Why Data and Technology are Key to Gaining an Edge in Syndicated Lending
In the world of syndicated lending, accessing accurate and timely data is critical for origination and distribution teams to make meaningful and effective decisions. At the same time, the lifecycle of a loan and the activity of its participants is complex, so, unearthing, understanding, and applying this data is not always easy, especially when it sits on multiple systems. Ensuring that data is accurately reflected in documentation at all times adds to these challenges.
As loans become more complex in nature - involving ever more banks and lenders, higher transaction volumes and increasingly complex structures - it becomes vital to access and manage the relevant data in order to optimise deal outcome.
Technology plays two key roles in helping deal teams leverage data to maximum effect: Firstly, it allows us to unearth and focus-in on relevant information in each case. Secondly, it provides tools to efficiently execute key actions based on the data provided.
Unearthing Relevant Data
We live in an age where data is continuously collected. Every action and click of a button is recorded and every decision is logged. This has provided organisations across all industries with the potential to access more and better information than ever before. However, it’s not necessarily data access which is delivering the most significant advantages anymore. For most commercial organisations, the ability to sift through the enormous volumes of data to extract that which is most relevant to the current task, is far more impactful than simply having access to everything. This applies equally to syndicated loan desks.
The loan syndication industry transacts over $4.5 trillion each year. There are tens of thousands of loans executed across dozens of industries and sectors, involving hundreds of thousands of lenders. The amount of data recorded throughout this activity is immense. The majority of this information however, will be irrelevant to most syndication teams, who only want to analyse what is relevant to the deals and participants they are working with on a given deal.
The question for syndicate teams becomes, “how do we cut through the fog and access specific data which is relevant to my network and for this deal?” without needing to undertake detailed and complex analyses of huge data sets.
By using specifically designed data tools and analytics, deal management systems can unearth the most important information by analysing the relationship between two data points (for example a lender and a deal), based on behavioural patterns, historical data and user preferences. The result is that syndication teams are proactively presented with matches or even suggestions as to which combinations of participants and deals would make for the most effective and best outcomes. Not only does this give arranger banks the best chance of closing a deal on the best terms, but it allows them to do so with maximum efficiency.
Taking Action
The way in which deal teams react to and take action based on data, is just as important as uncovering the relevant information in the first place. Having insights and suggestions is a good start, but if users need to resort to disjointed, manual processes to take action, then the effectiveness of this data is diminished. Building and using technology with good logic helps deal teams capitalise on access to relevant, real-time insights.
There are two key factors in successfully deploying such technology. The first is having a deep understanding of the industry, workflows and stakeholder drivers which capture and process the data which generates the suggestions and matches. There are many data points which contribute to the matching score and it is important for the user to understand broadly which of these lender interactions have led to the system suggestion. Enhancing data analytics is an iterative process which needs to be customised to the specific industry or type of loan.
The second factor is having pre-set actions which are triggered at the press of a button. An example of this might be the ability to ‘match’ a lender to a deal and send a system invitation so the lender can view the deal. Another example is when a member of the distribution team wants to syndicate a loan opportunity to a joint-bookrunner with an automated email template detailing key information. In both cases, having access to these pre-set actions instantly accelerates an entire workflow.
Uncovering and actioning relevant data, arms loan syndication teams with a far more efficient and scientific way of making decisions and executing deals. Having a platform which supports these technologies and data functions, provides a significant competitive advantage for those willing to evolve from their existing systems and processes.
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