Anti-Fraud and Anti-Money Laundering: Proactive Detection Using Artificial Neural Networks with the Fraud Hexagon Approach to Strengthen the Stability of Indonesia’s Financial Ecosystem
DOI:
https://doi.org/10.53787/iconev.v6i1.105Keywords:
Artificial Neural Network, Financial, Fraud, Money Laundering, RegulationAbstract
Fraud often serves as the initial stage before progressing to money laundering, making it a crucial global financial issue. Such fraudulent practices continue to occur in Indonesia, as reflected in the country’s Corruption Perception Index score of 34/100 in 2022. This concern is further supported by a 2023 report from PPATK, which revealed more than 51,000 suspicious transactions with potential links to money laundering crimes (TPPU). In contrast, developed countries such as Singapore have managed to address fraud and money laundering by leveraging technological innovations, particularly detection tools based on artificial intelligence such as Artificial Neural Networks (ANN). The Association of Certified Fraud Examiners (ACFE) has also highlighted that 40% of fraud and money laundering cases stem from weak proactive detection by internal parties and regulators.The use of proactive detection technologies like ANN enables financial data to be updated in real time and suspicious patterns to be identified at an early stage. The primary advantage of ANN lies in its ability to detect suspicious transactions that conventional methods fail to recognize, thereby reducing the likelihood of fraud before it escalates into money laundering. Given the proven effectiveness and superiority of this detection system, it is recommended that the Financial Services Authority (OJK) enforce regulations requiring companies with annual revenues of ≥ Rp50 billion to adopt ANN-based mechanisms as an initial step to weaken fraud and prevent money laundering. This measure is expected to foster sustainable stability within Indonesia’s financial ecosystem.
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