Ελαχιστοποίηση πιστωτικού κινδύνου με χρήση αλγορίθμων μηχανικής μάθησης
Minimization of credit risk with the use of machine learning algorithms
The so-called credit features, that is the features based on which someone can be evaluated as to whether one is creditworthy for the loan repayment or not, conduct a very important part of the banking everyday routine since more and more people wish to obtain customer loans. Of course, now a days, the features based on which one can be evaluated for their creditworthiness are so many that consists human analysis even infeasible. In the following thesis we will see if the machine learning algorithms can take into consideration, the numbers as well as the words, that are available as credit features and decide whether the loan receiver will repay the loan or if the financial institute is under the risk of loss.