Tell us about the portfolio monitoring team at Hayfin.

As investors’ expectations around transparency grow, LPs are demanding more timely, standardised and high-quality data from GPs. It’s essential to have the infrastructure and resource to support bespoke requirements, as LPs increasingly seek to have consistent reporting from all of their GPs.

Further, with increasing geopolitical and macro-economic uncertainty, the need to be front-footed in monitoring portfolio health has never been greater. Our dedicated team has brought efficiency and streamlining to the reporting, ratings, monitoring and valuations processes, which allow for proactive portfolio management and early intervention.

 I joined the firm last year to build out and lead this team. This was all part of a wider drive to invest in the quality of service we can offer to our growing LP base globally, at a time when institutional asset allocation to European private credit remains on the rise.

Our team is making real progress in the kind of insights we can extract from our portfolio companies’ underlying data. This has entailed a substantial investment of the team’s time and resource in structuring, standardising and analysing our portfolio data.  Even within the past year, we’ve grown the portfolio monitoring team from two to eight with 2 more joining before the end of the year.

Why is portfolio monitoring important to LPs and what can they expect to gain from it?

For LPs, a portfolio monitoring function is essential for their managers to provide detailed reporting, proactive performance monitoring and value preservation.

Data granularity and enhanced analytic capabilities offer a range of benefits. We can exert better oversight of the portfolio, not only to allow for early engagement with management teams and sponsors, but also to inform future investment decisions, by using our insights and learnings for underwriting and portfolio construction and providing additional, contextual information to aid decision-making.

One example of enhanced analytic capabilities is our early warning indicators, which are fundamental to how we now review the portfolio and prioritise individual deals. This tool has been automated and will soon be available across teams.

Similarly, we were proactive in understanding how PIK-toggles impacted fund-level performance, and also in assessing first and second-order impacts from tariffs to the portfolio.

Through tools like this we believe we can be better partners – to both our borrowers and our LPs. Being able to identify red flags early enables us to engage with borrowers to protect value. Meanwhile, we give LPs the means to assess performance and make informed decisions about their allocations, as well as speeding up their due diligence processes on new fund allocations.

What makes data analysis so complex in private credit?

There are a few structural factors which help explain why the private credit industry has been a relatively slow adopter of automation and big data analytics.

The industry is relatively young, having only emerged in Europe post-crisis. As lenders, you rely on your borrowers to provide high-quality data, and you don’t necessarily have the same levers to pull as the shareholders to compel them to do so. That all impacts the size and quality of the dataset.

With over €50 billion invested in more than 500 companies over 15 years, Hayfin has built a proprietary data bank that supports differentiated and data-driven insights in Europe. We have long requested high-quality, consistent information from our portfolio companies to enable performance tracking. We believe this is now a key differentiator for our private credit platform.

We are now grappling with almost the opposite challenge. Like other large alternative asset managers, we deal with a high volume of data from a variety of investments which is received in many different formats, frequently changing over time and often multi-lingual. That requires us to standardise data from multiple unstructured sources. The quantity of data we receive is growing so cracking the standardisation problem isn’t just about cleaning up data; it’s about gaining a real competitive edge. We believe the firms that can collect, structure, analyse and share this information the fastest and most consistently, will be the preferred choice for asset owners and investors.

One additional challenge is that, increasingly, LPs want reporting delivered in a consistent format, often tailored to their internal systems. That puts pressure on GPs to evolve their processes and technology infrastructure to accommodate those requests.

What does the future hold for portfolio monitoring?

It almost seems too cliché to mention at this point, but we believe that elements of portfolio monitoring will centre around the intelligent use of generative AI. The industry is relatively early in its AI journey, but the pace of improvement and adoption will only accelerate.

The tricky nature of unstructured portfolio data has somewhat slowed the industry’s adoption of advanced data techniques compared to other sectors, but AI is already showing promise in streamlining underwriting, data analysis and deal logistics.  

It’s important as an industry that we don’t rush the integration of generative AI into portfolio monitoring, as these GenAI models are often not sufficiently accurate to be fully relied upon. AI should supplement, but never replace, the human judgment, governance and validation that must remain central to our investment activities and portfolio monitoring.

Over time, I expect AI will help the industry resolve pervasive issues related to standardising performance tracking, as well as helping to detect anomalies and incorporate alternative data sources to flag emerging risks. But handling sensitive borrower data requires purpose-built tools and robust infrastructure, which will no doubt take time to perfect.