Predictive Analytics in Consumer Finance: Modelling Spending Behaviours for Long-Term Economic Resilience

• Published in: 2025 International Conference on Data Science and Business Systems (ICDSBS) pp. 1 - 8
• Main Authors: Dahake, Parihar Suresh, Gajghate, Abhijit, Mohare, Rahul, Bhadade, Pritam
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.04.2025
• Subjects: Analytical models; Business; Cluster Analysis; Companies; Consumer Finance; Data models; Economics; Finance; Forecasting; India; Industries; Predictive analytics; Predictive models; Spending Pattern; Unsecured Consumer Lending
• DOI: 10.1109/ICDSBS63635.2025.11031713

The growing importance of predictive analytics, big data, and exploratory data analysis enables businesses to successfully manage and benefit from a better understanding of their customers. Consumer finance companies can grow and meet investor expectations by investing in products, technologies, and worldwide expansion. Historical insights into consumer spending obtained through technology, regulatory frameworks, and data application for prediction and development of recommendation systems provide enormous opportunity for financial institutions to thrive. Although still in its early stages, predictive analytics is progressing by forecasting and tracking consumer behavior using a combination of customer data, previous company encounters, and predetermined business norms. This article presents a comprehensive examination of predictive analytics in analyzing client spending behaviors in the consumer finance industry, including data features as well as the potential benefits and downsides of various applications and methodology used. Finally, we give a list of subjects that should guide future research projects. This paper describes how a leading online consumer finance company designed and implemented predictive analytics to forecast consumer finance spending behaviors using a financial model, business rules, time-series analysis methods, exploratory data analysis procedures, and visualization techniques. It collects data, evaluates the variables that influence the model, and creates a simple financial forecasting model that includes financial benefit metrics and, based on the results, devises a plan for automating data processing and storage forecasts. The goal of implementing predictive analytics in this firm is to detect and capitalize on emerging market opportunities, automate labor-intensive predictive analytics procedures, and research new possibilities using machine learning. Furthermore, exploratory data analysis can help segment clients based on financial differences.