[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117855-en":3,"doc-seo-117855-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117855,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Utilization of Machine Learning to Detect the Possibility of Suspicious Financial Transactions","Money laundering and terrorism financing pose urgent risks to banks and financial systems, making effective detection of suspicious financial transactions (SFT) essential for regulatory compliance and operational stability. This quantitative study evaluates supervised machine learning approaches to identify the likelihood of SFT in Bank XYZ. Three models are compared—Decision Tree, Gradient Boosting, and Random Forest—implemented using KNIME. Results show Random Forest achieves the highest accuracy at 99.98%, indicating that historical company data with machine learning can substantially strengthen SFT detection performance.","Utilization of machine learning to detect the possibility of suspicious financial transactions  \nDina Anggraeni1, Siti Nurwahyuningsih Harahap2  \n1,2 University of Indonesia  \n[dina.anggraeni@ui.ac.id](dina.anggraeni@ui.ac.id)  \nArticle Info ABSTRACT  \n\n| Article history:\u003Cbr>Received Jan 12th 2023 Revised Feb 18th 2023 Accepted Mar 4th 2023 | In order to prevent money laundering and terrorism financing, it is critical for banks to develop an effective mechanism to detect suspicious transactions. Nowadays, oneof the most widely developed methods is machine learning. This article aims to discuss the best algorithm model in machine learning to detect possibilities for Suspicious Financial Transactions in XYZ Bank. This research uses quantitative research methods. The machine learning method used is supervised machine learning, with three models compared: Decision Tree, Gradient Boosting, and Random Forest. The tool used is The Konstanz Information Miner (KNIME) . According to the findings of the study, the best model for detecting the possibility of SFT in bank XYZ is random forest with an accuracy rate of 99,98% . Based on this level of accuracy, this study reveals that a machine learning approach using historical company data makes a significant contribution to XYZ bank in detecting Suspicious Financial Transactions. |  |\n| --- | --- | --- |\n| Keyword:\u003Cbr>KNIME; Machine learning; Detect suspicious transactions; Financial transactions; Supervised learning algorithm |  |  |\n|  |  | © 2022 The Authors. Published by Accounting Study Program, Indonesian Cooperative Institute. This is an open access article under the CC BY NC license ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)) |\n\nINTRODUCTION  \nMoney laundering and financing of terrorism are important issues for economies and financial institutions around the world. Criminals and terrorists exploit financial institutions as a means of carrying out organized, large-scale money laundering (Krishnapriya & Prabakaran, 2014) . In the end, this puts financial institutions, like banks, in a difficult position when it comes to complying with regulations, preserving financial security, preserving reputation, and averting operational risks like liquidity crises and legal action. The Head of the Indonesian Financial Transaction Reports and Analysis Center (PPATK), Kiagus Ahmad Badaruddin, stated that the world is entering the \"digital money laundering era\". He also stated that, \"Revenues generated from 11 transnational crimes, such as drug trafficking, illicit arms trafficking, and human trafficking, are estimated to range from US$1.6 trillion to US$2.2 trillion per year\" (Wicaksono, 2020) .  \nBanks, as parties directly involved in this matter, play an important role in detecting SFT. Banking is a business that relies heavily on customer trust and is strictly regulated by the government (Latif, 2020) . Therefore, it is crucial for banks to constantly improve services in terms of customer protection while also ensuring that the company's operations are in accordance with applicable regulations. In 2021, Bank XYZ's Corporate Assurance has identified several primary focus areas of risks, including cybercrime and suspicious financial transactions. This is consistent with a study conducted by ORX (Operational Risk Horizon) in November 2021, which stated that information security risk, including cyber security, is the top risk today (Johnson, 2021) . As a result, it is essential for Bank XYZ to improve the bank's ability to detect SFT.  \nCurrently, Bank XYZ's SFT detection system is using a rule-based system to identify SFT. However, currently the use of the system is considered to have several challenges in dealing with SFT. Some of the challenges faced are insufficient number of AML compliance officers. Traditional rulebased systems can produce a large number of unnecessary alerts, which makes them extremely inefficient (Hossain et al., 2019). The ","cbCaielzQzljhyvv","https://ap.wps.com/l/cbCaielzQzljhyvv","pdf",666916,1,7,"English","en",105,"# Introduction\n## Background and importance of SFT detection\n## Current rule-based challenges at Bank XYZ\n## Related work and algorithm selection\n# Research Method\n## Quantitative approach and supervised learning models\n## Compared algorithms: Decision Tree, Gradient Boosting, Random Forest\n## Tool: KNIME\n# Results and Discussion\n## Model performance and best accuracy\n## Implications for Bank XYZ","[{\"question\":\"Why is detecting suspicious financial transactions important for banks?\",\"answer\":\"It helps banks comply with regulations and reduce operational risks while protecting financial security and reputation in the context of money laundering and terrorism financing.\"},{\"question\":\"What machine learning models are compared in this study?\",\"answer\":\"The study compares Decision Tree, Gradient Boosting, and Random Forest using supervised learning.\"},{\"question\":\"Which model performs best for detecting SFT at Bank XYZ?\",\"answer\":\"Random Forest achieves the highest accuracy rate of 99.98% in detecting the possibility of SFT.\"}]","Utilization of Machine Learning to Detect the Possibility of Suspicious Financial Transactions | 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