Journal of Ai ML DL
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Journal of Ai ML DL

Volume 1 Number 1 2025

Article Contents

RESEARCH ARTICLE   (Open Access)
Contents Vol 1 (1)

Enhancing Data Reliability in Management Information Systems through Artificial Intelligence Driven Validation and Error Detection Models

Md Sharfuddin1*, Proggo Choudhury2

+ Author Affiliations

Journal of Ai ML DL 1 (1) 1-8 https://doi.org/10.25163/ai.1110442

Submitted: 18 March 2025 Revised: 04 April 2025  Published: 06 May 2025 

Abstract

Background: Management Information Systems (MIS) are essential for organizational decision-making by delivering timely and accurate information. However, data reliability remains a major challenge due to human errors, inconsistent data entry, and integration of heterogeneous sources. The wrong information leads to poor decision-making which results in both operational problems and financial damage. The integration of Artificial Intelligence (AI) into MIS systems provides an automated solution which performs validation and detects anomalies and predicts potential errors.

Methods: The research created AI validation and error detection models through financial and HR and operational historical data. The data processing stage involved three main steps which consisted of data cleansing and normalization and missing value handling. The research used Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) machine learning algorithms to find anomalies and confirm data accuracy.

Results: AI integration significantly improved data reliability compared to traditional methods. The system operated at its highest precision to find errors through RF but the performance of ANN excelled when detecting complex anomalies. The implementation of AI technology resulted in a 32% decrease of undetected errors and a 28% reduction of duplicate entries which resulted in improved data consistency between departments that supported better decision-making.

Conclusion: AI-driven validation systems in MIS operations create superior data quality standards which stop human errors from happening while making better use of available resources.

Keywords: Artificial Intelligence, Management Information Systems, Data Reliability, Error Detection, Validation

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