Credit Risk Management through Big Data Analytics - Sharma, Deepika (International Journal of Computer Sciences and Engineering, 2025)
Credit risk remains till date one of the biggest and most challenging issue in the lending financial institutions. Credit risk refers to the probability of default which may occur if the liabilities are not fulfilled under the terms of the contract, resulting into the loss of the financial institution or banks (the creditor). Difficulties in credit risk management arise because the credit default occurs mostly, unexpectedly. The databases of the banks around the world have accumulated large quantities of information about clients and their financial and credit history. These databases can be used for the credit risk assessment, but they are generally high dimensional and traditional data analytics may not be able to handle such large volume of high dimensional data. How to develop a high-performance platform to efficiently analyze the Big Data Analytics (BDA) that can lead to better and more informed credit decisions? This study seeks to answer this question by discussing a macroscopic view of emerging Big Data techniques for addressing the vital issues of credit risk across the various sectors of finance and aim to identify the suitable BDA tools for the purpose of managing Credit Risk.