International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 05 | May 2024
p-ISSN: 2395-0072
www.irjet.net
BAYESIAN INFERENCE IN THE ERA OF BIG DATA: TRANSFORMING FINANCIAL DECISION-MAKING Saloni Thakkar Texas A&M University, USA
--------------------------------------------------------------------------***----------------------------------------------------------------ABSTRACT Numerous domains, including finance, can benefit from the probabilistic modeling and inference capabilities of Bayesian Networks (BNs). This article explores the foundation, principles, and applications of Bayesian networks, focusing on their potential to revolutionize the process of financial decision-making. The comprehensive case study conducted regarding the utilization of Bayesian Networks in credit scoring demonstrates their superior predictive capabilities when compared to traditional methods, as well as their ability to capture complex interrelationships among credit risk factors. The paper highlights the advantages of Bayesian networks, such as their capacity to incorporate expert knowledge, represent uncertainty, and produce results that are straightforward to comprehend [1]. In addition, challenges associated with integrating Bayesian networks with alternative machine learning techniques, concerns regarding scalability, and the scarcity of data in practical financial contexts are discussed [2]. Potential future research and innovation avenues are also deliberated, with particular emphasis on the utilization of big data and alternative data sources to enhance the precision and robustness of Bayesian Network models within the finance sector. In an effort to assist academics and professionals who wish to make informed financial decisions using Bayesian networks, this article endeavors to bridge the gap between theory and practice. Keywords: Bayesian Networks, Financial Decision-Making, Credit Scoring, Probabilistic Modeling, Machine Learning Integration.
INTRODUCTION The approach to complicated problems involving uncertainty has been greatly influenced by the emergence of Bayesian Networks (BNs), a powerful framework for probabilistic modeling and inference. Rooted in the groundbreaking research of 18th-century Thomas Bayes [3], and further advanced by pioneers like Judea Pearl [4] and Richard E. Neapolitan, Bayesian networks have been widely adopted in industries such as artificial intelligence, healthcare, and finance. Bayesian networks provide a compelling solution to the challenges of modeling and decision-making in the finance sector, especially when dealing with uncertainty. Due to the intricate relationships between various factors such as economic indicators, company performance, and investor sentiment, financial markets are inherently complex. Traditional financial models often struggle to incorporate the probabilistic nature of financial events and capture their complexities [5]. Through the use of a logical method, Bayesian networks assist analysts in understanding uncertain events and reaching informed conclusions [6]. Bayesian Networks (BNs) utilize Bayesian inference and a directed acyclic graph (DAG) to illustrate probabilistic dependencies [7], facilitating the integration of expert knowledge and data-driven insights. In recent years, there has been a significant rise in interest in the application of Bayesian networks in the field of finance. Researchers and experts have explored the use of BNs in various areas such as credit risk assessment, fraud detection [8], portfolio optimization, and financial forecasting. Bayesian Networks have become increasingly popular in the financial domain because of their ability to handle incomplete data, model causal relationships, and produce understandable results [9]. This article aims to provide a comprehensive introduction to Bayesian networks and their applications in finance. The fundamental ideas of Bayesian networks are outlined, including directed acyclic graphs, conditional probability, and inference algorithms. The use of Bayesian Networks for credit scoring is thoroughly examined through a case study. This study demonstrates the effectiveness of these models in predicting outcomes and representing complex relationships between credit risk factors, when compared to more conventional techniques. In addition, we cover the advantages of Bayesian networks in finance, including their capacity to interpret findings and incorporate uncertainty and expert knowledge. The discussion includes an exploration of the challenges and possible directions for future research and innovation. It highlights the advantages of integrating machine learning and Bayesian network techniques with large datasets and alternative data sources. This article aims to bridge the gap between theory and practice in order to assist researchers and practitioners in making well-informed financial decisions using Bayesian networks. The goal is to contribute to the ongoing discussion and advancements in this captivating field by exploring the principles, applications, and potential futures of Bayesian networks in finance.
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