Leveraging data quality analytics for enhanced loan application and performance insights: A comprehensive approach

The objective of this research is to discover the ways in which the banking sector can use data quality analytics to improve the processing of applications for loans and gain better insights into their performance. We use SQL, Power BI, and Python to examine how these decisions can be improved, how customer experiences can be made better and more compliant, and how we can use good data analytics to ensure that proper regulatory guidelines are followed. To do this, we employ a quasi-mixed method that solves the "what" question with descriptive and inferential statistics, the "how" question with good data visualization, and the "why" question using advanced analytics. The data we use encompass over 300K records of loan performance and over 150K entries of loan applications from the years 2020–2022. Data cleaning, transformation, analysis, and visualization are done using SQL, Python, and Power BI to derive actionable insights. The analytics side of data quality is examined in this study with a view to understanding its impact on the loan application process and the performance of the business. The analytics have shown that the quality of data and the insight which can be gained from it has a material effect on the aforementioned processes and performance. The study shows how sophisticated analysis can be applied in the actual operations of a banking institution. It underscores the value of solid customer and loan data for making high-level decisions within the bank.