A financial innovation enthusiast, Christhi Theiss advises financial institutions and fintechs within the capital markets and lending sphere. As former Head of Europe in Capital Markets, including roles at RBS, Santander, and Societe Generale, he combines over a decade in investment banking with a global entrepreneurial fintech trajectory. An Expert Advisor to the European Commission on Innovation, he is passionate about new business models which are reshaping our daily lives.
Making money the “old way” is getting tough in capital markets. Even large and savvy financial players have seen profits shrink. Regulators are pushing for transparency and raising their expectations of good conduct, with demands for the execution of financial products on electronic platforms and the disclosure of profit margins following each trade.
The reaction among many banks has been to implement the required regulatory changes, while shifting their focus to higher margin and less regulated business segments. However, a few have sighted an opportunity to reinvent their business model so they can thrive in an era of regulatory constraints. Could it be that embracing the spirit and not just the letter of regulations – clarity on profit margins, protecting clients from financial harm, focusing on quality of service – is actually a blessing in disguise?
Although this adaptation may have been forced, banks that truly listen to and understand their customers will inevitably form closer relationships. Herein lies the potential: in investment banking, a closer relationship means getting much more business from your customers.
The Impact of Broker Rankings
The opportunity is pretty simple. Capital markets managers know that their desks see only a tiny bit of each customer’s overall business. The proportion they receive is based, in large part, on each customer’s internal broker ranking. In my experience, an asset manager’s top 5 banks get a whopping 70% of his or her overall business. The rest must fight over the “crumbs” that remain. Admittedly these are multi-million dollar crumbs, but this uneven distribution creates an opportunity that contenders can exploit.
If a bank can achieve a one-step increase in a customer’s broker ranking, let’s say from 11th to 10th position, that will typically have a multiplying effect of about x3 on the volume of business. Any desk head would give their right arm for a technique that triples their business, but there are no tricks at play here or anything that will upset the regulators. Broker rankings make customer service an investment that has a calculable (and potentially lucrative) business impact.
The challenge is how to systematically influence this external grading. Few leading banks will find low-hanging fruit when they look to make customer service improvements. If you’re leading a capital markets business, I would expect you to argue that your department is already highly customer-oriented. Perhaps you’ve formalized customer service values, maybe you survey customers to gauge their opinion. Far be it from me to disagree, but ask yourself… how much can you really know about what your customers think?
How to Spot a Complaint
I ran client-facing businesses for over a decade and, if I’m honest, my understanding of each customer’s level of satisfaction as a whole was quite vague. Caring about it was not the issue; the difficulty was how to obtain accurate information. Take any mid-size asset manager for example. Just in their Fixed Income department there usually are more than 20 employees, including portfolio managers, analysts, management and back office staff. They are in constant communication with over 20 counterparts at the bank. And that’s just one stream of interactions taking place in Fixed Income. The same customer will talk with several other departments. It adds up to thousands of phone conversations, emails, chats and other messages. Millions of occasions where the customer might mention a problem, but no way of knowing if that had happened and how my bank had responded.
What I lacked was a reliable means of capturing and objectively measuring what is revealed in customer communications. Anyone who was seriously dissatisfied would most likely make their feelings known, but what about the many grumbles and gripes that individually appear to be trivial but collectively describe a customer relationship that is deteriorating?
If I took the helm of a capital markets department today there would be no need for me or the business to suffer this lack of insight. Advances in technology mean that unstructured communications data – the language captured in myriad emails, Bloomberg chats, documents, and phone calls – can now be identified with unprecedented precision and perception. Reviewing every customer interaction and discerning the pieces of language that suggest a complaint would once have been completely impractical and unaffordable. Now this task can be automated.
AI-Enabled Communications Analytics
This remarkable revolution has been made possible by artificial intelligence. Disabuse yourself of AI’s hyped promises; I’m talking about proven technology already in widespread use by major banks. Applied to capital markets, it puts in scope data that was once considered too difficult to analyze. Avoiding or mitigating customer complaints is one of the primary use cases and is rapidly becoming a focus for management.
AI delivers extraordinary insight into customer satisfaction. Envision a real-time, aggregated cross-departmental analysis that encompasses all actors within the bank – sales, trading, syndicate, origination, analysts, back office personnel, etc. Unlike keyword analytics, a technique far too inaccurate for large volumes of communications data, this next generation approach makes sense of language in context. It adapts to the nuances and variations that are inherent to normal human expression. Soft complaints that are easily missed – perhaps overlooked by a busy salesperson or ignored because they were voiced to the wrong department – will be recognized, categorized, and scored for management’s attention.
Let’s consider a soft complaint about pricing as an example. A portfolio manager at a big asset management company mentions to her bank’s relationship manager that, among several other things, she would like to see better prices. The same day a salesperson gets a chat message from a different portfolio manager saying that he lost a trade due uncompetitive pricing. Since it was just one trade the salesperson doesn’t give it much importance. These soft complaints are scattered through interactions with the bank’s front office staff, but there is no holistic view. So, the subpar pricing goes on, the portfolio managers bring less business to the bank, and eventually the asset management firm downgrades the bank in their broker ranking. The result is a 60% fall in business, which happens overnight! And this costly, shock loss of trade occurs with seemingly no warnings or forceful complaints.
Forewarned is Forearmed
Banks and their capital markets desks that adopt AI gain a de-facto early warning system that prevents this kind of scenario. They can detect not just the existence of complaints but also their substance: issues with pricing, settlement, coverage, new issues allocation, you name it. They can track which customer raised the issue, to whom, the severity, the frequency, the broader context. These insights are translated into patterns that can be red-flagged to all concerned desks and their management.
Awareness of emerging problems allows for the development of strategies for resolving dissatisfaction and actively influencing broker rankings. Banks can, for example, adjust their prices and track further complaint development. Relationship management and sales can go back to the portfolio manager and show that they listened to complaints and acted on them. Managers can see in real time which parts of the business work well and which need attention. As satisfaction increases and volumes edge up, the improvements are recognized through a higher broker ranking. What follows is a bigger boost to volumes and expansion of market share.
And what happens with banks reluctant to make this shift and use AI insights to manage their day-to-day business? Well, we’ve seen repeatedly what happens whenever disruptive innovation meets traditional services: the customer-centric players succeed while the rest are pushed to the edge of the field.