Title: Intelligent Auditing of Financial Transactions Using AI: Toward a Strengthened Fight Against Fraud and Money Laundering in the DRC
Authors: Prof. KALOMBO Masimango Monique-Stéphanie, PhD.
Volume: 9
Issue: 10
Pages: 10-22
Publication Date: 2025/10/28
Abstract:
: The present study examines the use of artificial intelligence (AI) in financial auditing in the Democratic Republic of Congo to enhance the detection of fraud and money laundering. The main objective is to demonstrate how intelligent auditing could improve the transparency and reliability of financial transactions, while specific objectives included identifying prevalent fraud types, analyzing the limitations of traditional audits, and proposing a practical framework for the DRC. The methodology combined descriptive and analytical approaches, with data collected from the Central Bank, CENAREF, and local banks, alongside a pilot experiment using a supervised machine-learning model (Random Forest) on six months of anonymized transactions. Results show that intelligent auditing increases fraud detection rates (from 8% to 36%), reduces detection time (from 72 hours to 6 hours), and decreases financial losses by approximately 40%, while enhancing AML/CFT compliance. The study confirms the initial hypotheses, highlights the potential of complementary technologies such as blockchain audit trails and real-time compliance monitoring, and suggests future research on the cybersecurity of intelligent auditing systems.