Why every credit investor now needs a “Credit Data Strategy”

Welcome to the first of our Credit Data Strategy blog series.

Based directly on conversations with our clients and other market participants, this special blog series will spotlight how the credit market is leveraging technology and automation to unlock greater performance. Whether your firm has already implemented a credit data strategy or is yet to begin, this series will help you benchmark your practices and offer resources and advice on how to succeed in an ever-shifting landscape.

Introduction

Over the past three years, I’ve spoken with senior management at more than 300 global asset managers and hedge funds across Europe and the US. These conversations have revealed both excitement and uncertainty about the near-term impact of Artificial Intelligence (AI) on credit investing.

However, many organizations have been slow to adapt, uncertain about how this emerging technology fits into their traditional credit research process. This has created a clear divide between credit firms with a defined “credit data strategy” and those without. For the latter, which still makes up the majority of the industry, the key question is how best to start positioning for the inevitable changes coming in the near future.

As AI rapidly advances, credit investors who fail to leverage its potential will find it increasingly difficult to compete with their more agile peers in terms of performance and AUM. In an ever-more-digitalized credit market, sooner than most market participants expect, the industry will be characterized by the following: 

• corporate disclosure will be read by machines, and summary analysis written by machines
• credit metrics used to evaluate credit risk will be automatically generated
• credit instruments will be valued by real-time fair value pricing systems
• systematic credit strategies will grow as a % of total credit market
• algo market-making will expand as a % of total sell-side volumes
• electronic execution will become more commonplace down the ratings spectrum
• new issues will price faster and be less resource intensive
• LPs will diligence firms’ tech infrastructure and data sophistication, allocating funds based on relative strength in these areas
• investment professionals (analysts, traders, and PMs) will demand best-in-class data systems and associated analytics as part of their day-to-day toolkit, and this will become a differentiator in recruiting top-talent

If these developments aren’t part of your central business planning today, this report is for you. 

"Many organizations have been slow to adapt, uncertain about how this emerging technology fits into their traditional credit research process. This has created a clear divide between credit firms with a defined “credit data strategy” and those without."

As we’ve stated previously, while AI won’t replace human credit analysts anytime soon, credit analysts using AI will quickly replace those who don’t. And by the end of this decade, the credit industry will be split between technology “haves” and “have-nots”, determined by the strategic decisions and investments being made currently. So, how should you start tackling this challenge?

Applying AI to make data-driven decisions, automate tasks, and extract real-time insights depends on the availability of high-quality structured data. Structured data refers to organized, formatted information that is easily searchable and processed by algorithms or computer systems, typically stored in databases with defined fields and schemas. In contrast, unstructured data—like individualized spreadsheets with inconsistent labels, formats, and data types—is difficult to query and process, making it unsuitable for scalable, automated tasks.

For this reason, we believe the best way for most credit firms to start positioning for the market’s digital evolution is to define a suitable “credit data strategy”, including:

1. a clearly defined approach outlining how structured data will be utilized within one’s investment business, stating specific targets;
   
2. a top-down mandate empowering investment professionals to pursue analytical innovation actively in their business;
3. the required data sourcing supported by the necessary technical personnel and infrastructure to achieve their stated targets; and
4. funding commitments from management that facilitate success with (a) through (c) above.

The overarching goal of this credit data strategy should be firm-specific, but most often cover some combination of performance and efficiency gains across the business. Combined, these benefits should have a transformational impact on a firm’s credit investing operations:

To support this process, we’ve organized our thoughts on this topic as follows:

• How advanced is your firm’s credit data operation today?
• Why so many credit investors lack a credit data strategy
• The ascendance of data and automation outside of credit markets
• Advantages of implementing a credit data strategy
• Key initial requirements for a successful implementation
• Five examples showcasing current best-in-class
• Conclusion

Coming up: Credit Data Operations Checklist

Over the next few weeks, we'll address each of the topics above. In our second piece, we'll provide our own Credit Data Operations Checklist designed specifically to help you evaluate how your firm stacks up against its peers in this area and offer guidance for those unsure of what to do next. 

As always, Cognitive Credit would be pleased to support you through this process, so please contact us if we can be of further assistance.

Next: How advanced is your firm's credit data strategy?