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Detecting anomalous payments networks: A dimensionality-reduction approach


  • León, Carlos


Anomaly-detection methods are aimed at identifying observations that deviate manifestly from what is expected. Such methods are usually run on low-dimensional data, such as time series data. However, the increasing importance of high-dimensional payments and exposure data for financial oversight requires methods for detecting anomalous networks. To detect an anomalous network, dimensionality reduction allows measuring of the extent to which the network's main connective features (i.e. the structure) deviate from those regarded as typical. The key to dimensionality-reduction methods is the ability to reconstruct data with an error; this reconstruction error serves as a yardstick for deviation from what is typical. Principal component analysis (PCA) is used as a dimensionality-reduction method, and a clustering algorithm is used to classify reconstruction errors as normal or anomalous. Based on data from Colombia's large-value payments system and a set of synthetic anomalous networks created through simulations of intraday payments, detecting anomalous payments networks is feasible and promising for financial-oversight purposes.

Suggested Citation

  • León, Carlos, 2020. "Detecting anomalous payments networks: A dimensionality-reduction approach," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
  • Handle: RePEc:eee:lajcba:v:1:y:2020:i:1:s2666143820300016
    DOI: 10.1016/j.latcb.2020.100001

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    References listed on IDEAS

    1. Berndsen, Ron J. & León, Carlos & Renneboog, Luc, 2018. "Financial stability in networks of financial institutions and market infrastructures," Journal of Financial Stability, Elsevier, vol. 35(C), pages 120-135.
    2. Brooks,Chris, 2008. "RATS Handbook to Accompany Introductory Econometrics for Finance," Cambridge Books, Cambridge University Press, number 9780521896955, November.
    3. León, Carlos & Berndsen, Ron J., 2014. "Rethinking financial stability: Challenges arising from financial networks’ modular scale-free architecture," Journal of Financial Stability, Elsevier, vol. 15(C), pages 241-256.
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    Cited by:

    1. Luis Gerardo Gage & Raúl Morales-Resendiz & John Arroyo & Jeniffer Rubio & Paolo Barucca, 2022. "Classifying payment patterns with artificial neural networks: an autoencoder approach," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Machine learning in central banking, volume 57, Bank for International Settlements.
    2. León, Carlos & Barucca, Paolo & Acero, Oscar & Gage, Gerardo & Ortega, Fabio, 2020. "Pattern recognition of financial institutions’ payment behavior," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    3. Ajit Desai & Anneke Kosse & Jacob Sharples, 2024. "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," BIS Working Papers 1188, Bank for International Settlements.
    4. Ajit Desai & Anneke Kosse & Jacob Sharples, 2024. "Finding a Needle in a Haystack: A Machine Learning Framework for Anomaly Detection in Payment Systems," Staff Working Papers 24-15, Bank of Canada.

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    More about this item


    Anomaly detection; Payments; Network; Dimensionality; Clustering;
    All these keywords.

    JEL classification:

    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods


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