Investors seeking higher yield have driven the growth of the private debt market. European private debt, although still much smaller than the US market, has also been growing rapidly. European lenders managed assets of $350bn as of June last year, according to Preqin, in a total market of $1.19trn. This is more than double the level in December 2016.
But if European private debt data and credit risk models were to converge, the market could triple its size and even overtake the US. This is the view of Altin Kadareja, who co-founded and runs Cardo AI, an investment infrastructure technology firm that uses artificial intelligence (AI) to analyse private markets.
Data is critical
Private debt investors face more challenges than those investing in public debt when it comes to gaining access to the right data to make informed investment decisions. Overcoming these challenges is becoming even more important with the increasing focus on incorporating ESG criteria. Can new sources of data be of value in this regard?
Kadareja has a clear vision for a way forward. Incorporating and analysing data from numerous sources is critical for private debt investors. Their objectives are to protect the downside with better credit risk models and real-time monitoring and maximise the upside when negotiating investments through better pricing insights from data analysis.
Incorporating ESG criteria brings in a new dimension for asset owners. To do this, Kadareja argues that there are a number of steps. The first is to agree what the key strategic investment goals are. The second is to set ESG targets that can be monitored to determine how far away the investor is. “If you want to reduce your private debt portfolio carbon footprint by say 30% by 2030, that would be a clear, measurable and direct target,” he says. The third is to structure a strategy to reach the targets.
Creating such a strategy, as Kadareja argues, first requires a policy to identify objectives, strategy and responsibilities for embedding ESG criteria into the organisation. Targets need to respect the legal and regulatory regimes in which an investor, such as a pension fund, operates.
Avoiding ‘greenwashing’ by fund managers is a challenge. A low-effort methodology would be to deliver a questionnaire for investment managers and hold in-person meetings, as with every due diligence process.
A more proactive way would be to use technology to give end investors access to detailed data on how ESG KPIs have been embedded by the fund managers and to compare managers before and after agreeing on an investment mandate.
Another idea is the creation of an incentive system for fund managers, directly impacting performance fees. Examples could include failing to meet minimum ESG-related targets, such as complying with article 8 of SFDR or reducing a portfolio’s carbon footprint. Conversely, reaching an objective, such as compliance with article 9 of SFDR or exceeding a carbon reduction target, could result in an increase in performance fees.
A problem Kadareja raises is the different ways asset owners and asset managers view ESG factors. “Asset owners are finding the same problems that philanthropic donors have with respect to the lack of data, as well as the lack of standardised process because even regulatory regimes are not very clear.”
The EU taxonomy was supposed to be delivered by December 2021. The fact it had to be postponed highlights the challenges that regulatory bodies have in creating acceptable market standardisation regulatory requirements.
“We have run a few exercises of EU taxonomy alignment calculations for our clients which have been very painful. We asked a lot of ESG rating providers and still could not manage to run the whole exercise in a perfect way.” If organisations that know the ESG landscape well and have the necessary technology and resources find it very difficult, it will be harder for pension funds, for whom this is not their main objective.
“We have run a few exercises of EU taxonomy alignment calculations, which have been very painful. We asked a lot of ESG rating providers and still could not run the whole exercise in a perfect way”
For investors, financial statements updated every 12 months are too infrequent to be of much immediate value. By that point the data is too old for well-informed decision-making. A solution could be to provide tax breaks, similar to R&D fiscal incentives, for more frequent financial statements, on a bi-annual or even quarterly basis.
However, as Kadareja points out, European private debt investors should be able to pull in real-time data from a range of sources. But in Europe this comes with challenges.
Room for growth
In most cases, data is unstructured and needs to be formatted. Another challenge is the lack of consistency with data communication protocols. Connectors and APIs are not yet available across financial institutions. As a result, data is prone to errors and saved in suboptimal formats.
Despite the challenges, if process, data and models across European markets could be standardised, Kadareja believes it would be possible to triple the size of the private debt market. A standardised approach would enable all credit lenders, originators and service providers to make decisions based on the same level of information; all actors would be in a position to scale up faster.
New models could help achieve this by combining traditional data, such as borrowers’ financial statements, performance indicators, credit history and credit curve compositions, with new data sources, such as credit pricing benchmarks, insurance data, real-time banking transactions or supply chain cash-flow analysis. Alternative data from borrowers’ online footprint could include real-time organisational sentiment from employees, product or service reviews, market news and technology readiness data.
This would help not only to evaluate and monitor deals on a single loan or debtor level, but also at the overall fund level. Investors could more easily calculate the concentration, impact and potential contamination arising from any relevant connection to the borrower.
This, Kadareja argues, is key to proactive portfolio construction and monitoring, especially in times of potential economic crisis. It would reduce due diligence times without creating additional risk, resulting in more deals and better options.
The growth of data platforms and new sources of ESG and impact metrics hold great promise if some consensus can be achieved on what is important and what can be measured. New data and analyses should enable investors to make informed decisions that reflect their objectives beyond pure risk and return.
Joseph Mariathasan is a contributing editor to IPE and a director of GIST Advisory