Bankadelic: The colorful side of finance
Oct 21, 2026 · 21 min · 12 segments
series of podcasts taped live at 2026’s Money 20/20 in Las Vegas, host Lou Carlozo brings you the latest from one of the premier financial services conferences in the world. On this episode, Lou…
Lou CarlozoHostVelko KamenovGuest
You could have just taken what you did there and rubber stamped it, but instead you decided to build in the US rather than buy in.
What was behind that decision and what kind of advantages do you think it has had for you?
Everything what we have done during almost 20 years to bring here and to make it better.
Everything in a different way and something that we have really succeeded to implement directly here was how we sell, how we manage the risk and how we service the customers under totally different regulations, totally different requirements.
our benefits still continue to be our competitive advantage to be really accessible and of course totally digital approach that really focus on new market, new target markets.
able to transfer from your original market? Where were some of the challenges and where were some of the pleasant surprises?
I will say maybe we have seen that the local access to data that we can manage, it's something that we need really to uplift in order to have our scoring model, internal scoring model, that help us to brighten the access of the new customers.
But Velko can give you more insight about this, the challenge, especially in the underwriting process.
Actually, we managed to transfer the main concepts from Europe pretty well, and we observed some similar trends, but also a lot of differences here in the US.
So what we've managed to transfer is that the knowledge which are the data sources most predictive for underwriting in our segments so these are the open banking data and the credit bureau data but the interesting part for me as a professional in in the market segment where we operate in and in the data science segment creating the scoring models for underwriting has always been the fact that The mainstream off-the-shelf scoring models typically don't work for our target population.
They don't work well enough and we can't underwrite profitably for us and safely on them.
So what we do is that we fully internalize the scoring model so we build features on top of the open banking data transactions that the customers are making we build custom features on top of the great history of our applicants built internal scoring models and in this way we achieve significantly better results in terms of lending money to the right customers for us and from the customer perspective I believe it's also good because what we see across a lot of lenders both in Europe and here in the US is that there are a lot of individual hard criteria leading to rejection of customers for example if let's say a customer has some problematic credit history charged off loans this can be a single factor leading to rejection while we tend not to do this not to rely on single factors in for the decision making in our scoring models and in this way we manage to find the right customers for us to give chance even to such customers to be approved for our products and to make this mutually beneficial partnership between us and the customers and this actually expands our target segment because we manage to work we manage to work profitably and to add value to customers that other lenders neglect due to single individual factors.
Yeah, it sounds like you've really solved for a pain point and there are people who are getting left behind.
You could have just taken what you did there and rubber stamped it, but instead you decided to build in the US rather than buy in.
What was behind that decision and what kind of advantages do you think it has had for you?
Everything what we have done during almost 20 years to bring here and to make it better.
Everything in a different way and something that we have really succeeded to implement directly here was how we sell, how we manage the risk and how we service the customers under totally different regulations, totally different requirements.
our benefits still continue to be our competitive advantage to be really accessible and of course totally digital approach that really focus on new market, new target markets.
able to transfer from your original market? Where were some of the challenges and where were some of the pleasant surprises?
I will say maybe we have seen that the local access to data that we can manage, it's something that we need really to uplift in order to have our scoring model, internal scoring model, that help us to brighten the access of the new customers.
But Velko can give you more insight about this, the challenge, especially in the underwriting process.
Actually, we managed to transfer the main concepts from Europe pretty well, and we observed some similar trends, but also a lot of differences here in the US.
So what we've managed to transfer is that the knowledge which are the data sources most predictive for underwriting in our segments so these are the open banking data and the credit bureau data but the interesting part for me as a professional in in the market segment where we operate in and in the data science segment creating the scoring models for underwriting has always been the fact that The mainstream off-the-shelf scoring models typically don't work for our target population.
They don't work well enough and we can't underwrite profitably for us and safely on them.
So what we do is that we fully internalize the scoring model so we build features on top of the open banking data transactions that the customers are making we build custom features on top of the great history of our applicants built internal scoring models and in this way we achieve significantly better results in terms of lending money to the right customers for us and from the customer perspective I believe it's also good because what we see across a lot of lenders both in Europe and here in the US is that there are a lot of individual hard criteria leading to rejection of customers for example if let's say a customer has some problematic credit history charged off loans this can be a single factor leading to rejection while we tend not to do this not to rely on single factors in for the decision making in our scoring models and in this way we manage to find the right customers for us to give chance even to such customers to be approved for our products and to make this mutually beneficial partnership between us and the customers and this actually expands our target segment because we manage to work we manage to work profitably and to add value to customers that other lenders neglect due to single individual factors.
Yeah, it sounds like you've really solved for a pain point and there are people who are getting left behind.
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