<caption><image_73>The image shows a man standing in front of a large screen with the logo of Tua Financial on it. He is wearing a blue shirt and appears to be giving a presentation. The screen is displaying the company's logo in blue and green colors. Behind the man, there is a bar with various items on it and a crowd of people sitting in the foreground. The image appears to have been taken in a conference room or auditorium.</caption> there's some people who might just have the money in the account they don't want to pay you right they just want to keep it there are other people who you know absolutely intend to honor their obligations and they're just really bad with money and they just are never going to have it and you know these are things that you generally see in the credit file now these are extrapolations right and they're approximations they're never completely accurate but what we tend to look at in credit modeling is you know the past behavior is fairly indicative so one of the things that we look at in assessing somebody who either has a pretty bad credit file or no real credit file is to look at their bank account right so we asked them to log into their online banking we tell them exactly what we're gonna do we get a copy of their last 90 days and we can look and see well are they bouncing a lot a lot of checks do they make more money than they spend what's the minimum balance on their account we can make determinations that are just as good as what we would find in a credit file about the probability that they're going to be in Oh overpay in terms of you mean paying more than they are or yes they're the same thing the the algorithm well the algorithm that I wrote three and a half years ago was an auto ML algorithm to apply all different types of algorithms
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