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All Journal Indonesian Economic Review Hikamatzu Journal of Multidisciplinary Research
Maureen Cahayli
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Religion Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Library & Information Science

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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 Kus Larisa Nathania Zita; Maureen Cahayli; Najwa Salsabila Azzahra; Fatkhur Rohman
Indonesian Economic Review Vol. 6 No. 1 (2026): February : Indonesian Economic Review
Publisher : Cahaya Abadi Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53787/iconev.v6i1.105

Abstract

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.
Artificial Intelligence for Greenwashing Detection: A Conceptual Analysis of NLP and LLM in Sustainability Reporting Mohammad Mostaf Fauzil Mufti; Tiara Rizky Cahya; Zahwa Nura Aziza; Khristina Putri Kasihwigati; Maureen Cahayli; Dina Safitri; Diajeng Fitri Wulan
Hikamatzu | Journal of Multidisciplinary Vol. 3 No. 1 (2026): Multidisciplinary Approach
Publisher : Hikamatzu | Journal of Multidisciplinary

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Abstract

Greenwashing, the practice of making misleading environmental claims, continues to hinder genuine progress toward sustainable development. Studies show that a significant proportion of corporate sustainability claims are exaggerated or unfounded, creating a demand for effective tools to identify such practices. Traditional methods of detecting greenwashing, such as manual reviews and basic keyword analysis, are often insufficient due to the complexity and volume of data involved. This study uses a conceptual and analytical research design to summarize existing evidence on the use of Artificial Intelligence (AI), including Natural Language Processing (NLP) and Large Language Models (LLMs), in detecting greenwashing. By analyzing sustainability reports, press releases, and social media content, these AI tools offer a more efficient and accurate approach to identifying discrepancies between corporate claims and actual practices. The findings demonstrate that AI technologies can significantly advance greenwashing detection, contributing to more reliable and accessible sustainability assessments. However, limitations remain, as the study focuses on only two AI methodologies. Future research should explore a wider range of AI tools and techniques to address industry-specific challenges and regulatory concerns, ensuring a more comprehensive approach to detecting greenwashing in corporate practices.
Co-Authors Diajeng Fitri Wulan Dina Safitri Khristina Putri Kasihwigati Kus Larisa Nathania Zita Mohammad Mostaf Fauzil Mufti Najwa Salsabila Azzahra Rohman, Fatkhur Tiara Rizky Cahya Zahwa Nura Aziza