Privacy-Preserving Federated Learning for Detecting Fraudulent Financial Transactions in Japanese Banks

• Item type: Journal(1)
• 公開日: 2022-12-15
• タイトル: Privacy-Preserving Federated Learning for Detecting Fraudulent Financial Transactions in Japanese Banks
• タイトル / 言語: en
• タイトル: Privacy-Preserving Federated Learning for Detecting Fraudulent Financial Transactions in Japanese Banks
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集:持続可能な社会のIT基盤に向けた情報セキュリティとトラスト（招待論文）] privacy-preserving federated learning, deep learning, fraudulent financial transaction detection, demonstration experiment
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: National Institute of Information and Communications Technology
• 著者所属: National Institute of Information and Communications Technology
• 著者所属: National Institute of Information and Communications Technology
• 著者所属: National Institute of Information and Communications Technology
• 著者所属: National Institute of Information and Communications Technology
• 著者所属: National Institute of Information and Communications Technology
• 著者所属: National Institute of Information and Communications Technology
• 著者所属: Kobe University
• 著者所属: Kobe University
• 著者所属: National Institute of Information and Communications Technology
• 著者所属: Kobe University
• 著者所属: National Institute of Information and Communications Technology
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology
• 著者所属(英): en
• 著者所属(英): Kobe University
• 著者所属(英): en
• 著者所属(英): Kobe University
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology
• 著者所属(英): en
• 著者所属(英): Kobe University
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology
• 著者名: Sachiko, Kanamori; Taeko, Abe; Takuma, Ito; Keita, Emura; Lihua, Wang; Shuntaro, Yamamoto; Le, Trieu Phong; Kaien, Abe; Sangwook, Kim; Ryo, Nojima; Seiichi, Ozawa; Shiho, Moriai
• 著者名(英): Sachiko, Kanamori; Taeko, Abe; Takuma, Ito; Keita, Emura; Lihua, Wang; Shuntaro, Yamamoto; Le, Trieu Phong; Kaien, Abe; Sangwook, Kim; Ryo, Nojima; Seiichi, Ozawa; Shiho, Moriai
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: To tackle financial crimes including fraudulent financial transactions (FFTs), money laundering, illegal money transfers, and bank transfer scams, several attempts have been considered to employ artificial intelligence (AI)-based FFT detection systems, particularly, deep learning-based ones. However, to the best of our knowledge, no federated learning systems using real transaction data among financial institutions have been implemented so far. This is because there are several issues to be addressed as follows: (1) it is difficult to prepare sufficient amount of transaction data for training by one financial institution (e.g., a local bank), and a small amount of dataset does not promise high accuracy for detection, (2) each transaction data contains personal information, and thus it is restricted by Act on the Protection of Personal Information in Japan to provide the transaction data to a third party. In this paper, we introduce out demonstration experimental results of privacy-preserving federated learning with five banks in Japan: the Chiba Bank, Ltd., MUFG Bank, Ltd., the Chugoku Bank, Ltd., Sumitomo Mitsui Trust Bank, Ltd., and the Iyo Bank, Ltd. As the underlying cryptographic tool, we proposed a privacy-preserving federated learning protocol which we call DeepProtect, for detecting fraudulent financial transactions. Briefly, DeepProtect allows parties to execute the stochastic gradient descent algorithm using a set of techniques for the privacy-preserving deep learning algorithms (IEEE TIFS 2018, 2019). In the demonstration experiments, we built machine learning models for detecting two types of financial frauds ― detecting fraudulent transactions in customers/victims' accounts and detecting criminals' bank accounts. We show that our federated learning system detected FFTs that could not be detected by the model built using the dataset from a single bank and detected criminals' bank accounts before the bank actually froze them.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.789; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: To tackle financial crimes including fraudulent financial transactions (FFTs), money laundering, illegal money transfers, and bank transfer scams, several attempts have been considered to employ artificial intelligence (AI)-based FFT detection systems, particularly, deep learning-based ones. However, to the best of our knowledge, no federated learning systems using real transaction data among financial institutions have been implemented so far. This is because there are several issues to be addressed as follows: (1) it is difficult to prepare sufficient amount of transaction data for training by one financial institution (e.g., a local bank), and a small amount of dataset does not promise high accuracy for detection, (2) each transaction data contains personal information, and thus it is restricted by Act on the Protection of Personal Information in Japan to provide the transaction data to a third party. In this paper, we introduce out demonstration experimental results of privacy-preserving federated learning with five banks in Japan: the Chiba Bank, Ltd., MUFG Bank, Ltd., the Chugoku Bank, Ltd., Sumitomo Mitsui Trust Bank, Ltd., and the Iyo Bank, Ltd. As the underlying cryptographic tool, we proposed a privacy-preserving federated learning protocol which we call DeepProtect, for detecting fraudulent financial transactions. Briefly, DeepProtect allows parties to execute the stochastic gradient descent algorithm using a set of techniques for the privacy-preserving deep learning algorithms (IEEE TIFS 2018, 2019). In the demonstration experiments, we built machine learning models for detecting two types of financial frauds ― detecting fraudulent transactions in customers/victims' accounts and detecting criminals' bank accounts. We show that our federated learning system detected FFTs that could not be detected by the model built using the dataset from a single bank and detected criminals' bank accounts before the bank actually froze them.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.789; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 63, 号 12, 発行日 2022-12-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764
• 公開者 / 言語: ja
• 公開者 / 出版者: 情報処理学会