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Collateral IT: Optimizing HSBC asset allocation with Google Cloud

Have you ever heard of an optimization problem? Imagine you have a million marbles, all of different sizes, colors, patterns, and weights. You need to fill up 1,000 jars of different sizes with them, but each jar has restrictions as to which colors, patterns, and how many marbles of each type it can hold. After filling all the jars, you may keep any leftover marbles, so you want to ensure that these are the shiniest ones in the bunch. How do you go about solving this puzzle? There are many possibilities, but what would be the most efficient way to guarantee you’ll reach the best possible outcome every time? 

At HSBC, our Collateral Treasury desk and Collateral Management team have been solving a similar problem, but instead of marbles and jars, we work with around 50,000 assets that can be used as collateral, and 1,000s of collateral accounts.

Our Collateral Treasury Trading desk helps finance our Markets business by providing the required collateral, such as certain debt or equities, to cover its obligations to a client. The collateral could be used by HSBC for its margin requirements, CCPs (Central Counterparty Clearing Houses), or for securities financing transactions. Each obligation can have different eligibility criteria about what assets can be used as collateral for each client. These rules and restrictions revolve around the type of asset allowed or daily liquidity factors.

The process of matching collateral to obligations is known as an allocation, and it can get complicated the more diverse the collateral pool and the more customers you have. It is important to allocate collateral in the most efficient way and, traditionally, this business problem has been managed manually or by a third party against a set of simple rules.

But by collaborating with Google Cloud, we can improve collateral allocation through automation. Even small efficiency gains have the potential to make a significant difference when the amount of collateral inventory managed is tens of billions of dollars. It means the Collateral Treasury desk is much more efficient when managing its own funding costs or regulatory ratios such as the Liquidity Coverage Ratio (a key financial resource measure for banks that ensures sufficient high quality assets are readily available to survive periods of liquidity stress).

Leveraging AI to tackle a complex business problem

Our solution is OPTIC, a HSBC platform utilizing Google Operation Research Tools, or OR-Tools, an open-source optimization library provided by Google AI. Its main goal is to allow the Collateral Treasury desk to automate the collateral allocation process in the most optimal way on any given day. OPTIC’s architecture is based on microservices and provides the ability to handle large volumes of data, for which a scalable and self-managed infrastructure is needed.  OPTIC runs on Google Kubernetes Engine, which provides it with workload rebalancing, auto-scaling capabilities, and high availability. 

Additionally, we collaborated with Google Operations Research, which gave us access to the experience of Google AI engineers who were able to advise us on the best way to implement their optimization libraries to solve our business problem.  

We’ve found that using linear programming solvers such as Google OR-Tools is the best way to achieve the optimization capability that fundamentally changes how we manage our inventory. It enables us to optimize for multiple outcomes and be certain that we are doing this in the most efficient way possible. 

Finding the optimal way forward with automation

OPTIC works by consuming data feeds from a multitude of systems, then it standardizes the data, and combines with decision-making parameters and weightings Google OR-Tools can use, to understand and arrive at an optimal outcome. OPTIC can also provide insights and metrics that help optimize business decisions such as which assets can be added or removed in the future to make collateral allocation even more efficient.

Looking forward, this project makes us optimistic about using Google Kubernetes Engine to manage more of our microservices in other platforms. This will mean that we can scale without worrying about hardware or making big changes to our environment. With this solution, we’ll be able to mobilize and optimize our collateral according to our own view of the value and quality of the collateral, as well as the best fit for our exposures at any given time.  

Over time, the project can bring visible long-term benefits to HSBC’s Markets business, including decreased operational risks and potential to save significant funding costs. Next, we will continue to add new capabilities and data sources to our solution to continue solving some of the most complex business challenges in our industry.

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