Sprinting Ahead: How Banks can Learn From NFL Analytics to Reduce Trade Settlement Fails

By Daniel Carpenter, Head of Regulation at Meritsoft (a Cognizant company)

Fans of American football and sports enthusiasts will have closely followed the 2021 NFL Draft in recent weeks. With data analytics playing an increasingly important role in helping teams recruit the players most likely to boost their tally of winning games in the coming season, there is a lot that banks could learn as they too look to improve performance. 

In the area of trade settlement fails, recent incidents, such as GameStop, serve to highlight the impact on market participants and the cost of these fails. With fail rates still unacceptably high across the markets, both back and front office teams should be looking to apply greater analytics capabilities to establish the reasons why trades typically fail to settle, anticipate their incidence, and take preventative action to reduce their overall number. 

The NFL Draft has long relied upon data analytics to understand player performance down to minute measurements of time, speed, and power. This has created pools of data points that teams and coaches can analyze to judge which players are right for them. With so much at stake in professional sports, both financial and reputational, this data is exposed to cutting-edge data analytics and Artificial Intelligence (AI) to provide the best possible insights.  

With financial institutions already grappling with the rising cost of settlement failure, the possibility of punitive measures under CSDR and the ripple effect of these new rules beyond the European jurisdiction, the time has come to adopt more effective fails prevention and management capabilities. What is required is a centralized data solution that works towards reconciliations on a daily basis, as opposed to end of week or month-end as is still very often the case. With more data points on the trading lifecycle tracking when and with whom trades are failing to settle, houses can then apply AI to predict if, and when, they will receive the stock or payment from their counterparty. With this insight, the front office can make informed trading decisions, adjusting to prioritise those counterparties that match trades more consistently.

Following the Draft, senior decision makers on Wall Street may well be asking themselves why there is not a greater impetus to implement equivalent steps to improve this part of the post-trade workflow. The ability to use trend analysis to predict fails and act to prevent them is set to become a key component not only of an effective end–to–end workflow solution for fails management but also across other post-trade processes, such as in the management of transaction taxes, claims and brokerage trade expense. 

With improvements in settlement rates, banks can reap the cost and operational benefits, and sprint ahead of their competitors. Investment to digitize, centralize and analyse key data points will serve to enhance the performance of the trading desk and improve the customer experience by opening new areas of value for clients.