Credit scoring

One of the most important risks that the banks and financial institutes face, is credit risk which is related to not-paid instalments or the instalments paid with delay by borrowers. Banks use credit scoring models In order to prevent this type of risk. The goal of this research is to investigate the joint effect of economic time cycles and the industry sector on credit scoring models we are seeking to answer the key question: “when should bank change their credit scoring models based on economic time cycles and for which industry sectors?”. The dataset of the research involves all companies that were applied for a loan in one of the Iranian major banks during the years 2008-2011. The companies have been divided into four industry sectors including “Industry and Mine”, “Oil and chemical”, “Service and Infrastructure” and finally “Agriculture”. Based on the sector of the industry and year, 54 explanatory variables, both financial and non-financial, 12 distinct industry sectors and time-specific data sets are built then classification methods were used to classify customers into two groups of defaults and non-defaults. Finally, we compared the results by Wilcoxon Test. The results show that the companies that are in the groups of Industry and Mine and Agriculture, need their own special credit scoring based on industry type model and time but two other groups don’t need of course in the studies dataset duration. Finally, the study concluded by introducing the credit scoring strategies for different four-cycle of economies

The impact of personal judgment on the assessment of an individual’s financial situation has been drastically reduced through the development of credit scoring. The systems are capable of deciding based on an applicant’s total score which is a combination of several factors and indicators. Over the past few decades, credit scoring has been considered an essential tool for evaluation in various institutions and has also been able to transform the industry as a whole. Most of the research conducted in the field has taken into account traditional credit scoring, but considering the ever-evolving technological world that we live in and the increasing emergence of new social media networks, such research has now become obsolete. Such technological advancements have not only paved the way for far more sophisticated credit scoring systems but also essentially rendered the previous generations useless. It should be noted that credit scoring and its features have widely been discussed across the globe but, considering the various aspects and models that have to be taken into account, no one best method has been designed or suggested for it so far. This study shows that social media channels tend to perform relatively well in predicting stock market trends when the overall index is growing positively. The research also illustrates that a higher number of days of activity and a large number of signals released do not necessarily mean that the channels can or have credited their offered stock return on a one-month time frame. The methodology used is "CRISP-DM," which consists of six steps. The main variables include social and financial variables that are examined for six months. In the research, we seek to identify, analyze and categorize active telegram channels in stock signals using the data mining model and the RFM method. The k-means algorithm is selected for this category. Then, in each cluster, the importance of social variables and the performance of the channels are extracted by the EXTRATREECLASSIFIER algorithm, and channel performance is measured by considering the changes in the total index.