You’ve done the training and got the fitness but decide at the last minute to quit the race. Seems strange to sacrifice a finisher medal when you’re fully prepared, right?

In most circumstances we’d consider this attitude to be strange. However, as financial institutions adapt their business processes to Basel III liquidity requirements (such as BCBS 239), many fail to make it over the finish line and leverage the vast amount of aggregated data, beyond what is required for basic regulatory purposes.

In this article, we use the Liquidity Coverage Ratio (LCR) to explore how financial institutions can better utilise their data. Check back here for one of our earlier articles, to see how it’s calculated.

Preparation is the key to a successful race

To accurately calculate LCR, a bank is required to gather granular data relating to all open transactions as well as off-balance sheet exposures. Unsurprisingly, the process of gathering, interpreting, cleaning and monitoring this much data is operationally demanding. Just like athletes carrying out a training programme, banks who complete this exercise are often fatigued.

Akin to sport, what differentiates the high performer from the average competitor is the ability to push past the feeling of exhaustion and capitalise on effort.

Financial institutions must seize the opportunity to transform the vast amount of daily data they are collecting into valuable business insights.

To put this into context, financial institutions must seize the opportunity to transform the vast amount of daily data they are collecting into valuable business insights. Nowhere is data aggregation and value creation opportunity more critical than in central treasury (or equivalent).

There are many ways that this concept can be demonstrated. However, in this article we illustrate (by means of a simple example) the extent to which liquidity costs can be reduced by simply understanding the data and making small adjustments.

Analyse & adjust your training to improve your performance

Before we go on it’s worth noting that, like a tailored training programme, each organisation is different and no one-size-fits-all approach exists. Therefore, we encourage firms to explore their own unique circumstances, balance sheets and client compositions in order to determine the most effective way to benefit from this idea.

Let’s imagine company A – a large non-financial corporate client who has an assumed available overdraft facility of £1m. Under Basel III a £1m overdraft[1] will typically require the bank to hold High Quality Liquid Assets (HQLA) equivalent to 30% of the undrawn amount of the overdraft (after the required haircuts for Level 2 assets have been applied).

In the base case, assuming a mix of level 1 and level 2 assets, the bank would need to set aside approximately £360,000 of liquid assets to satisfy the requirements imposed by Basel III (assuming it holds the maximum allowable amount of Level 2a and Level 2b assets)[2].

If however, the bank has an awareness of customer behaviour and can derive business insights from this information, they could be faced with an opportunity to reduce their current holding of HQLA and costs associated with setting aside this capital.

For this example, let’s assume that company A is a large retailer and is subject to highly seasonal peaks and troughs in its cashflow. Specifically, we’ll assume their cashflow follows the average for a UK retailer: in the last month of each quarter revenues are at least 20% greater than in the remaining months.

If we can demonstrate that behaviour is consistent and predictable for company A, then we can apply the insight to the calculation of estimated outflows[3]. To quantify this, if we can reliably forecast that a liquidity facility will not be required in March, June, September and December, and we model this as a 50% reduction in estimated outflows (to be conservative), then the bank can generate a c.17% reduction in the average amount of HQLA required to be held by the bank for company A.

The theory of marginal gains

Of course, this is just one simple example. However, the idea of making small incremental changes to something to produce an overall improvement is not a new one. In fact, many athletes adopt this ‘marginal gains’ mentality by making minor improvements to their lifestyles on and off the track. This is with the intention of producing an overall improvement to performance.

Using this theory, a bank can optimise net outflows against the cap and reduce the burden on their HQLA. This is done by making ‘marginal gains’ to forecasted outflows based on behavioural assumptions and scaling these up across the bank’s entire book of products. As an additional and consequential benefit, this optimisation provides positive signals to the market and can reduce the overall cost of these instruments, therefore providing significant savings.

To put this into perspective, if each of the top four UK high street lenders were able to reduce their HQLA by even half of the 17% we calculated in the above example, on average they could realise a cost saving of approximately £360 million per year.

To put this into perspective, if each of the top four UK high street lenders were able to reduce their HQLA by even half of the 17% we calculated in the above example, on average they could realise a cost saving of approximately £360 million per year,[4].

Fail to prepare, prepare to fail

Although the calculation of LCR is neither complicated nor time consuming, the amount of effort and preparation that goes into collecting and cleaning transaction level data is huge. We have worked alongside several banks during this process and understand the temptation to overlook or descope the analytics that unlock the potential that this data has. However, we also understand the dangers of ignoring its potential.

We help our clients on the journey to understand how their data can be used to reduce HQLA – and deliver much more. If your competitors are already thinking about this, don’t be the last one out of the starting blocks. Our advice to businesses: use your data more efficiently to reduce your organisation’s liquidity costs.