Conference Papers

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

Browse

Filters

2022 - 20242

Settings

Subject: search.filters.subject.Banking

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    Money Laundering Detection in Banking Transactions using RNNs and Hybrid Ensemble
    (Institute of Electrical and Electronics Engineers Inc., 2024) Girish, K.K.; Bhowmik, B.
    The financial sector has witnessed significant transformations due to the emergence of financial technology (FinTech), transitioning from traditional paperbased processes to a dynamic digital ecosystem. Despite the industry's advancements driven by FinTech innovations, concerns persist, particularly regarding financial fraud, notably money laundering. Perpetrators exploit modern technologies to launder illicitly obtained funds, posing a global threat to economies. Effective detection mechanisms for money laundering are crucial. This paper introduces a novel approach utilizing a recurrent neural network (RNN) for detecting money laundering in banking transactions. The proposed framework exercises standalone RNN models such as LSTM, GRU, BiLSTM, and stacked RNN models for the detection. Additionally, the effectiveness of hybrid ensemble models combining RNNs with XGBoosts is investigated. The evaluation achieves standard performance metrics, with the stacked RNN model achieving 92% accuracy. Surpassing it, the ensemble model achieves an impressive 95%. These results underscore the superiority of hybrid ensemble models over standalone RNNs, particularly in accurately detecting money laundering activities. © 2024 IEEE.