Credit default represents a significant breach in financial conduct, occurring when borrowers fail to meet the obligations stipulated in a loan or credit agreement. The repercussions of such defaults are far-reaching, extending beyond immediate financial strain to long-term consequences such as severe impairment of credit scores, possible legal ramifications, and notably, restricted access to future credit facilities. In this context, the ability to preemptively identify potential defaults becomes crucial, serving as a cornerstone for risk mitigation strategies in financial institutions.
Our project is primarily focused on this predictive challenge. We aim to harness the predictive power of data analytics to forecast the likelihood of customers defaulting in the upcoming months. By utilizing historical payment data and demographic information, we seek to build a model that not only predicts defaults with a high degree of accuracy but also aids creditors in making informed decisions. The intent is to refine the allocation of credit resources, safeguarding the financial ecosystem against the perils of non-payment while extending credit to those deemed capable of sustaining it.
In navigating through this complex domain, our study recognizes the intricate balance between facilitating credit access and maintaining fiscal responsibility. The ensuing pages detail our exploration of a dataset, robust in demographic and financial variables, and delineate the methodologies that lead us towards predictive insights, ultimately aiming to curtail the risks associated with credit defaults and support sustainable credit distribution.
