Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Anusha Hegde, H.Subject: search.filters.subject.FinTech

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    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.
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    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.
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    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.