAs, improving underwriting, fighting fraud. Own it at the leadership level.
✅ Invest in people and mindset: AI isn’t here to replace risk managers—it’s here to empower them. Upskill your teams. Encourage data-literacy across functions. Build bridges between credit officers and data scientists.
✅ Build strong governance: Set up review committees. Test your models regularly. Keep human oversight in the loop—especially for decisions that impact customers’ lives.
Final Thoughts
AI and ML won’t replace human judgment. But they will amplify it, challenge it, and—if used wisely—enhance it.
As someone who’s led banking transformations on the ground—from rural branches in Jharkhand to pan-African fintech implementations—this is a rare moment. It is a moment when technology can truly redefine how we understand, measure, and manage risk.
The question is not if we embrace it—but how well we do it.
If you’re leading a financial institution, building a fintech, or investing in this space, I’d love to hear your thoughts. Let’s connect. Let’s build the future of risk—together.
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