“Sequence matters: first be an expert in your business, then become an effective AI user.” For Nick Gushchin, Founder of Swiss AI Chatbot Factory, that principle isn’t theory—it’s a career arc.

A former Swiss corporate banker and self‑taught Python developer, Nick has lived at the intersection of banking fundamentals and applied AI. His message to credit professionals is pragmatic and direct: AI is moving from pilots to daily practice, and those who anchor in core credit skills while adding just enough technical fluency will lead the next chapter.

Across traditional credit and lending, Nick sees AI embedded in workflows end‑to‑end. In the front office, assistants and copilots help relationship managers prepare meetings, surface comparable deals and draft client materials. In risk and compliance, models support screening, transaction monitoring, fraud detection, and early‑warning indicators of distress. The throughline is speed and coverage without abandoning judgment. “These copilots won’t replace judgment,” he emphasizes, “but they will compress analysis time and increase coverage, allowing teams to evaluate more cases with greater depth.”

The most immediate shift, he says, is arriving in underwriting. “The next wave in lending is AI copilots for underwriters and credit analysts – systems that assemble a complete view of a borrower by cross‑checking financials with market standards, peer positioning, and non‑financial signals such as supply‑chain events, management changes and regulatory news.” For practitioners wrestling with volume and complexity, that capability turns the first mile of analysis from a bottleneck into an accelerator.

Nick’s advice on skills is shaped by his path from banker to builder. A baseline of AI concepts and programming literacy “even at a ‘read and adapt’ level”, helps credit professionals collaborate with technology teams, challenge models intelligently and avoid treating them as black boxes. It also sharpens day‑to‑day practice. “It improves prompt quality and review discipline – you know what to ask for, what to double‑check, and what ‘good’ looks like in a credit context.”

At the same time, he is unequivocal that technical skills cannot substitute for banking fundamentals. “AI gives extreme leverage, like steroids. Used correctly by a trained professional, it builds capability but used without fundamentals, it can do damage.” The anchor remains core credit judgment regarding risk appetite, structuring, collateral, covenant design, regulatory constraints, sector cycles, client context and more. Models surface patterns, human bankers interpret signals, weigh trade‑offs, and take responsibility for decisions.

His practical advice for staying relevant starts with mastery of the core, then adding two “boosters.” “Learn just enough Python to build and audit simple analytics beyond Excel, and to understand how data pipelines and basic ML workflows hang together.” And use generative‑AI tools daily as copilots. “Use ChatGPT, Claude, and similar for research synthesis, drafting memos, scenario framing, and first‑pass modeling ideas. Pair them with practical, real cases (anonymised if needed) rather than theory.”

Whilst AI can reduce the friction, he’s keen to emphasise that “the tool amplifies what you already do well; it should not replace your judgment.”

An example from his own practice illustrates the payoff. “Recently I assessed an unfamiliar industry in a different geography. Using Perplexity’s Deep Research, I generated in about an hour what would previously have taken weeks: a structured overview of key players, market size, margins, business models, pressure points, and risk factors, with cited sources to verify.” The result wasn’t automation of a decision – it was a compressed path to a credible first map. “My expertise still determined what to trust, where to dig deeper, and how to translate findings into a credit view.”

Looking ahead, Nick argues the human banker’s role grows more important. “With AI expanding access to information and enabling deeper cross‑checks, decisions will be better informed but also more complex.” The banker becomes “a high‑value expert and trusted advisor who uses AI as a copilot to explore scenarios, stress‑test assumptions, and communicate trade‑offs — and then owns the decision, the ethics, and the client relationship.”

What will distinguish the best professionals at the credit–AI intersection? He highlights four qualities. “First, storytelling and framing – the ability to speak with AI through clear prompts, and to translate model outputs into crisp narratives stakeholders can act on. Secondly, systematic thinking – building repeatable frameworks for how information is gathered, validated, and compared across cases. Next, critical thinking – interrogating sources, recognising model limitations and bias, and knowing when not to automate. And finally, domain depth and judgment – the core banking fundamentals that keep decisions safe, fair, and value‑creating.”

For credit professionals, the takeaway is clear. AI is becoming part of the fabric of lending—from relationship preparation to risk controls to underwriting copilots. It rewards those who lead with domain expertise, add practical programming literacy, and use generative tools to remove low‑value friction while sharpening decisions.