Welcome to the third part 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.
The reasons why firms don't yet have a credit data strategy
When we’ve met with investment firms without a defined credit data strategy, several reasons have been most common. These each highlight the challenges of adapting to change and the need for senior management to provide the necessary support for their business to overcome entrenched mindsets and ways of working.
1. Time constraints
Time constraints plague most investment professionals, leaving little room for strategic thinking. With daily demands dictating priorities, there’s often no designated mandate or bandwidth to delve into critical strategy matters outside of a near-term horizon.
2. Lack of awareness
There’s a pervasive lack of awareness regarding what’s possible today in terms of deploying advanced analytics and AI in credit. Unlike their counterparts in other more quantitatively-oriented asset classes, credit investors often lack the perspective to define realistic technology priorities and/or how to transition to a modern data-oriented approach in a
low-risk way. This knowledge gap presents a significant barrier to
innovation adoption.
3. The status quo
A stubborn adherence to the status quo further impedes progress. Many professionals cling to outdated processes simply because “that’s the way it’s always been done” or they do not want to “rock the boat” with colleagues. Such bias stifles innovation, especially in the credit market, where so much of the core work has been done in an entirely manual way for as long as anyone can remember.
4. Budget pressures
Budget pressures can present a formidable challenge. Amid lingering fee pressures in the industry, allocating resources to develop a robust credit data strategy may not always seem feasible or a top priority, so it continually gets deferred to “next year”.
It would not be surprising if you and/or your firm are struggling with one or more of the above, as this is common across the industry today. Yet none of these blockers are insurmountable. In most cases, a mandate to change is best communicated by the firm’s leadership, ensuring that at least one person within the relevant business unit is empowered to think through necessary changes in the business and/or allocate additional budget to in-house innovation projects.
While it will always be easier to do nothing, the best practices that we describe below do not need to be budget-breaking or disruptive, and the returns of successful implementation are almost always year one accretive. Rather, it is delayed prioritization where the cost is now too great for firms not to act.
Providing credit investors with high quality, out-of-the-box structured data and empowering these organizations to achieve their credit data strategies is precisely why Cognitive Credit was founded. Our expansive coverage and functionality, custom-designed for credit analysts, requires minimal change from users and fits seamlessly into existing workflows, easing the burden of technological transformation for our clients.
Coming up: Data & automation outside of credit markets
Next week, we look at how other major asset classes (equities, FX, rates, etc.) have been dramatically transformed by data and automation.
As always, if you'd like to discuss your own strategy, contact us if we can be of further assistance.