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 Seminar 31st 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. P.Papsdorf - Discussion anomaly detection
See
See
P.Papsdorf - Discussion anomaly detection

3. Idea Aim Result Anomalous payment behaviour
Location,
Summary
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:
Idea
Apply machine learning to help identifying anomalies
Aim
Can method identify liquidity problems of a bank in the data?
Result
“Method worked reasonably well.”
See Practical Machine Learning: A New Look at Anomaly Detection by E. Friedman, Ted Dunning
P.Papsdorf - Discussion anomaly detection
Name of the Author

4. AI / machine learning Anomaly detection (outlier detection)
Location,
Summary
AI / machine learning
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 detection based on labelling of data as "normal" and "abnormal".
Autoencoder
Feed-forward neural network, which learns from examples. It applies learnings to new data. 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)
See 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
P.Papsdorf - Discussion anomaly detection
Name of the Author

5. What was done here Summary Training dataset Holdout dataset EVALUATION
Location,
Summary
[Please select]
[Please select]
What was done here
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
Nov 08 – Aug09
LEARNING
Training dataset
Sept – Oct 08
OPTIMISATION
Holdout dataset
Sept – Oct 09
EVALUATION
Test data
P.Papsdorf - Discussion anomaly detection
Name of the Author

6. Novel approach to apply AI to payments data.
Comments and questions
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 …
P.Papsdorf - Discussion anomaly detection

7. One hour time intervals for liq. vectors vs longer/shorter intervals.
Comments and Questions
Model set-up
One hour time intervals for liq. vectors vs longer/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.
P.Papsdorf - Discussion anomaly detection

8. Chosen scenario typically evolves very differently.
Comments and Questions
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).
P.Papsdorf - Discussion anomaly detection

9. Ethical and moral aspects related to AI
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