Artificial Intelligence on Performance Optimization and Prediction in E-Business

V.Prema Kumari; S.Antony Raj

doi:https://doie.org/10.1224/BMEE.2024446680

DOI: https://doie.org/10.1224/BMEE.2024446680

Abstract

Purpose: The purpose of this study is to enhance forecasting accuracy and optimize the performance of Payment Service Providers (PSPs) in the e-commerce sector. By integrating Management Information Systems (MIS) with advanced AI-driven time-series models such as ARIMA, SARIMA, and LSTM, this research aims to improve transaction volume forecasts and key performance indicators, ultimately contributing to better decision-making and operational efficiency.
Methodology: This study employs a quantitative research methodology using historical transaction data to develop and test forecasting models. The approach integrates ARIMA, SARIMA, and LSTM models for comparative analysis. AI algorithms, particularly LSTM networks, are utilized for their ability to capture complex, non-linear dependencies in time-series data. Additionally, MIS is employed to systematically gather, process, and analyze data, providing real-time insights for decision-making. Sensitivity analysis is conducted to assess the robustness and adaptability of the AI-driven LSTM model in various scenarios.
Findings: The analysis reveals that AI-powered LSTM outperforms ARIMA and SARIMA, achieving a Mean Absolute Percentage Error (MAPE) of 2.9%, compared to 5.1% and 4.8% for ARIMA and SARIMA, respectively. The integration of MIS contributes to a 5.7% increase in approval rates and a reduction in business and technical declines by 2.5% and 2.0%, respectively. These findings demonstrate that leveraging AI-driven LSTM models combined with MIS enhances forecasting accuracy and operational efficiency, leading to optimized PSP performance in e-commerce.
Originality: This study is original in its approach by integrating AI-driven time-series forecasting models, MIS, and predictive analytics to create a comprehensive framework for PSP optimization in e-commerce. While previous research has explored these components individually, this paper is one of the first to combine them in an integrated manner for a holistic impact on e-commerce transaction management.
Research limitations: The main limitation of this study is the reliance on historical transaction data from a specific e-commerce context, which may not fully represent all market conditions. Future research could expand to include a variety of data sources and apply the model to different e-commerce sectors to generalize findings.
Practical implications: Practically, the study provides e-commerce managers and PSPs with actionable insights on utilizing advanced AI-driven forecasting models and MIS to make data-driven decisions that improve transaction approval rates and reduce declines. This approach can lead to better resource allocation and operational planning.
Social implications: The improvements in transaction volume forecasting and operational efficiency, driven by AI, could contribute to a more reliable and seamless online shopping experience for consumers, enhancing trust and engagement in digital payment systems.

Keyword : E-commerce, Transaction volume forecasting, PSP Optimization, Predictive analytics, Management Information Systems, Artificial Intelligence, Time-series forecasting, LSTM, ARIMA, SARIMA, Decision-making, Operational efficiency.,

How to Cite

Kumari, V. & Raj, S. (1970). Artificial Intelligence on Performance Optimization and Prediction in E-Business. Business, Management and Economics Engineering, 22(1), 136-154. https://doie.org/10.1224/BMEE.2024446680

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Jan 01, 1970

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