Short answers. The long ones are the conversation.
We make the score you already have sharper, starting from what's in place. Your credit box, rules, vendors, and people stay. We take the data you already collect, build one model per source with the bank statements first, blend them into a score tuned to your book, and show you what it's worth in dollars before you buy it.
No. We give them better glasses. If you have a data scientist, we work alongside them and leave them stronger than we found them. If you have a risk officer and no data scientist, we're the data scientist. Either way, the decisions stay with your people.
No, and we won't pretend to. We sharpen the score that tells your underwriters where to look. If what you need is underwriting judgment, a credit box, or a policy written down for the first time, that's a different conversation, and we're glad to have it.
No. Slop is what you get when the tool is driving. Models are models, the math is the science, and we were building them by hand for twenty years before anyone called it AI. Do we use AI to build and test now? Absolutely. A model that took us months to build at Credibility takes days today. That isn't a shortcut around the expertise; it's the expertise finally moving at the speed of the ideas. We can try ten things where we used to try one, throw out nine, and show you the one that moved the number. Knowing which nine to throw out is what you're paying for, and no model does that part for us.
Keep them. We use what they produce. Where a vendor is costing you more than it's lifting, the assessment will say so, and that's your call to make with a number in hand.
It's the standard measure of how well a score separates the deals that pay from the deals that don't. Zero is a coin flip, one is a crystal ball, and most of the industry lives between twenty and fifty. The number page explains it in a paragraph and turns it into dollars.
For the assessment: your funded deals from the last two or three years, with how each one performed, and whatever you collected at application, bank data above all. Declines help too, if you kept them. We'll send a one-page list after the first call so nobody's guessing.
To a locked machine, not to a cloud, and never to an AI company. To start, that machine is ours. When you're ready, we set the same thing up inside your walls and the data never leaves the building again. If you want the box unplugged from the internet, it can be.
Yes. Yours or ours. Nothing moves before it's signed.
The first conversation is free. The assessment is a flat fee, agreed before we start. The system is a build fee plus an ongoing fee you keep paying only while the score keeps improving. If all you want is an hour of our time on a specific problem, that's $750, and gladly.
The assessment takes weeks, not months, once the data lands. How long the system takes depends on your stack, and we'll tell you honestly on the call rather than guess here.
It hasn't happened yet, and we'd love to be there when it does. If your book is already scored as well as the data allows, then congratulations: you've won risk management, there's nothing more to see here, and we'll refund the assessment fee. You're doing it right, and that deserves a round of applause, not an invoice.
A scorecard on your risk function, graded the way your capital would grade it if they knew how, and the number: where the lift is in your book, by segment, in dollars. The scorecard covers ten areas: the data you collect, the data you buy and what each vendor is lifting per dollar, the score itself and how it holds up over time, where your cutoff sits against where the money is, whether one score is being asked to do three jobs, what your declines would have done, how fast you'd know the score had drifted, the renewal book, fraud and stacking, and whether your policy is written down in a form a lender to lenders can read. You keep the scorecard whether or not you ever buy anything else.
No score, no data scientist, maybe no written policy yet. We can help. Let's talk. It may not be this product; it may be consulting: what to collect from day one so a model is possible next year, which vendors to call and which to skip, a credit box and a policy you can hand to your capital with a straight face. We'd be happy to do that work. Reach out.
If you fund lenders, their underwriting is your collateral. We can read it for you before a facility and watch it during one, in the same language we'd use with the lender. Talk to us.
SBA lenders, whose world is regulatory in ways we don't work in. Banks with deep manual underwriting shops, who've decided that's the business. And anyone who wants a vendor to blame instead of a number to act on.
A Bayesian surprise is the data point that should change your mind. Finding yours is the whole job.