Security Analysis of Machine Learning Systems for the Financial Sector

Shiori Inoue and Masashi Une
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Shiori Inoue: Institute for Monetary and Economic Studies, Bank of Japan (E-mail: shiori.inoue@boj.or.jp)
Masashi Une: Director, Institute for Monetary and Economic Studies, Bank of Japan (E-mail: masashi.une@boj.or.jp)

No 19-E-05, IMES Discussion Paper Series from Institute for Monetary and Economic Studies, Bank of Japan

Abstract: The use of artificial intelligence, particularly machine learning (ML), is being extensively discussed in the financial sector. ML systems, however, tend to have specific vulnerabilities as well as those common to all information technology systems. To effectively deploy secure ML systems, it is critical to consider in advance how to address potential attacks targeting the vulnerabilities. In this paper, we classify ML systems into 12 types on the basis of the relationships among entities involved in the system and discuss the vulnerabilities and threats, as well as the corresponding countermeasures for each type. We then focus on typical use cases of ML systems in the financial sector, and discuss possible attacks and security measures.

Keywords: Artificial Intelligence; Machine Learning System; Security; Threat; Vulnerability (search for similar items in EconPapers)
JEL-codes: L86 L96 Z00 (search for similar items in EconPapers)
Date: 2019-05
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ict and nep-pay
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