In today’s trading environment, the most successful banks are adopting technology that enables them to reduce costs by automating repetitive and time-intensive tasks, and improve performance by offering insights into client and market behavior.
But banks cannot expect to reap the rewards of artificial intelligence if their data and infrastructures are not optimized to provide the right inputs and integrated correctly or distributed widely enough so that everyone who could benefit from the analysis has a chance to use it.
For a trader sitting behind the wheel in the stormy fixed income markets, access to accurate data analytics can mean the difference between navigating successfully to the chosen destination, or staying unguided and continuing with business as usual.
In today’s trading environment, banks that fail to unlock the potential of the huge amount of data already available to them within their organizations quickly fall behind their forward-looking peers. The most successful players are adopting technology that enables them to reduce costs by automating repetitive and time-intensive tasks, and improve performance by offering insights into client and market behavior that was previously hidden to the human eye, or, at least, not widely distributed within the organization.
But, much like filling a Ferrari with the wrong type of fuel and expecting to win the Grand Prix, banks cannot expect to reap the rewards of highly sophisticated artificial intelligence technology platforms if their data and technological infrastructures are not optimized to provide the right inputs and integrated correctly or distributed widely enough through the organization that everyone who could benefit from the analysis has a chance to use it.
Clearing the fixed income fog
Fixed income desks are dealing with the same clients across multiple products and potentially across multiple geographies. Managers and traders need to be able to get a holistic view of their interactions with each client in order to maximize interactions with their most profitable ones and eliminate any anti-social trading activity.
Desks need to know if the market moves immediately after the bank trades with a particular client – or, how much of the available business is each desk capturing, and why some business is going to its competitors.
By bringing together market data with proprietary data on the business all the fixed income desks are trading, managers can see how the market moves after trading with a given client, in order to identify and reduce toxic trading. They can see when they are consistently losing business to other banks and put recourses into investing into those client relationships. They can also see which desks are performing well and which are struggling, within the context of their own business and the market as a whole. This data value chain ensures that the customer’s motivation is aligned with your motivation to compound the value of your proprietary dataset.
These are just some of the questions which analytics can help to answer, opening up significant opportunities for fixed income desks to improve performance. It is possible to generate these sorts of insights in real time and even begin applying predictive technologies to anticipate the need of the client and service them in a different way, provided that data analytics are correctly integrated within the bank’s existing technology.
Creating a single view of each client’s activity
It is vital that data from across the organization can be brought together into one analytics viewpoint, with one shared data model.
The first step is standardizing the format in which data is stored within the organization. However, for analytics to be truly effective, it is also vital to get a holistic view of all data on each client and the market at the same time. This allows the data analytics technology to analyze the bank’s fixed income business as a whole and identify the patterns which lie within the data.
Doing so does not necessarily require building a new data warehouse and migrating existing data stores. Such an approach is time consuming and costly for IT teams which are already stretched. In addition, it increases the likelihood of data duplication and significantly increases the risks to the fixed income department’s data security by creating one storage point with a huge amount of highly valuable and confidential data.
Instead, the bank can look to pull data together from across the various databases within the fixed income department and analyse it “in memory,” or by utilizing cloud technology. This negates the need for separate storage infrastructure and speeds up the analytics process. Crucially, such an approach is also highly secure and approved for use by regulated financial institutions.
Provided that the data is stored in a standardized format, this “in memory” approach creates a data feed which offers a holistic and real-time view of the fixed income desk activity. Accessible via an API, the data now can be used to power the analytics technology which will derive the intelligence the desks need to better understand its business. It can also be pulled out into a downloadable report to automate back-office reporting tasks and speed up compliance.
Building full-stack products requires deep subject matter expertise. Selling these products requires trust, respect, and relationships within the industry. Teams that manage to combine the subject matter and technical expertise are able to model the domain richly and drive innovation that comes from thinking outside the box by understanding what the box is.
Implementing this technology can sound like a daunting task. Most banks’ IT teams are currently focused on readying fixed income desks for MiFID II, putting performance improvements on the back burner. One solution could be rolling out one of the technology solutions already in operation on one desk, thus cutting down on the amount of new technology and systems changes which need to be implemented.
However, this can cause internal issues, as desks baulk at having their well-worn practices superseded by those of their colleagues. It also means that the choice of system is limited to the best within the organisation, rather than looking for the best solution in class across the whole market.
Bringing in an expert third-party specialist provider can help make the integration process smoother. Implementing a system which is new to all desks can avoid some of the complexities around inter-desk politics, while a vendor’s technical team can take the strain off the bank’s in-house IT teams.
In order to ensure that the data store and analytics are fully integrated with the bank’s existing proprietary analytics technologies, the vendor can create a secure “container” into which the standardized algorithms can be deployed. Those algorithms built in-house and by the analytics provider or third-party quantitative analysts can be deployed within this environment and insights then distributed across the organization via API.
With the right approach, the process can be swift, cost effective and requires minimal infrastructure change.
From this solid foundation of aggregated, integrated data, powerful analytics can interrogate the data to generate insights, both in real time and utilizing AI to predict client behavior. These can then be distributed throughout the whole organization, not just to the quant teams but rather to help traders and sales desks to become proactive.
These insights can help clear the fog and give managers and traders around the world a clear and exact view of their current trading environment and how their interactions with their clients are relating to the broader market. Only then can they take proactive steps and gain a competitive edge over their competition.
Taking full advantage of the power of analytics requires further work, from training staff and driving behavioral changes within the organization, to perfecting the algorithms themselves over time. However, getting the integration of the core technology right is the cornerstone around which the rest of a successful analytics project is based.
CEO & Founder, Mosaic Smart Data