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Security Analysis of Machine Learning Systems for the Financial Sector

Author

Listed:
  • 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))

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.

Suggested Citation

  • Shiori Inoue & Masashi Une, 2019. "Security Analysis of Machine Learning Systems for the Financial Sector," IMES Discussion Paper Series 19-E-05, Institute for Monetary and Economic Studies, Bank of Japan.
    • Handle: RePEc:ime:imedps:19-e-05
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    File URL: https://www.imes.boj.or.jp/research/papers/english/19-E-05.pdf
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    More about this item

    Keywords

    Artificial Intelligence; Machine Learning System; Security; Threat; Vulnerability;
    All these keywords.

    JEL classification:

    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • L96 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Telecommunications
    • Z00 - Other Special Topics - - General - - - General

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