Firm foundations
05 Oct 2020
Operational resilience, robust infrastructure and technology in the back office of securities post trade has enabled the front end to cope with unprecedented trading volumes and prepare for an uncertain future, according to Michaela Ludbrook of Deutsche Bank
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For Michaela Ludbrook, Deutsche Bank’s global head of securities services lockdown and overnight relocation to home “offices” was a poignant reminder of the importance of getting the nuts and bolts right to enable clients in the front office to continue to trade with minimal business interruption. “It highlighted the need for data and technology to protect, scale and streamline, people to invigorate processes, and partnerships to pioneer the next set of solutions,” she says.
Transforming partnerships
“With the crisis precipitating both home and split office working across various locations around the world, we have invoked digital tools such as electronic signatures, transparency around flows, data reporting and lifecycle transparency,” she says.
As digitalisation accelerates right across the corporate banking landscape, operating and business models will change as the way people execute tasks or the technology they use evolves. “Artificial intelligence (AI), data and distributed ledge technology (DLT) will be crucial for future transparency around data and people being able to self-service,” reflects Ludbrook.
Deutsche Bank Securities Services is assessing all digital tools and deciding which ones to integrate to reduce data latency, improve transparency and improve overall client experience. Ludbrook believes the industry needs to be flexible about which ones to test drive and then integrate.
“It could be eliminating tasks like email queries with self-service models, for instance application programming interfaces (APIs) enable us to link, and plug and play very quickly and DLT enables some parallel processes to eliminate handoffs.”
To this end, Ludbrook shares that the bank’s securities services team is “driving a partnership and co-creation approach with clients to build differentiated solutions that deliver end clients’ benefits and solve common problem statements”.
The business is advancing its exploration of DLT, API, AI, data analytics and machine learning to deliver client-centric securities services with several use cases in play:
- Using DLT to automate the over-the-counter bond lifecycle with digital “money” through allocation, primary issuance to secondary market trading and post-trade activities
- A pilot using DLT to enable beneficial ownership transparency in Europe, thereby simplifying compliance with regulatory requirements by providing investor beneficiary holding information while ensuring data privacy
- Using APIs to provide real-time trade settlement status updates to minimise the manual inputting of settlement instructions; the launch of Chatbot Debbie being an example of this
- Collaboration with an asset management company to enhance its liquidity management with real-time cash notification APIs
- Collaborations with a global custodian bank to develop an API-based foreign exchange (FX) custody solution that automates time-sensitive FX-related processes
- Using data analytics and machine learning, delivering a data analytics model that displaying clients’ cash liquidity usage and how this corresponds to Deutsche Bank’s funding provision in the market. Another tool, a settlement efficiency dashboard, uses big data to provide clients and operations with performance, volume and operating metrics
This article first appeared in Deutsche Bank’s Guide to Sibos at www.db.com/sibos
Transforming partnerships
“With the crisis precipitating both home and split office working across various locations around the world, we have invoked digital tools such as electronic signatures, transparency around flows, data reporting and lifecycle transparency,” she says.
As digitalisation accelerates right across the corporate banking landscape, operating and business models will change as the way people execute tasks or the technology they use evolves. “Artificial intelligence (AI), data and distributed ledge technology (DLT) will be crucial for future transparency around data and people being able to self-service,” reflects Ludbrook.
Deutsche Bank Securities Services is assessing all digital tools and deciding which ones to integrate to reduce data latency, improve transparency and improve overall client experience. Ludbrook believes the industry needs to be flexible about which ones to test drive and then integrate.
“It could be eliminating tasks like email queries with self-service models, for instance application programming interfaces (APIs) enable us to link, and plug and play very quickly and DLT enables some parallel processes to eliminate handoffs.”
To this end, Ludbrook shares that the bank’s securities services team is “driving a partnership and co-creation approach with clients to build differentiated solutions that deliver end clients’ benefits and solve common problem statements”.
The business is advancing its exploration of DLT, API, AI, data analytics and machine learning to deliver client-centric securities services with several use cases in play:
- Using DLT to automate the over-the-counter bond lifecycle with digital “money” through allocation, primary issuance to secondary market trading and post-trade activities
- A pilot using DLT to enable beneficial ownership transparency in Europe, thereby simplifying compliance with regulatory requirements by providing investor beneficiary holding information while ensuring data privacy
- Using APIs to provide real-time trade settlement status updates to minimise the manual inputting of settlement instructions; the launch of Chatbot Debbie being an example of this
- Collaboration with an asset management company to enhance its liquidity management with real-time cash notification APIs
- Collaborations with a global custodian bank to develop an API-based foreign exchange (FX) custody solution that automates time-sensitive FX-related processes
- Using data analytics and machine learning, delivering a data analytics model that displaying clients’ cash liquidity usage and how this corresponds to Deutsche Bank’s funding provision in the market. Another tool, a settlement efficiency dashboard, uses big data to provide clients and operations with performance, volume and operating metrics
This article first appeared in Deutsche Bank’s Guide to Sibos at www.db.com/sibos
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