The End of Exclusive Credit Intelligence

For decades, the best credit intelligence lived behind the walls of the biggest banks. JPMorgan, Citi, and HSBC, these institutions had the teams, the budgets, and the proprietary datasets to build risk models that smaller players couldn't touch. Regional banks made do with stale data. Trade finance shops relied on gut instinct. Community lenders crossed their fingers.

That world is disappearing. As credit markets face their most volatile decade in a generation, access to timely intelligence isn’t just a competitive edge — it’s survival.

AI is breaking down the old hierarchy. Tools that once required massive infrastructure and years of in-house expertise can now be deployed by a 50-person lender in Ohio or a trade finance shop in Singapore. Credit intelligence is becoming infrastructure, not an advantage.

This matters more than it might seem. When local lenders and regional asset managers can evaluate credit risk with the same precision as global banks, markets work better. Capital moves where it should. Borrowers get fairer assessments. And the system becomes less fragile because fewer institutions are making all the important decisions.

Seeing What Others Miss

Take First Brands Group’s September 2025 bankruptcy. The automotive parts supplier collapsed, taking $1.2 billion in creditor value with it. Jefferies lost $715 million. UBS funds got hit for another $500 million. Most lenders saw it coming, maybe six months out, when the cash crunch became obvious.

But peer network analysis flagged the risk 42 months earlier. Cooper Standard, another automotive supplier, filed Chapter 11 in April 2022. That bankruptcy wasn’t just one company’s problem. It was the first crack in a network of interconnected suppliers all carrying the same vulnerabilities. By 2024, 20% of automotive suppliers were already in distress before tariffs even hit. The suppliers in plastics, rubber, and metal manufacturing saw distress rates jump from 25% to nearly 40%. First Brands operated across all these segments.

The kicker? Private suppliers like First Brands were 27% more likely to fail than their public peers, but they flew under the radar because they didn’t report earnings. While public companies like AutoZone maintained solid ratings, private players in the same supply chain were quietly deteriorating.

A regional lender using AI-driven peer analysis could have caught that divergence and either avoided the exposure or priced it correctly. Most didn’t have the tools. The big banks did.

From Relationships to Reality

Credit analysis used to be an art. You looked at management experience, reputation in the industry, and maybe a few years of financials. If you knew the CEO personally, even better.

Those factors still count, but they’re not enough anymore.

Take a mid-sized manufacturer looking for financing. Ten years ago, a regional lender saw limited financial history and maybe a personal introduction. Today, that same lender can pull real-time credit signals, sector benchmarks, supply chain risk data, even climate exposure metrics. The judgment call doesn’t go away. It just gets backed by actual data.

Why This Is Happening Now

Several forces converged to make better credit intelligence essential:

• Markets got volatile. Interest rates whipsawed. Inflation came back. Geopolitical risk spiked. Institutions of all sizes needed sharper tools just to survive
• Private credit exploded. Billions moved into private markets, deployed by new entrants operating outside traditional bank channels. Without better intelligence, that growth could turn into a mess.
• Climate became financial. Companies now get evaluated on environmental metrics, not just balance sheets. That requires entirely new forms of data.
• Emerging economies opened up. Access to credit expanded in developing markets. Better risk assessment helps ensure that growth doesn’t collapse into a default crisis. Meanwhile, developed economies face their own new vulnerabilities, supply chain fragility, and digital infrastructure risk.

Resilience Through Distribution

Here’s the uncomfortable truth: When only a handful of global banks can see risk clearly, the system is brittle. Concentrated intelligence means concentrated decision-making. When those few institutions screw up, everyone pays.

But when regional banks, trade finance firms, and asset managers can see risk just as clearly, resilience gets distributed. The system strengthens. Capital flows more confidently, not just to the biggest borrowers but also to small and mid-sized enterprises, emerging markets, and new industries.

The Hard Part

Democratization won’t happen by itself. Slapping “AI” on a product doesn’t cut it. The real test is whether these tools actually give smaller players a fair shot at competing.

That requires three things:

• Trust. Credit professionals need to understand how the AI reaches its conclusions. Black boxes don’t inspire confidence. If you can’t explain why the model flagged a borrower, no one will use it.
• Transparency. Firms need to see the data sources and methodologies. Without that visibility, decisions get skewed by biases no one can identify.
• Usability. The tools have to work for actual credit officers and portfolio managers, not just for Ph.D.s in quantitative finance. If the interface is incomprehensible, the technology is worthless.

Get those three right, and AI becomes infrastructure for a more open financial system. Get them wrong, and it’s just expensive noise.

What Comes Next

The old hierarchy is cracking. The biggest banks no longer have a monopoly on the best intelligence. AI isn’t just changing workflows; it’s redistributing power.

The firms that win in this new world won’t be the ones with the deepest pockets. They’ll be the ones that combine data, context and speed to make better credit decisions faster. Intelligence becomes a shared advantage, not a proprietary fortress.

This evolution is about more than technology. It’s about fairness, resilience, and opportunity. When more participants can compete on equal terms, markets function better for everyone.

Rajiv Bhat, CEO, martini.ai

Rajiv Bhat is co-founder and chief executive officer at martini.ai, a leader in AI-driven credit analytics. Rajiv was co-founder at social commerce startup Mertado (Y Combinator W2010) through its acquisition by Groupon. Later, he led data science at ad tech unicorn InMobi. He holds a Ph.D. in theoretical physics from the University of Colorado at Boulder and an undergraduate degree from the Indian Institute of Technology (IIT) Kanpur.