Conference Papers

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    Analyzing Banking Services Applicability Using Explainable Artificial Intelligence
    (Association for Computing Machinery, 2022) Sriram, A.; Gorti, S.S.; Amin, E.G.; Anand Kumar, M.A.
    Over the last few years, the banking sector has had a pivotal role to play in the global economy, comprising of about 24% of the global GDP and employing millions of people worldwide. Banks have a wide array of products and services to offer, ranging from ATMs, Tele-Banking, Credit Cards, Debit cards, Electronic Fund Transfers (EFT), Internet Banking, Mobile Banking, etc. Machine learning is a method of data analysis that automates analytical model building and can be an essential decision support tool for banks in providing services to certain customers and to help in improving customer satisfaction and experience based on collected data. In this study, we made use of several machine learning models and Artificial Neural Networks (ANN) to help banks make predictions about timely customer loan repayment and customer satisfaction. We explored different machine learning algorithms and have performed SHAP analysis, which has helped make conclusions about the significant features driving these decisions. © 2022 ACM.
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    Exploring the Impact of External Factors on Ride-Hailing Demand: A Predictive Modelling Approach
    (The Society for the Study of Artificial Intelligence and Simulation of Behaviour, 2023) Sriram, A.; Ananthanarayana, V.S.
    This paper presents a comprehensive study on the usage of Uber in different markets, with a focus on understanding the impact of demographic factors, public transit proximity, weather and extreme events on the demand for Uber ride-hailing services. This study involves application of Explainable AI techniques for feature selection among multiple data sources to model external factors on the Uber ride usage. Furthermore, factors such as weather and local events are used for ride usage forecasting using spatiotemporal aspects and extreme event analysis. The results of this study showed that certain factors like demography, proximity of public transit play a role in shaping the usage patterns of Uber. Also, extreme events, such as weather conditions and local events, were found to have a significant impact on the demand for Uber services. This study provides valuable insights for Uber, similar ride-hailing services and policymakers for optimal resource allocation, and lays the foundation for further research on the relationship between transportation services and various contextual factors. © AISB Convention 2023.All rights reserved.
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    A Comprehensive Analysis of Classification Techniques for Effective Multi-class Research Article Categorization on an Imbalanced Dataset
    (Springer Science and Business Media Deutschland GmbH, 2025) Gowhar, S.; Kempaiah, P.; Sowmya Kamath, S.; Sugumaran, V.
    Categorizing scientific articles into specific research fields is a challenging problem, affected by the volume and variety of literature published. However, existing classification systems often suffer from limitations regarding taxonomy or the models used for classification. This article explores a comprehensive analysis of approaches built on Sentence Transformer embeddings combined with Machine Learning algorithms, Neural Networks, and Transformers to classify articles into 123 predefined classes, with the dataset being heavily imbalanced. The effectiveness of Large Language Models (LLMs) for generating synthetic data is also experimented with, along with synonym augmentation SMOTE and employing 1D CNNs for text classification. The best-performing model is a hierarchical classification model trained on MP-Net sentence embeddings that achieved an accuracy of 78%, outperforming all other models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
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    Enhanced Default Risk Assessment: The Integration of Outlier Detection, Borrower Network Similarity, and Explainable AI
    (Springer Science and Business Media Deutschland GmbH, 2025) Hegde, A.; Bhowmik, B.
    Evaluating credit risk in peer-to-peer (P2P) lending platforms is crucial due to the absence of information typically accessible through conventional banking channels. The financial system’s integrity may be compromised if default risk is not accurately assessed. This research proposes an Outlier Detection with Borrower Network Similarity (ODBN) framework for enhancing the accuracy of credit risk assessment on P2P platforms. To achieve this goal, we suggest incorporating alternative data into conventional credit assessment methodologies. Initially, an unsupervised learning approach is used to differentiate between borrowers who exhibit unconventional behavior and those that follow regular borrowing patterns. The borrower network centrality measures are computed for these two categories of borrowers to provide alternative data. The predictive power of the similarities found in the regular borrower cluster samples is observed to be higher than that of the eccentric borrower cluster. To gain deeper insights into the results, the Shapley values are visualized as a network. The empirical findings on the Lending Club dataset suggest that the ODBN improves the model’s ability to explain and forecast with more precision. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.