Revolutionizing Banking Decision-Making: A Deep Learning Approach to Predicting Customer Behavior

Journal of Business and Management Studies, Год журнала: 2024, Номер 6(3), С. 21 - 27

Опубликована: Май 7, 2024

This article explores a machine learning approach focused on predicting bank customer behavior, emphasizing deep methods. Various architectures, including CNNs like VGG16, ResNet50, and InceptionV3, are compared with traditional algorithms such as Random Forest SVM. Results show models, particularly outperform ones, an accuracy of 86.66%. A structured methodology ensures ethical data use. Investing in infrastructure expertise is crucial for successful integration, offering competitive edge banking decision-making.

Язык: Английский

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Deep Learning for Enterprise Decision-Making: A Comprehensive Study in Stock Market Analytics

Journal of Business and Management Studies, Год журнала: 2024, Номер 6(2), С. 153 - 160

Опубликована: Апрель 20, 2024

This study explores the transformative impact of deep learning, specifically Convolutional Neural Networks (CNNs), on organizational decision-making in stock market. Utilizing CNN architectures like VGG16, ResNet50, and InceptionV3, research emphasizes significance leveraging learning for improved business intelligence management. It highlights superiority models over traditional algorithms, with VGG16 achieving an accuracy rate 90.45%. The underscores potential extracting valuable insights from complex data, leading to a shift optimizing processes. Additionally, it stresses importance investing infrastructure expertise successful integration, alongside addressing ethical privacy concerns. Through dive into real-time mathematical concepts, provides functionality offers comparisons between different architectures, aiding specialized applications such as market trends.

Язык: Английский

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Advancements in Retail Price Optimization: Leveraging Machine Learning Models for Profitability and Competitiveness

Journal of Business and Management Studies, Год журнала: 2024, Номер 6(3), С. 103 - 110

Опубликована: Май 23, 2024

Retail price optimization is essential for maximizing profitability and maintaining competitiveness in today's dynamic retail landscape. This study addresses as a regression problem, utilizing machine learning models to predict optimal points products. Leveraging factors such product attributes, competitor pricing dynamics, customer behaviors, analysis provides structured approach understanding the intricate relationships between variables. Among various techniques, Random Forest Regressor emerges potent strategy, offering robustness versatility tackling complex tasks. Results indicate that outperforms Decision Tree Logistic Regression regarding accuracy, precision, recall, overall predictive performance. With achieving an accuracy of 94%, it demonstrates superior capability capturing data patterns making accurate predictions prices. By leveraging advanced analytics retailers can optimize strategies, maximize profits, maintain market. underscores importance continuously analyzing refining strategies gain competitive edge

Язык: Английский

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Transformative Impact of Deep Learning in Stock Market Decision-Making: A Comparative Study of Convolutional Neural Networks

Journal of Business and Management Studies, Год журнала: 2024, Номер 6(3), С. 28 - 34

Опубликована: Май 7, 2024

This research delves into the transformative impact of deep learning, specifically Convolutional Neural Networks (CNNs) such as VGG16, ResNet50, and InceptionV3, on organizational management business intelligence. The study follows a comprehensive methodology, emphasizing importance high-quality datasets in leveraging learning for enhanced decision-making. Results demonstrate superior performance CNN models over traditional algorithms, with (VGG19) achieving an accuracy rate 89.45%. findings underscore potential extracting meaningful insights from complex data, offering paradigm shift optimizing various processes. article concludes by significance investing infrastructure expertise successful integration, ensuring ethical considerations, addressing data privacy concerns. contributes to growing discourse application management, providing valuable resource businesses navigating dynamic landscape global market.

