Review of predictive modeling and machine learning applications in financial service analysis
Year: 2024
Type: article
Abstract: The financial services industry is undergoing a profound transformation driven by the adoption of predictive modeling and machine learning (ML) technologies. These advancements enable financial institutions to leverage vast datasets, optimize decision-making processes, enhance customer experiences, and mitigate risks more effectively. This review provides a comprehensive exploration of how predictive modeling and ML are revolutionizing various domains within financial services, including risk management, fraud detection, customer analytics, and market forecasting. Predictive modeling techniques, such as regression analysis, decision trees, and support vector machines, are instrumental in assessing credit risk, optimizing portfolio management, and improving financial forecasts. In parallel, ML algorithms particularly deep learning and natural language processing offer sophisticated approaches to detecting fraud, identifying transaction anomalies, and extracting insights from unstructured data sources like social media and financial reports. The review also highlights the application of ML in personalizing customer interactions, enabling banks and fintech companies to deliver tailored products and services that enhance customer loyalty. Despite the promising benefits, integrating these technologies presents challenges related to data privacy, model interpretability, and compliance with regulatory standards. The need to address algorithmic biases and ensure data security remains paramount as the financial industry navigates the complexities of digital transformation. This review emphasizes the importance of striking a balance between innovation and ethical considerations to foster trust and transparency. By synthesizing current applications, industry use cases, and future trends, this review underscores the critical role of predictive analytics and ML in shaping the future of financial services. It advocates for continued research and collaboration to unlock new opportunities and drive sustainable growth in the sector. Keywords: Predictive Modeling, Machine Learning, Financial Service Analysis, Review.
Institution: East Texas A&M University
Language: English
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4404602479