Arun Kumar Gharami
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View article: A Deep Learning Framework for Detecting Fraudulent Accounting Practices in Financial Institutions
A Deep Learning Framework for Detecting Fraudulent Accounting Practices in Financial Institutions Open
Fraudulent accounting practices pose a significant threat to the stability and integrity of banking systems, leading to financial losses, reputational damage, and systemic risks. This study proposes a deep learning-based framework for dete…
View article: Deep Learning-Driven Customer Segmentation in Banking: A Comparative Analysis for Real-Time Decision Support
Deep Learning-Driven Customer Segmentation in Banking: A Comparative Analysis for Real-Time Decision Support Open
In this study, we investigate the effectiveness of various deep learning algorithms for customer segmentation in the banking sector, aiming to enhance targeted service delivery and customer experience. We employ a comprehensive pipeline en…
View article: AI-Driven Demand Forecasting for Multi-Echelon Supply Chains: Enhancing Forecasting Accuracy and Operational Efficiency through Machine Learning and Deep Learning Techniques.
AI-Driven Demand Forecasting for Multi-Echelon Supply Chains: Enhancing Forecasting Accuracy and Operational Efficiency through Machine Learning and Deep Learning Techniques. Open
Demand forecasting plays a crucial role in optimizing supply chain operations, particularly in multi-echelon supply chains where goods move through various stages, including manufacturers, wholesalers, and retailers. Traditional time-serie…
View article: Forecasting Bank Profitability Using Deep Learning and Macroeconomic Indicators: A Comparative Model Study
Forecasting Bank Profitability Using Deep Learning and Macroeconomic Indicators: A Comparative Model Study Open
This study proposes a deep learning framework for forecasting bank profitability by integrating macroeconomic indicators with firm-level financial data. Using models such as LSTM, GRU, Transformer, and TCN—compared against traditional appr…
View article: AI-Powered Sentiment Analytics in Banking: A BERT and LSTM Perspective.
AI-Powered Sentiment Analytics in Banking: A BERT and LSTM Perspective. Open
In recent years, the banking industry has witnessed a surge in digital feedback channels, where customers regularly share their experiences and opinions. Extracting meaningful insights from this unstructured data is vital for enhancing cus…
View article: Integrating Consumer Sentiment and Deep Learning for GDP Forecasting: A Novel Approach in Financial Industry.
Integrating Consumer Sentiment and Deep Learning for GDP Forecasting: A Novel Approach in Financial Industry. Open
Accurate forecasting of Gross Domestic Product (GDP) is essential for informed policy-making and strategic economic planning. This paper proposes a hybrid deep learning model that integrates consumer sentiment data with traditional economi…
View article: Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems
Deep Learning for Real-Time Fraud Detection: Enhancing Credit Card Security in Banking Systems Open
In this study, we present a deep learning-based approach for real-time credit card fraud detection in banking systems, with a primary focus on Long Short-Term Memory (LSTM) networks. Using a highly imbalanced credit card transaction datase…