Human-machine trust in credit systems

Balancing innovation and regulatory compliance in AI-driven credit models is a critical challenge for financial institutions. As AI expands into credit risk assessment, banks will need to navigate the fine line between leveraging cutting-edge technology and adhering to stringent regulatory requirements. AI-powered credit models offer unprecedented accuracy and efficiency in assessing creditworthiness, analyzing vast amounts of data to identify patterns and predict default risks. However, the "black box" nature of some AI algorithms poses significant compliance risks and can erode trust. To address this, credit issuers are implementing robust model risk management frameworks. This includes clearer documentation, rigorous testing, and ongoing monitoring to ensure AI models remain accurate, fair, and compliant over time. Regulatory sandboxes are emerging as valuable tools, giving banks the ability to test AI solutions under regulatory supervision. Transparency and explainability are paramount. Financial institutions will be required to ensure their AI systems can provide clear rationales for credit decisions, aligning with regulations like GDPR and potential AI-specific legislation. This is smart business: commitment to transparency fulfills regulatory requirements and builds trust with customers and stakeholders. This often requires balancing advanced AI techniques with more interpretable models. Collaboration between AI teams, compliance officers, and regulators is vital. Early engagement with regulators and a proactive approach to addressing issues can help organizations navigate the complex regulatory landscape while still driving innovation. Careful management can harness AI's potential to enhance credit risk management while maintaining regulatory compliance as well as customer trust.

Our industry can often get caught up in the speed and impact at which AI is advancing. But I believe it is also important that we stop to understand how we’ve got to the evolved use of AI that we now use today. 8 years ago at the start of our Rich Data Co journey (team pic below), AI was not as advanced as it is now. Charles Guan, Gordon Campbell, Michael Coomer and I had a big belief that there was more that AI could do, and that in financial services, it was bankers, credit analysts and portfolio managers that could benefit most. At RDC we knew we could be leaders in this industry by improving how AI can learn from humans – we call this “teachable intelligence” in AI decisioning for business and commercial lending.   When we think about ‘Teachable Intelligence’, it is all about how AI learns from humans (bankers, credit analysts and portfolio managers) and supports humans to make better decisions, ultimately building trust in the platform. When we apply teachable intelligence for business and commercial lending, we think about how: 1️⃣ Most business and commercial lending decisions are complex, and the stakes are high. 2️⃣ For several decades, Commercial and Business banks globally have leveraged experienced people to make these decisions.   3️⃣ The RDC platform leverages ‘Teachable Intelligence’ to support bankers, credit analysts and portfolio managers to make better decisions through deeper insight into their customers financial behaviour. 4️⃣ Applying AI in Commercial and Business banks has several core challenges: small data, complex data, high regulatory hurdles, difficulty in capturing knowledge and experience. Therefore we designed an AI Decisioning platform that supports this through: data foundation, combining machine learning and knowledge management in AI, Combining rule-based decision systems with AI and Self-describing decisions.   Through building trust between AI and humans, we can apply more accurate and explainable decisions in the credit lending process. In this article I share more of my insights around how we’re using the concept of teachable intelligence at RDC and some of the challenges we had to overcome to become the industry leaders that we are today: https://lnkd.in/etNxqySz #ArtificalIntelligence #MachineLearning #FutureOfCredit

Tech can approve a loan in seconds. But can it spot a lie? Everyone’s talking about the “paperless,” “AI-driven” future of lending. I hear it every day from industry leaders. Smooth onboarding, instant credit checks, no messy paperwork—sounds perfect, right? But let’s be honest. That’s not the full story, especially if you’re building for Bharat. At Refinserv, our team is out in rural India every single day. We see the excitement around digital lending, but we also see what algorithms miss. Here’s how it actually goes: → AI scans bank statements → AI checks credit scores → AI runs the numbers and gives you a green light in minutes But AI can’t ask the awkward questions. It can’t read the pause on a phone call, the sidestep in a conversation, or the real reason a borrower wants that loan. That’s where human intelligence steps in—and sometimes saves the day. Real story: A borrower fills out everything perfectly. Great score, strong statements, no red flags. On paper, he’s a dream. But one of our team calls him up. Within five minutes, just by asking the right questions, we discover he’s about to use that loan for risky trading—not the “business expansion” he claimed. No system would have caught that. No app. No AI. That’s why as we expand from North India to the rest of Bharat, we’re betting on a mix: AI for the speed, HI (human intelligence) for the trust. People don’t trust platforms. They trust people who listen, ask questions, and look them in the eye—even if it’s on video. Here’s what I know: → You can automate processes. → But you can’t automate intent. → Trust will always need a human face. So when someone says, “Bharat will go 100% digital,” I just smile. We’re not even close. Not for the real Bharat, where lives change with every loan. Lending will only truly scale when we build for both—AI and HI together. Would you trust an algorithm alone for your most important financial decision? Or do you still want a real person on the other end? What’s your take?