Язык: Английский

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Machine Learning Model in Digital Marketing Strategies for Customer Behavior: Harnessing CNNs for Enhanced Customer Satisfaction and Strategic Decision-Making

Journal of Economics Finance and Accounting Studies, Год журнала: 2024, Номер 6(3), С. 178 - 186

Опубликована: Июнь 22, 2024

In the realm of digital marketing for banking industry, integration deep learning methodologies, particularly Convolutional Neural Networks (CNNs) such as VGG16, Resnet50, and InceptionV3, has revolutionized strategic decision-making customer satisfaction. This study explores how models leverage neural networks with multiple layers to analyze vast complex datasets, uncovering intricate patterns in behavior preferences. By enhancing segmentation, optimizing campaign performance, refining personalized experiences, CNNs empower banks make precise, data-driven decisions that elevate satisfaction loyalty. Comparative analyses demonstrate CNNs' superior performance over traditional like Random Forest Logistic Regression, achieving accuracies up 89% F1 scores 88%, thereby highlighting their transformative potential reshaping strategies within sector. research underscores critical implications adopting advanced techniques meet evolving demands customers today's dynamic landscape.

Язык: Английский

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Enhancing Credit Card Fraud Detection: A Comprehensive Study of Machine Learning Algorithms and Performance Evaluation

SSRN Electronic Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Revolutionizing Organizational Decision-Making for Banking Sector: A Machine Learning Approach with CNNs in Business Intelligence and Management

Journal of Business and Management Studies, Год журнала: 2024, Номер 6(3), С. 111 - 118

Опубликована: Май 23, 2024

This research investigates the transformative impact of deep learning, particularly Convolutional Neural Networks (CNNs) such as VGG16, ResNet50, and InceptionV3, on organizational management business intelligence within banking sector. Employing a comprehensive methodology, study emphasizes crucial role high-quality datasets in harnessing learning for improved decision-making. Results reveal superior performance CNN models over traditional algorithms, with (VGG16) achieving an impressive accuracy rate 90%. These findings underscore potential extracting valuable insights from complex data, presenting paradigm shift optimizing various processes. The article concludes by highlighting importance investing infrastructure expertise successful integration, while also addressing ethical privacy considerations. contributes to evolving discourse applications management, offering banks navigating challenges global market.

Язык: Английский

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Enhancing Credit Card Fraud Detection: A Comprehensive Study of Machine Learning Algorithms and Performance Evaluation

Journal of Business and Management Studies, Год журнала: 2024, Номер 6(3), С. 252 - 259

Опубликована: Июнь 13, 2024

Credit card fraud detection remains a significant challenge for financial institutions and consumers globally, prompting the adoption of advanced data analytics machine learning techniques. In this study, we investigate methodology performance evaluation various algorithms credit detection, emphasizing preprocessing techniques model effectiveness. Through thorough dataset analysis experimentation using cross-validation approaches, assess logistic regression, decision trees, random forest classifiers, Naïve Bayes K-nearest neighbors (KNN), artificial neural networks (ANN-DL). Key metrics such as accuracy, sensitivity, specificity, F1-score are compared to identify most effective models detecting fraudulent transactions. Additionally, explore impact different folds in on performance, providing insights into classifiers' robustness stability. Our findings contribute ongoing efforts develop efficient systems, offering valuable researchers striving combat effectively.

Язык: Английский

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Product Demand Forecasting with Neural Networks and Macroeconomic Indicators: A Comparative Study among Product Categories

Journal of Business and Management Studies, Год журнала: 2024, Номер 6(2), С. 170 - 175

Опубликована: Апрель 23, 2024

In the fiercely competitive global corporate arena, intricacies of demand forecasting in retail sector have become a focal point. While previous research has delved into various methodologies, it consistently overlooks distinct performances models within different product categories. Understanding these variations prediction is pivotal, enabling firms to fine-tune for each category. This study bridges this gap by scrutinizing tailored Building on recent research, we incorporate external macroeconomic indicators like Consumer Price Index, Sentiment and unemployment rate, alongside time series data sales spanning amalgamated dataset employed train Long Short Term Memory model, projecting future across We further extend analysis identifying features that contribute most towards explaining quantifying their strength. The fitted yield comprehensive insights pinpoint categories warranting more focused model development.

Язык: Английский

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Maximizing Sales and Customer Satisfaction in E-Commerce Through Ai-Driven Techniques

Опубликована: Янв. 1, 2024

Язык: Английский

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