Detection and Explanation of Anomalous Payment Behavior in RTGS Systems

1. Public Patrick Papsdorf Adviser European Central Bank Discussion: “Detection and Explanation of Anomalous Payment Behavior in RTGS Systems” by Trieples, Daniels, Heijmans 15th Payment System Simulator Seminar31st August 2017, Helsinki/Finland The views expressed here are those of the author and do not necessarily represent the views of the European Central Bank and the Eurosystem.

2. See https://jakubmarian.com/wp-content/uploads/2014/12/milk-consumption.jpg See https://jakubmarian.com/wp-content/uploads/2017/03/nuts2-researchers.jpg P.Papsdorf - Discussion anomaly detection

3. Anomalous payment behaviour Anomaly detection in payments data Demand: high for identifying anomalous behaviours timely, (semi) automatically with high accuracy Challenges: data complexity (3V’s),networks, scope setting, resources … Paper: Summary See Practical Machine Learning: A New Look at Anomaly Detection by E. Friedman, Ted Dunning P.Papsdorf - Discussion anomaly detection

4. Anomaly detection (outlier detection) Identify items/events/observations that do not conform to an expected pattern or other items in a dataset. Here: Unsupervised anomaly detection. Detects anomalies in unlabelled data sets. “You don’t know exactly what you are looking for.” Supervised anomaly detectionbased on labelling of data as "normal" and "abnormal". Autoencoder Feed-forward neural network, which learns from examples. Itapplieslearningstonewdata. No learning of concrete examples but recognition of patterns. See Wikipedia on neural networks Trained to reconstruct input layer at the output layer by processing input via a hidden layer in which a set of neurons form a compressed representation of the input in a lower dimensional space. (See Triepels, Daniels, Heijmans) Summary AI / machine learning See https://betanews.com/2016/12/12/deep-learning-vs-machine-learning/ See Practical Machine Learning: A New Look at Anomaly Detection by E. Friedman, Ted Dunning Input layer Hidden layer Output layer See https://blog.keras.io/building-autoencoders-in-keras.html P.Papsdorf - Discussion anomaly detection

5. Dutch component of TARGET2, customer payments of 20 most active banks broken down in time intervals and liquidity vectors Three different autoencoders Optimal point of neurons/compression determined: more neurons would not lead to much better reconstruction (low MRE), i.o.w. dynamics of liquidity vectors well captured. Anomalies spotted (above set threshold) and three observation areas (A, B, C) examined by drill down using time interval, banks, in/outflows Bank that was subject to bank run was identified Summary [Please select] [Please select] What was done here P.Papsdorf - Discussion anomaly detection

6. Thank you for this! Novel approach to apply AI to payments data. Autoencoder method resulted in identifying outliers. Possibly opening a new strand in FMI analytics interesting for System Operator and Oversight. Many potential fields could be considered, like AML/CTF, fraud, intraday liquidity management, funding issues, MM outliers, auto-triggers/alerts, interdependencies … Comments and questions P.Papsdorf - Discussion anomaly detection

7. Model set-up One hour time intervals for liq. vectors vslonger/shorter intervals. Reasoning for chosen timespans of datasets. How to operationalize method in a dynamic environment - continued learning? Anomaly detection Threshold setting and review is manual. Type1 and Type 2 errors to underpin “reasonable accuracy”. Compression level: risk of Over-fitting vs. Under-fitting. Timeliness and accuracy – some outliers (A,C) only explained as non-relevant over time. Comments and Questions P.Papsdorf - Discussion anomaly detection

8. Scenario Chosen scenario typically evolves very differently. Once run is detected it may be too late. Are there other earlier signals in payments data?E.g. CB operations, interbank, delays, intraday credit usage, cash reservations that could be tested. Knowledge of bank run helped to understand identified outlier (B). Comments and Questions P.Papsdorf - Discussion anomaly detection

9. Thank you … and a discussion appetizer for later S.Hawking: “The development of full artificial intelligence could spell the end of human race.” L.Page: “Artificial intelligence would be the ultimate version of Google.” Ethical and moral aspects related to AI E.Musk: “biggest risk we face as a civilization.” V. Rometty: “this technology will enhance us. So instead of artificial intelligence, I think we'll augment our intelligence.” P.Papsdorf - Discussion anomaly detection