While industry STP rates are high in transaction processing, there are pockets of exceptions, where instructions or transactions are received through unstructured mediums. Brown Brothers Harriman (BBH) adressed this issue through democratized automation tools – mapping trade templates, extracting trade details automatically and feeding them directly onto its trade platform.
Capturing the data in this way reduces the risk of manually re-keying information and increases efficiency. Leveraging the subject matter experts to facilitate this automation created capacity and surfaced a further opportunity for greater efficiency.
Consider an ETF servicing team which is laser focused on monitoring unmatched trades that are in high risk, mandatory buy-in or Central Securities Depositary Regulation (CSDR) markets that apply financial penalties for fails. A machine learning algorithm that acts as an early warning system by predicting the likelihood of the trade failing in advance of settlement date could be a major asset for an analyst. This would direct them to take corrective action on the trades most at risk, helping to mitigate penalties and fees that would get passed onto customers and counterparts.