Steps Ahead

Finding and protecting FICC liquidity

Q&A with Matthew Hodgson, CEO and Founder

Amidst a backdrop of volatility, economic uncertainty and lower market activity in some asset classes, FICC market participants are struggling more than ever to source and protect liquidity. In addition, maximising the productivity of sales and trading teams has become top priority as cost saving and headcount reduction has become the norm.

Finding the right inventory, at the right time and making the right recommendations to clients is critical to achieving a competitive advantage and retaining and growing market share. The key to this lies in deploying innovative AI-driven data analytics tools that enable banks to see where the herd is trading, where the opportunity for alpha is, and where they can find the specific instruments they want to trade. This is all automated and delivers huge time and cost savings for a bank – freeing up frontline staff to focus on client contact and activities.

1. Why can liquidity be so challenging to find in today’s FICC markets?

The underlying drivers are complex but are related to the slow shift from voice to electronic, plus fragmentation in electronic markets – when liquidity is low, voice trading becomes more important to find liquidity and to execute. Vanilla, however, is mostly electronic in all asset classes.

In addition, lots of bonds are still traded over the counter – so it can be hard to find bid and ask prices for some bonds, particularly complex bonds and lower-grade corporate bonds.

2. How is data spending changing as a result of participants’ mission to find and protect liquidity?

Market data spending grew 25% from 2018 – 2022, according to Burton-Taylor International Consulting, with real-time and trading data accounting for the largest portion of spend, representing 38% of the total spending on data.

In addition, 80% of buy-side firms believe data budgets will rise over the next 12 months, according to Greenwich, with over one-quarter anticipating a rise of at least 5%. Some categories of market data are growing by 10% or more.

Against this backdrop, market data spending is clearly on a roll – but at the same time, the vast majority select their data providers based on overall data quality rather than price. The most valuable data is transaction data – if it is aggregated and standardised it can be used to provide analytics and insights that tell banks what they need to act on and when. It is your data – just use it correctly across the organization with no additional data fees.

3. How can AI and machine learning provide the insights to drive more and higher quality transactions?

FMIs and other large financial institutions sit on a wealth of untapped data from the markets they facilitate, but typically struggle to convert this raw unstructured information into actionable insights that put the data into a standardised format and contextualise it to provide true market colour.

By applying data models and AI and machine learning to comprehensive data sets that are reflective of entire market activity, a new class of data can be delivered that provides an unprecedented level of insight.

4. How can data be leveraged to see where the herd is trading and then where the opportunity is for alpha?

Leveraging comprehensive, high quality transaction data from best-in-class providers and running it through proven data models gives participants the insights at their fingertips to truly understand market behaviour and make more informed trading decisions.

The result for FICC market participants is access to a highly intuitive, user-friendly platform that unlocks new depths of understanding in the markets, products and instruments of their choice. With a clear picture of the market, they can quickly identify where their peer group is trading and where the opportunities for alpha lies for their clients.

5. Once banks have found liquidity, how can this technology help them personalise the service they offer clients?

In a world where we increasingly expect personalisation of digital services in our consumer lives – for example recommendations by Netflix on which movies we might enjoy – clients are also demanding greater personalisation in the enterprise space.

Whereas banks typically looked at their clients by segment and tailored the information they provided to them in this manner, they are now beginning to use AI to look at each client as their own segment, and hyper-personalising the insights and service they provide them.

This is optimised with natural language generation technology, which delivers reports such as multi-asset morning briefings in a human tone of voice with easy-to-interpret analytics. The result for the bank is increased loyalty and greater share of mind amongst clients. A client recently told us that since deploying our platform, their sales team had made 20% more calls, has 22% longer conversations with clients, and this had resulted in significantly more volume.

In addition, AI-driven insights can point to the biggest opportunities with your clients to ensure you add value to their trading strategy and are able to take proactive action to keep their business. Salespeople no longer have to wait for the call – they can make the call with very valuable insights and recommendations.