The myth of the perfect credit model
The myth of the perfect credit model
By John Downie, CEO & Founder ofSteadypay
Silicon Valley promises the perfect credit model – an algorithm so sophisticated it could predict default risk with near-certainty. But no matter how many billions poured into machine learning, it can’t replace humans. Creditworthiness isn’t a mathematical formula. The perfect model can never exist.
Value in parsing and processing
AI in financial services works in two areas. First, automating repetitive tasks – compliance, fraud detection, loan document processing. Then there’s data-intensive areas where parsing huge amounts of information creates new value. But more data doesn’t automatically mean better credit decisions.
The first is about efficiency – imagine a mortgage assessor handling 3-5 times more applications daily with AI summarising data and flagging areas needing attention. The second isn’t about efficiency at all – it’s about value creation, finding correlations. Yet correlation isn’t causation.
The human element stays
Humans must stay in the loop. When dealing with vulnerability and financial difficulty, high-emotion situations require emotional intelligence that LLMs can’t replicate. In lending, sympathy and wider understanding are essential. While AI can help with recommendations or summarising data, human-to-human interaction remains crucial. Creditworthiness isn’t just about numbers on a spreadsheet.
While the thought of a hybrid approach may feel complicated, measuring ROI isn’t difficult. On the efficiency side, it’s straightforward – are we seeing the benefits we projected? Take transaction classification – is it more accurate, and how often does it need retraining? For more complex setups, we define success measures upfront, test against them, and refine periodically. The key is accepting that “good enough” credit models that improve incrementally are better than chasing an impossible perfect model.
Transparency is a non-negotiable
Regulators are still playing catch-up with new technology – just look at crypto and digital assets. The Consumer Credit Act of 1974 struggles in a digital world, and while there’s been plenty of review papers, meaningful reform continues at glacial pace.
AI must be explainable. A black box decision for something as important as finances is unacceptable. Complex neural networks that even their creators can’t fully explain won’t work. Building sustainable, trustworthy financial services requirestransparency.
The business case for AI needs to stack up – if you’re currently using fuzzy logic, lists, and several humans to classify data, adopting an LLM with fewer humans makes sense. But that’s far from the “AI everywhere” mantras we heard last year. We’re now deep in the trough of disillusionment with AI. Companies have realised the perfect credit model doesn’t exist, and throwing more AI at the problem won’t magically create one. How reliant should you be on a company that might go bust or have their whole business replaced in the next ChatGPT release?
What we’re witnessing demands smart, strategic implementation where it actually moves the needle. The winners in 2025 aren’t the ones with “AI” plastered across their pitch decks, but those who’ve seamlessly integrated it into their core operations with clear metrics and measurable outcomes. Look for those reporting higher revenue per employee and sustained customer satisfaction with leaner teams. They’re the ones who’ve abandoned the myth of perfection in favour of practical, iterative improvements to their credit decisioning.
Throwing AI at everything isn’t a strategy. The perfect credit model is a mirage that leads companies to waste resources chasing diminishing returns. Market leaders are pivoting to hybrid approaches, keeping their crown jewels in-house while leveraging multiple AI providers to future-proof their operations. With players like Google Gemini driving down costs, the economics shift weekly, and those that don’t adapt face a harsh reality.
In a regulated industry where customer outcomes are the bedrock of everything we do, explainability isn’t optional – it’s your bulletproof vest against regulatory scrutiny and legal headaches. The myth of the perfect credit model must give way to the reality of responsible, transparent, and incrementally improving systems. The future belongs to those who’ve integrated AI with a razor-sharp focus on profitability and revenue growth, while keeping that crucial human element in play.
