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All Journal International Journal of Multidisciplinary Sciences and Arts BULLET : Jurnal Multidisiplin Ilmu
Ghimire, Ashok
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Humanities Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

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Harnessing Big Data with AI-Driven BI Systems for Real-Time Fraud Detection in the U.S. Banking Sector Ghimire, Ashok
BULLET : Jurnal Multidisiplin Ilmu Vol. 3 No. 6 (2024): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

AI BI systems with integration to Big Data is going to help change the face of the detect fraud for banking sector in United States. This paper examines how these technologies make it possible to detect fraudulent activities in real-time: the novel being that mega yards of transactional data may have to be ingested and analyzed in near real-time to make way for machine learning, predictive models, and/or AI. Banks are on the receiving end of those more advanced techniques and with the use of AI and Big Data there is capacity to analyze of those fraud patterns, improve accuracy and eventually diminish the made losses. However, the actual application of these systems has its drawbacks: concerns for data protection, having algorithms with certain biases, a history of the corresponding system being meddled with and needing to be updated. The present work aims to consider a few examples of applying the AI solutions in practice to investigate actual and pilot cases of frauds in the big US banks, such as JPMorgan Chase, Bank of America, and Wells Forgot. It also includes an emergence of fraud detection systems which in form of block chain technology, enhanced biometric science, quantum technology and shared fraud detection platform. However, all these technologies are seen to offer a great potential for enhancing the security level of the banking sector, especially as regards the prevention of fraud activities in the field. These are the goal posts which financial institutions have to clear while adopting change, controlling frauds to combat new techniques in an environment that moves towards an online financial services consumer’s environment.
Predictive models performance in financial services for identifying at-risk customers Ghimire, Ashok
International Journal of Multidisciplinary Sciences and Arts Vol. 3 No. 4 (2024): International Journal of Multidisciplinary Sciences and Arts, Article October 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v3i4.5161

Abstract

It is therefore essential in financial service where the concept of pre-emptive modeling is used to select customers that are most likely to engage in negative behaviors like loan defaulting, fraud or churn. This paper will seek to analyze different modeling techniques like Logistic regression, Decision trees, Random forests, Gradient boosting, and Neural network among others. The paper pays considerable attention to the data preprocessing step; problems such as imbalance ratio and missing values as well as important parameters such as precision, recall, and AUC-ROC to measure the efficiency of the models. Some of these problems that have been highlighted in the review include; data privacy, compliance to laws and regulations among others and model interpretability which plays an important role in the financial industry. This means that new achievements like XAI, Real-time analytics, Federated learning have contributed to the improvement of model interpretability, model size and data security. Such include technological applications to increase accuracy of Risk prediction while cutting costs, and help the financial institutions retain the trust of the customer given the ever changing regulation. By integrating these novelties and ethical issues, some more financial institutions should enhance the centrality of the presented predictions to improve the identification of the risks and customers’ appeal.
Predictive models performance in financial services for identifying at-risk customers Ghimire, Ashok
International Journal of Multidisciplinary Sciences and Arts Vol. 3 No. 4 (2024): International Journal of Multidisciplinary Sciences and Arts, Article October 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v3i4.5161

Abstract

It is therefore essential in financial service where the concept of pre-emptive modeling is used to select customers that are most likely to engage in negative behaviors like loan defaulting, fraud or churn. This paper will seek to analyze different modeling techniques like Logistic regression, Decision trees, Random forests, Gradient boosting, and Neural network among others. The paper pays considerable attention to the data preprocessing step; problems such as imbalance ratio and missing values as well as important parameters such as precision, recall, and AUC-ROC to measure the efficiency of the models. Some of these problems that have been highlighted in the review include; data privacy, compliance to laws and regulations among others and model interpretability which plays an important role in the financial industry. This means that new achievements like XAI, Real-time analytics, Federated learning have contributed to the improvement of model interpretability, model size and data security. Such include technological applications to increase accuracy of Risk prediction while cutting costs, and help the financial institutions retain the trust of the customer given the ever changing regulation. By integrating these novelties and ethical issues, some more financial institutions should enhance the centrality of the presented predictions to improve the identification of the risks and customers’ appeal.
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