HFM spoke with Portfolio BI CEO Jonathan White following our win in the best analytics tool category at the recent US services awards, focusing on the trends, challenges and impact of AI on Private Credit.
Given the current refinancing and liquidity pressures in private credit markets, where do you see the biggest challenges for managers in terms of data transparency, risk signal capture, and decision-making?
It all starts with the underlying data architecture across the multiple functions within an organization. One of the biggest challenges private credit managers face is operating across disparate systems and vendors, while trying to align the front office, operations, back office, and IR/Marketing teams.
Investors are very much in the driving seat today. They expect fast, often ad hoc responses to detailed portfolio and deal-level risk questions. Without an integrated data foundation, that leads to fragmented workflows, manual interventions, and highly skilled professionals spending time on what is essentially busy work. The reality is that managers struggle to carve out the time needed to properly invest in their data architecture.
Siloed and opaque data structures are also a key focus of operational due diligence. Investors want to know: does the manager truly have visibility into risk? Can they identify risk signals early and make informed decisions based on them?
The industry’s traditional “heavy lift” approach to data is no longer sustainable. There is a clear shift toward institutionalization — embedding structured data workflows and enrichment into a straight-through processing model, powered by technology. That’s becoming essential, not optional.
What trends are you seeing in how firms use analytics and data to justify pricing and monitor stress in the current market?
The focus is increasingly on effective portfolio management supported by enriched data from the very start of its lifecycle. If you don’t capture the right data attributes at origination, it becomes significantly harder to justify pricing decisions or monitor stress down the line.
In direct lending, which is where many of the current industry challenges sit, managers are dealing with large
volumes of unstructured data, deal terms, covenants, pipeline opportunities, and even prospective transactions they are monitoring. Extracting meaningful, structured insights from that environment is complex.
It’s less about real-time data in the traditional market sense and more about quarterly financial reporting cycles, with an emphasis on identifying stress signals between reporting periods, both at the individual deal level and at a broader macro level.
Another challenge is that there are still relatively few truly aligned solutions built specifically around private credit workflows. Managers need configurable systems that reflect the nuances of their business model rather than generic platforms that require excessive workarounds.
Which AI-driven capabilities are delivering tangible value today, and how do you see them reshaping traditional data workflows?
AI is widely viewed as a game-changing initiative, and it can be. But without solving the underlying data architecture challenge, many AI projects end up shelved. AI is only as strong as the data foundation beneath it.
So far, we’re seeing the most widespread adoption in IR and Marketing, particularly in areas that involve high
volumes of document creation and reporting.
More broadly, firms need to embrace the concept of the “human in the loop.” The trust quotient is very real.
Agentic AI workflows must highlight and preserve human decision-making at critical intersections. AI is not a 100% infallible solution, despite the perception that it might be.
Where we are seeing meaningful traction is in AI tools that enable limited but targeted human intervention. For example, pipeline transparency, deal attribute enrichment, and automating data capture at the point of trade order management. When data management tools are implemented with a whole-of-business mindset, they create a structured view of portfolio data that can then be queried using generative AI tools. That democratizes
access to data across the organization.
However, the question inevitably becomes: “How do I know it’s right?” Building trust in these systems is just as
important as deploying them.
What are the biggest workflow or integration bottlenecks you still see in private credit operations, and how should firms prioritize technology and governance investments?
Pipeline management, deal data enrichment, and trade order management remain significant bottlenecks.
Adoption of traditional front-office technologies in private credit is still relatively low, largely because of data ingestion challenges. If you can’t reliably and efficiently ingest and structure data at the beginning of the process, the rest of the workflow becomes fragmented.
Firms should prioritize investments that strengthen governance and establish clean, structured data flows at origination. Once that foundation is in place, resilience follows. Without it, every subsequent technology initiative becomes harder and more expensive to implement effectively.
Looking ahead, how do you see the role of technology providers evolving as investors demand greater transparency, standardization,and AI-enabled insight?
Private credit is a complex asset class from a data workflow perspective. In private markets more broadly, the daily cadence of data inflows, loan administration, cash flows and accruals, is fundamentally different from the traditional private equity model. Technology providers must understand those nuances.
Going forward, providers need to be able to integrate data across multiple platforms, sources, vendors, and service relationships. At the same time, they must build trust and education around AI-enabled solutions, focusing on improving portfolio monitoring outcomes in collaboration with administrators and other ecosystem partners.
Flexibility in the engagement model is critical. Many managers are operating within the constraints of legacy technology decisions. Providers need to accommodate that reality.
There are many “flavors” of private credit, and every manager differentiates themselves in how they structure and run their business. That means configurability is key, not heavy customization, but flexible, adaptable solutions that align with each manager’s operating model.
About PBI
Portfolio BI (PBI) provides front-to-back-office investment management software and public cloud infrastructure services to the global investment management industry. PBI’s flagship products and services, PBI Axiom Nova, PBI Vector, and PBI Stratus, enable alternative asset managers to address their data challenges in analytics, workflow, governance, and security. Over 175 top-tier hedge funds, asset managers, funds of funds, and institutional investors have trusted PBI’s technology for over 20 years.
For more information, visit www.portfoliobi.com.
