Credit EDA

EasyChair Preprint 12726

Abstract

This research paper delves into the realm of credit analysis through an in-depth exploration of two distinct datasets related to client loan applications. The first dataset encompasses a comprehensive array of client information recorded at the time of loan application, while the second dataset provides insights into the client's historical interactions with the loan application process. Our methodology comprised separate analyses of each dataset, followed by a meticulous integration process aimed at facilitating a holistic examination of credit-related trends and patterns. By employing a diverse set of exploratory data analysis techniques, including descriptive statistics, data visualization, and correlation analysis, we unearthed intricate relationships within each dataset. This approach enabled us to gain a nuanced understanding of client creditworthiness and the factors influencing loan approval outcomes. Moreover, the amalgamation of findings from both datasets enriched our insights, revealing critical connections between application attributes and historical application outcomes. This paper contributes to the evolving landscape of credit analysis by emphasizing the importance of leveraging diverse datasets for a comprehensive understanding of client credit profiles and enhancing decision-making processes in the financial domain.

Keyphrases: Exploratory Data Analysis (EDA), Financial decision making., Loan approval, Payment difficulties, client history, credit analysis, credit based datasets, data integration, data visualization

@booklet{EasyChair:12726,
  author    = {Pavan Manikanta Bellam and Rohith Chinnamallappagari and Manisha Chandramaully},
  title     = {Credit EDA},
  howpublished = {EasyChair Preprint 12726},
  year      = {EasyChair, 2024}}