Implementing CRISP-DM and Logistic Regression for Predictive Analysis in Financial Transactions: A Case Study

As artificial intelligence, data science, machine learning, and deep learning continue to advance, their applications span broader domains. Understanding financial transaction behaviors is crucial for financial institutions to predict and prevent fraudulent activities, improve customer satisfaction, and enhance decision-making processes [1]. This research addresses the critical need for accurate predictive models in financial transactions, providing a framework that can be applied to similar datasets in the industry. This paper delves into this evolving landscape by applying the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to a financial dataset. Implementing CRISP-DM and Logistic Regression for predictive analysis is versatile and widely applicable, extending beyond financial topics to effectively predict diabetes as well [2]. CRISP-DM offers a structured approach to planning data mining and machine learning projects through its six sequential phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Logistic regression was selected as the predictive model for this study due to its effectiveness in binary classification, interpretability, robustness, and simplicity [3]. The dataset analyzed consists of 200,000 rows and 200 numerical variables [4]. Exploratory Data Analysis (EDA) was conducted to understand the data's characteristics. The data preparation phase involved selecting relevant data, cleaning it, and transforming it for modeling. No missing values were found, and the dataset was split into training (70%) and validation (30%) sets. Logistic regression was chosen due to its effectiveness in binary classification problems. The model was trained on the training set and evaluated on the validation set. The evaluation metrics indicated high accuracy (0.9146) and specificity (0.9867) but low sensitivity (0.2689), suggesting the need for further refinement to improve sensitivity. Predictive modeling in financial transactions is a well-explored area, with numerous studies employing various machine learning techniques. Prior research has demonstrated the utility of methods like decision trees, neural networks, and support vector machines in predicting financial behaviors [5]. This study builds on this foundation by integrating the CRISP-DM methodology, ensuring a systematic approach to data mining projects. The primary contribution of this paper is the application of the CRISP-DM methodology to a realworld financial dataset, showcasing its effectiveness in structuring data mining projects. The novelty lies in the detailed implementation of logistic regression within this framework, highlighting the model's strengths and weaknesses and suggesting potential improvements for better predictive performance. The Business Understanding phase involved understanding the financial domain and specific challenges faced by Santander Bank in predicting customer transaction behaviors.