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Integrating Financial Analytics and Forensic Techniques in Enhancing Corporate Decision-Making and Fraud Detection
Subject area: Science,Engineering and Technology · Area of research: Finance
Abstract
The integration of financial analytics and forensic financial statement analysis has become increasingly critical in shaping sound corporate decision-making and detecting financial fraud. As organizations adopt advanced data-driven models and predictive tools, they gain enhanced capabilities to assess risk, allocate resources, and devise strategic financial plans. Simultaneously, financial statement analysis?incorporating forensic accounting techniques and red flag indicators such as the Beneish M-Score and Altman Z-Score?remains essential in identifying earnings manipulation and financial misrepresentation. This paper explores how combining financial analytics with robust fraud detection methodologies not only improves financial performance but also strengthens corporate governance and transparency. Through an examination of case studies ranging from AI-driven risk models in banking to high-profile fraud cases like Enron and Wirecard, the study demonstrates the dual role of analytics in fostering both strategic growth and fraud resistance. Ultimately, the convergence of these practices is shown to support more resilient, accountable, and data-informed financial ecosystems
Keywords
Financial Analytics, Corporate Finance, Predictive Modeling, Risk Assessment, Machine Learning, Financial Forecasting, Business Intelligence, Financial Statement Analysis, Forensic Accounting, Earnings Manipulation, Corporate Fraud, Fraud Detection Strategies.
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How to cite this paper
@article{1708418,
author = {Zainab Folakemi Azeez},
title = {Integrating Financial Analytics and Forensic Techniques in Enhancing Corporate Decision-Making and Fraud Detection},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {11},
pages = {788-811},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708418.pdf},
abstract = {The integration of financial analytics and forensic financial statement analysis has become increasingly critical in shaping sound corporate decision-making and detecting financial fraud. As organizations adopt advanced data-driven models and predictive tools, they gain enhanced capabilities to assess risk, allocate resources, and devise strategic financial plans. Simultaneously, financial statement analysis?incorporating forensic accounting techniques and red flag indicators such as the Beneish M-Score and Altman Z-Score?remains essential in identifying earnings manipulation and financial misrepresentation. This paper explores how combining financial analytics with robust fraud detection methodologies not only improves financial performance but also strengthens corporate governance and transparency. Through an examination of case studies ranging from AI-driven risk models in banking to high-profile fraud cases like Enron and Wirecard, the study demonstrates the dual role of analytics in fostering both strategic growth and fraud resistance. Ultimately, the convergence of these practices is shown to support more resilient, accountable, and data-informed financial ecosystems},
keywords = {Financial Analytics, Corporate Finance, Predictive Modeling, Risk Assessment, Machine Learning, Financial Forecasting, Business Intelligence, Financial Statement Analysis, Forensic Accounting, Earnings Manipulation, Corporate Fraud, Fraud Detection Strategies.},
month = {May},
}