1. Discussion for: Broadening the data base for deepening the focus
Discussion for: Broadening the data base for deepening the focus? The use of big data analytics in transaction banking – Dr. Martin Diehl Discussant: Adrian Guerin, Bank of Canada*
*Any opinions expressed herein are those of the discussant and do not necessarily represent the views of the Bank of Canada

2. Presentation Overview
Introduction to Big Data Analytics and methods
Use cases in financial industry
Available data in transaction banking
Overview of existing areas of research
Potential applications for central banks

3. Summary: Big Data Analytics
More data, different types, speed, accuracy, and value.
Variety of sources – internet, devices, corporate systems, infrastructures.
BDA: combination of Data Mining and Machine Learning Methods.
Methods:
Supervised learning
Unsupervised learning
Reinforcement learning

4. Summary: Use Cases, Transaction Banking
Existing use cases
Classification:
Anomaly detection and fraud detection
Default prediction
Forecasting:
Market returns, risk indicators
Sentiment analysis
Potential use cases
Predict RTGS liquidity needs (participant or system)
Forecast consumption

5. Comments Thank you: presentation highlights analytical opportunity
BDA (Data Science) an evolving field, skills in short supply
Applied most notably in private sector (e.g., Google, Amazon, IBM).
Proliferation of DS training (university level, certificate).
Content is timely
DLT or “blockchain” technology: potential for wealth of data provided to central banks and regulators
Possible “regulator node” with full ledger visibility (near real-time data).
Example initiatives:
ASX examining DLT option with DAH for CHESS replacement
DTCC partnered with IBM, Axoni, R3 to build DLT solution for derivatives post-trade processing (re-platform DTCC’s Trade Information Warehouse)

6. Comments/Questions
Overview of primary methods very helpful as introduction
Light touch on data scraping/mining and NLP, but appropriate given it may not be useful in payments systems context.
Questions: Data assessment (available data)
Are there opportunities for data mining to complement transaction banking data and analyses?
Can we confidently use ‘unofficial statistics’ acquired through data mining?
Comment, unsupervised learning:
Can be challenging to set up (e.g., k-means, how many clusters appropriate) and interpret (are there “natural” groups?).

7. Comments/Questions AI methods as “black box”
Comments: Agree, must (continue to) be vigilant of model risk.
Can measure performance of ML algorithms (e.g., confusion matrix for supervised learning), but…
May create false sense of confidence.
Are test/train data representative, contain outlies? Where are the false negatives?
Question/Comments: Feature selection
Key ML objective is to maximize measured model performance.
Does this create a risk similar to in econometrics with “kitchen sink” models and the search for statistical significance?
Do the techniques lend themselves to overreliance on such measures – is this a risk for feature selection (might we improperly leave something out)?
ML methods are “smart”, but continue to rely on data inputs; feature selection and lookback period / sample data considerations are important.

8. Thank you!