Authors : Snehal Shingode; Saurabh Thaware; Sakshi Katre; Harshal Balpande; Shravasti Gaikwad; Adil Sheikh; Harsh Dubey

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/5akc8wtn

Scribd : https://tinyurl.com/bdz9vwj4

DOI : https://doi.org/10.38124/ijisrt/25apr1924

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Abstract : In the banking and finance industry, exploratory data analysis, or EDA, is essential because it helps businesses extract insightful information from big, complicated datasets. EDA aids in the identification of patterns, the detection of anomalies, and the comprehension of underlying trends that influence decision-making in this field. EDA enables financial institutions to better understand market dynamics, risk factors, portfolio performance, and consumer behaviour by applying statistical and visualisation approaches. The uses of EDA in banking and finance are examined in this research, with a focus on how it might enhance investment strategies, fraud detection systems, and credit scoring models. It also emphasises how crucial feature selection, data preprocessing, and visualisation tools are to supporting efficient data-driven decision-making.

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