Faculty Publications
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Item Feature Selection for Peer-to-Peer Lending Default Risk Using Boruta and mRMR Approach(Institute of Electrical and Electronics Engineers Inc., 2023) Anusha Hegde, H.; Bhowmik, B.Peer-to-peer (P2P) lending in the Financial Technology (FinTech) sector is increasingly gaining attention from people where the online platform enables lenders to offer loans to borrowers. The platform as a much needed mechanism targets to reduce the risk of default and increase profitability for lenders and the platform. Each loan record maintains a variety of attributes, including details about the loan, the borrower, their credit history, their finances, and public data. If all the features are considered, the performance of the lending platform may decline. Finding the necessary characteristics more helpful in forecasting loan default is a concern. This paper investigates essential features of the P2P lending mechanism with adequate performance in lending money to individuals or businesses. We employ two algorithms to find the pertinent features: Boruta and Max-Relevance and Min-Redundancy (mRMR). Further, we use two classifiers-decision tree and XGBoost that exercise the selected elements to predict the loan defaults. © 2023 IEEE.Item Big Data Insights: Pioneering Changes in FinTech(Institute of Electrical and Electronics Engineers Inc., 2024) Anusha Hegde, H.; Bhowmik, B.The amount of data generated and stored by finance sector companies is rapidly increasing, allowing corporations to conduct data analytics and enhance their businesses. However, data scientists face immense challenges in efficiently handling massive amounts of data and generating insights with real business value. Big Data Analytics (BDA) tools and methods are required to handle vast data. Financial Technology's (FinTech's) growth in mobile Internet, cloud computing, big data, search engines, and blockchain technology has dramatically changed the financial industry. The appropriate application of big data in the management and business innovation of FinTech is therefore a significant concern that confronts the whole finance industry. This paper explores the significance of big data methods in the financial sector and offers insights into the difficulties in applying them as well as future potential for technological advancement. Along with its classifications, the paper examines how FinTech evolved from traditional to modern banking. Corporate banking encompasses several aspects, such as financial markets, corporate credit, and trade, involving substantial transactions and monetary resources. Consequently, this sector has a favorable opportunity to use emerging information technology (IT) advancements. Lastly, the study examines how BDA contributes to FinTech difficulties and projects how FinTech will develop in the future within the context of BDA. © 2024 IEEE.Item Navigating Data Imbalances in Credit Risk Management: A One-Sided Selection Approach(Institute of Electrical and Electronics Engineers Inc., 2024) Bennehalli, S.J.; Vakkund, S.; Anusha Hegde, H.; Bhowmik, B.Credit scoring plays a vital role in mitigating the information asymmetry that is pervasive on platforms for peer-to-peer (P2P) lending. A considerable challenge stems from the disparity in loan repayment outcomes: a significant minority of loan applicants defaulting on their loans, while the majority fulfilling their repayment obligations. The presence of imbalance in the dataset has the potential to incorporate bias into predictive model, which could lower its performance. In order to address this issue, data balancing techniques are often employed to enhance the performance of credit scoring models through the generation of datasets that are more balanced. This work constructs a robust credit scoring model capable of precisely assessing the creditworthiness of individuals seeking P2P lending. Four distinct classifiers - Logistic Regression, Random Forest, LightGBM, and Support Vector Machine (SVM) are employed. In doing so, it effectively mitigates the distortions that can result from unbalanced data distributions. This work achieves data balance with One-Sided Selection methodology along with Information gain and Pearson correlation which mainly determine the features to include. The proposed model thus works on both balanced and unbalanced datasets. Experimental results show that the standard metrics like accuracy, precision, recall, and F1-Score achieves upto 90.41%, 89.51%, 90.40%, and 89.96%, respectively. © 2024 IEEE.Item Enhancing Big Data Security Through Anomaly Detection(Institute of Electrical and Electronics Engineers Inc., 2024) Vakkund, S.; Kumar, S.; Rao, S.; Anusha Hegde, H.; Bhowmik, B.Securing the massive and fast-moving data streams typical in Big Data environments presents unique challenges that traditional static security measures simply can't handle. To effectively protect these data flows, we need methods that can analyze traffic in real-time and respond swiftly to potential threats. Anomaly detection is one such method, offering an automated way to identify unusual or suspicious activities within Big Data systems. In this study, we explore several widely-used anomaly detection algorithms, evaluating their effectiveness in identifying anomalies within large datasets. Specifically, we will assess these algorithms using the UNSW-NB15 Dataset, aiming to pinpoint which algorithm, or combination of algorithms, is best suited for the demands of Big Data security. © 2024 IEEE.Item Optimizing Lender Portfolios: A P2P Lending Recommendation Approach(Institute of Electrical and Electronics Engineers Inc., 2024) Sannapareddy, V.; Rifah, U.; Anusha Hegde, H.; Bhowmik, B.The proliferation of peer-to-peer (P2P) lending platforms has ushered in a new era of financial accessibility, but it has also brought to the forefront the growing concern of loan defaults. This paper explores the increasing significance of P2P lending platforms and addresses the critical issue of loan default prediction. The study focuses on the application of machine learning techniques, specifically employing the Random Forest algorithm and logistic regression, to train a predictive model for assessing the likelihood of default within a loan portfolio. The primary objective is to enhance the decision-making process for lenders by recommending optimal loan portfolios based on the predictive insights generated by the model. By leveraging the capabilities of this robust algorithm, the research aims to contribute to the advancement of risk assessment methodologies in P2P lending, ultimately fostering more informed and secure lending practices on these platforms. We trained and compared logistic Reression and random forest models and derived resultant optimal portfolio by considering both the models which is intended to give better results than a single model. © 2024 IEEE.