Deutsche Bank launches early warning service for securities settlement delays

Deutsche Bank will launch an enhanced securities settlement service that proactively identifies in-flight security transactions at risk of settlement delay, in early 2021.

The service is powered by the Elastic Stack's machine learning and anomaly detection, which will enable the bank's clients to avoid hefty financial penalties under the upcoming implementation of the new Central Securities Depositories Regulation (CSDR) next year.

Earlier this year, Deutsche Bank launched its current real-time settlement service in Euronext, Germany and the UK. The platform provides its operations staff with a real-time view of the issues that can delay a transaction settlement, a stark contrast to traditional platforms which only provide a retrospective view of what has caused settlement delays.

Using dashboards powered by Elasticsearch and Kafka, Deutsche Bank’s teams actively monitor transactions and are provided actionable tasks when a transaction is flagged at risk of delay. The operations teams can also offer other internal teams and external clients high-performance Application Programming Interfaces (APIs) to run their own settlement queries, using Elastic’s search and analytics engine.

Deutsche Bank now plans to enhance its settlement service with Elastic's machine learning technologies, enabling the platform to move from real-time to forward-looking. The enhanced service will proactively detect the in-flight transactions that require actions and alert the bank’s teams before the transactions encounter issues.

“We can now detect transactions in real-time that previously would not be flagged as at risk, and divert our attention from the transactions that ostensibly appear to be at risk, but upon historical analysis have always matched in time to settle,” said Christopher Daniels, director for data products and securities services at Deutsche Bank.

The analytics model underpinning the enhanced service is powered by Elastic’s anomaly detection feature, which considers seasonality, market variation and other changing dynamics to provide the bank’s operational teams with dashboards and action queues that are driven by a large set of factors that would be too broad and complex for a human to process.

Daniels explained: “We have developed several dashboards covering liquidity, settlement performance, and risk and control, but the most recent innovations have been in running machine-learning algorithms in production to provide outlier detection.

"We’re using the platform to identify the most influential features that are more likely to cause a late or failed settlement, and to focus our data quality reviews on activity that does not synchronise with what we would typically expect from a particular cluster."

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