This thesis focuses on the determinants of loan defaults of peer-to-peer lending platforms and analyzes whether the dependent variable “default” can be predicted. The datasets used for this research are 211.283 Bondora platform loans and 38 variables. The time period used is from February 2009 until October 2022 and only completed (paid off or defaulted) loans are considered. P2P lending is the act of lending money to individuals or small and mid-size enterprises via online platforms that connects lenders and borrowers. One of the hot topics in this field is risk assessment of applicants. A P2P lending company, in order to make sure the client will be able to pay back the loan in agreed duration, assesses the risk of each applicant individually. This will be done using a decision tree model to measure the default probability of loans.The main findings of this research are that age,PrincipalBalance,interest rate, loan duration, MonthlyPayment and Debt to Income are positively related to the probability of default. In contrast, the borrowers’ income, number of previous loans and value of previous loans have a negative correlation with default risk. In addition, a decision tree model and scorecard is performed to predict the probability of loan default. The predictive ability of the decision tree model is examined, through comparison with other machine learning models as Neural Networks, Random Forest & Decision Trees. Based on the predictive measures, the misclassification rate, the accuracy and the ROC curve, it appears that the Decision Tree model (Chi-Square) has relatively good predictive ability. Additionally, we examined the ability of the customers in repaying credit loans by classifying the loan receivers as ‘high risk or ‘low risk’ using scorecard in SAS. The term ‘low risk’ states that the loan receiver has an acceptable score and there has been no problematic payment records. On the other hand, the phrase ‘high risk’ suggests the opposite, that the applicant has a bad credit score or there were records for delayed payments or past defaults. Scorecards are essential in the lending process as they quantify the risk associated with loan applicants. They transform various borrower attributes, such as credit history, income levels, and employment status, into a numerical score. This score indicates the likelihood of a borrower defaulting on a loan, helping lenders make informed decisions that balance risk and reward.