AI could help triple the European private debt market | Features

But if European private debt data and credit risk models were to converge, the market could triple in size and even overtake the United States. That’s the view of Altin Kadareja, who co-founded and runs Cardo AI, an investment infrastructure technology company that uses artificial intelligence (AI) to analyze private markets.

Data is essential

Private debt investors face more challenges than public debt investors when it comes to accessing the right data to make informed investment decisions. Overcoming these challenges becomes even more important with the growing emphasis on integrating ESG criteria. Can new data sources be helpful in this regard?

Kadareja has a clear vision of the way forward. Integrating and analyzing data from many sources is essential for private debt investors. Their goals are to protect the downside with better credit risk models and real-time monitoring and to maximize the upside when trading investments through better price insights from data analytics.

The integration of ESG criteria brings a new dimension for institutional investors. To do this, Kadareja argues, there are a number of steps. The first is to agree on the main strategic investment objectives. The second is to set ESG goals that can be monitored to determine how far away the investor is. “If you want to reduce the carbon footprint of your private debt portfolio by 30% by 2030, that would be a clear, measurable and direct goal,” he said. The third is to structure a strategy to achieve the goals.

Creating such a strategy, as Kadareja argues, first requires a policy to identify the objectives, strategy and responsibilities for integrating ESG criteria into the organization. Targets must comply with the legal and regulatory regimes in which an investor, such as a pension fund, operates.

Avoiding “greenwashing” by fund managers is a challenge. A simple methodology would be to issue a questionnaire to investment managers and hold face-to-face meetings, as with any 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 taken on board by fund managers and to compare managers before and after agreeing to an investment mandate. ‘investment.

Another idea is the creation of an incentive system for fund managers, directly impacting performance fees. Examples could include failure to meet minimum ESG-related targets, such as meeting SFDR Article 8 or reducing a portfolio’s carbon footprint. Conversely, achieving an objective, such as compliance with article 9 of the SFDR or exceeding a carbon reduction objective, could lead to an increase in performance fees.

One issue raised by Kadareja is the different way asset owners and asset managers perceive ESG factors. “Asset owners face the same issues as philanthropic donors with respect to lack of data, as well as lack of standardized process, because even the regulatory regimes are not very clear.”

The EU taxonomy was due to be delivered by December 2021. The fact that it had to be postponed highlights the difficulties faced by regulators in creating acceptable regulatory requirements for market standardization.

“We have done some EU taxonomy alignment calculations exercises for our clients which have been very painful. We have asked many ESG rating providers and still have not been able to complete exercise in a perfect way.” While organizations that are familiar with the ESG landscape and have the technology and resources to do so find it very difficult, it will be more difficult for pension funds, for whom it is not their primary objective.

“We did some EU taxonomy alignment calculation exercises, which were very tedious. We asked many ESG rating providers and still couldn’t execute the whole exercise perfectly. »

Altin Kadareja

For investors, financial statements updated every 12 months are too infrequent to be of immediate value. At this point, the data is too old for well-informed decision-making. One solution could be to grant tax breaks, similar to R&D tax incentives, for more frequent financial statements, on a half-yearly or even quarterly basis.

However, as Kadareja points out, European private debt investors should be able to extract real-time data from a range of sources. But in Europe, this comes with challenges.

room for growth

In most cases, the 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 at all financial institutions. As a result, data is error-prone and saved in suboptimal formats.

Despite the challenges, if processes, data and models across European markets could be standardized, Kadareja believes it would be possible to triple the size of the private debt market. A standardized approach would allow all lenders, originators and service providers to make decisions based on the same level of information; all players would be able to scale up faster.

New models could help achieve this goal by combining traditional data, such as borrowers’ financial statements, performance indicators, credit histories, and credit curve compositions, with new data sources, such as credit pricing references, insurance data, real-time banking or supply chain. cash flow analysis. Alternative borrower online footprint data could include real-time employee organizational sentiment, product or service reviews, market news, and technology readiness data.

This would help not only to assess and monitor transactions at the single loan or obligor 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.

According to Kadareja, this is essential for proactive portfolio construction and monitoring, especially in times of potential economic crisis. This 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 holds great promise if consensus can be reached on what matters and what can be measured. New data and analysis should enable investors to make informed decisions that reflect their goals beyond pure risk and return.

Joseph Mariathasan is editor of IPE and director of GIST Advisory

Mary I. Bruner