Based on the simulations, the most appropriate interest rate is chosen: high enough to attract investors and low enough to attract borrowers. The options which the user is unlikely to handle are filtered out, and only the probable ones are suggested to the user so he could choose the one he finds the most useful.
The system also calculates all the kinds of payments which are included in the loan (principal, interest, servicing fee, etc.) for the user for every time period to see what he pays for. The simulations are run for thousand of users to estimate the probability of receiving a certain amount of money from scheduled payments for each time period.
The internal modules are connected through the API which allows the communication between the modules and internal services. The command line interface helps with experimenting with different machine learning algorithms and ways of estimating the user’s credit grade.
"They are very solution oriented and figured out how to make things work for everyone as opposed to pointing fingers and deflecting."
Oliver Centner CEO at UnoAPP.