Forensic Accounting in the Digital Era: Detecting and Preventing Financial Crime in High-Growth Start-Ups

Abstract

Objective: This study aims to evaluate the role and effectiveness of modern forensic accounting technology—particularly that based on artificial intelligence (AI) and data analytics—in detecting and preventing financial crime (fraud) in rapidly growing digital start-up companies.
Research Design & Methods: This study uses a descriptive qualitative approach and case studies. The analysis technique is conducted through content analysis to identify patterns of internal control weaknesses and the use of forensic tools in financial reporting systems.
Findings: The results of the study show that fraud in start-ups is largely triggered by weak internal control systems, the lack of separation between personal and company funds, and the non-involvement of digital audit technology. Transaction anomaly patterns such as undocumented fund transfers, GMV manipulation, and project documentation engineering were found to be key indicators of fraud. Conversely, the implementation of digital forensic tools such as IDEA, FTK Imager, Tableau TD3, and machine learning algorithms has proven effective in detecting anomalies early on and strengthening oversight systems.
Implications and Recommendations: This research's implications include the urgent need for start-ups to integrate digital audits into their financial systems from the outset. Regulators need to formulate more adaptive digital oversight policies, while investors and venture capitalists are advised to make forensic audits part of their due diligence process before funding.
Contribution & Value Added: This study provides theoretical and practical contributions to developing technology-based forensic accounting for the digital start-up sector. This research expands the literature on preventing financial crime in the digital age.