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The need for standardization in the European private debt market

Why does the European private debt market need a standardized model? In this article, we will explore the challenges of operating inside the private debt market and how technology, and in particular a standardized model, can help to overcome them. 

Billions of euros in private debt market transactions are managed using spreadsheets, word documents, and PDFs. Deal evaluation, pricing, reporting, and monitoring are all processes that are frequently carried out in Excel. This exposes the market to substantial operational risks. In addition, the difficulty in managing, cleaning, and leveraging this unstructured data significantly increases the amount of time it takes to close deals.

Challenges and opportunities of the private debt market

Private debt has become a very appealing alternative for fixed-income investors. Some of its attractive features are:

  • Higher yield prospects
  • Low volatility
  • Low correlation with other asset classes
  • Lack of market-to-market risk
  • Floating rate of the loans

However, the European Private Debt Market its a challenge: it is not standardized, it is illiquid, and it is very fragmented.

More funds and fewer alternatives to invest in have also increased overall competition. As a result, it’s very complex to operate in the market. This makes it difficult for private e-debt investors to move with speed and precision. Many try to solve the problem by increasing resources, and hiring more risk managers and operational staff, with scarce results.

The need for technology in the private debt market

The bytes of data created every day worldwide are at 2.5 quintillions. Private debt managers cannot miss the chance to identify better investment opportunities, structure better deals, and monitor closely their portfolios. 

Specialized tools could help automatize most of the repetitive tasks and embed them within the decision-making process with more data and better models.

In the healthcare industry, big data helps avoid preventable diseases by detecting them in their early stages. In a similar way, data could be immensely useful in the private debt market to predict different patterns of behavior before an event happens and act accordingly. Technology can also aid in recognizing illegal activities such as money laundering or fraud cases.

How can we do that? Private debt investors should extract real-time data from many sources that can actually serve their investment strategies. However, data and models in Europe are very local. Every geography has its format, and frequency, and typically follows different behaviors.

Is a standardized model the answer?

But if the process, data, and credit risk models could be standardized, then in relative terms, there is the potential to triple the size of the market.

Creating a level playing field across different geographies would enable all credit lenders, originators, and service providers to make decisions based on the same level of information. All actors could scale faster.

New models could help with this objective by using new data sources such as:

  • Credit pricing at benchmarked sector/geography/comparable company level
  • Cash flows and transactions history
  • Insurance instruments
  • Alternative data from the company’s web footprint.

Cardo AI’s technology

Our data scientists at Cardo AI are working on creating a standardized model. They are doing this by pulling real-time data from 100+ different private debt data sources from 25+ different European countries.

The objective is to optimize and improve the process of sourcing new investment opportunities, evaluating and managing better the risk involved with the available investments, and most importantly providing a framework to anticipate potential losses and better handle portfolio returns. 

Aggregating and managing different sources of data are the starting points. Currently, our technology covers the entire data management process: data sourcing and treatment, data standardization, and data modeling. 

The benefits and risks of a standardized model

By using a process like the one outlined above, it’s possible to evaluate and monitor deals not only on a single loan/debtor level but also on an overall fund level and at an asset management level. 

The opportunity to move the analysis at different levels can help the private debt investor to calculate the concentration, impact, and potential contamination arising from every exposure and their relevant connections. 

This is essential for a proactive portfolio monitoring strategy or a dislocation strategy, especially in times of potential economic crises or black swan events. 

Overall, this standardized approach would increase assets under management, reduce due diligence times without creating additional risk and result in more deals and better options for investors.

Let’s place the potential of using this model into context. The US (standardized) private debt market was valued at $412 billion in December 2020. It is now on track to achieve a market size of $600 billion by December 2022. The European market was valued at $264 billion – just over half of the US – in December 2020. If process data and credit risk models become standardized across Europe, the market has the potential to triple its size and become worth as much as, or even more than the US.

Risks of a standardized market

For all the pros, there is admittedly one downside. The main con to a standardized market is the decreased flexibility in offering custom-designed risk metrics and products that could specifically fit the needs of local markets.

However, It is clear how the pros here far outweigh this concern, particularly if Europe wants to maintain its status as an appealing global market for private debt investment in the longer term.

Want to know more about how we are revolutionizing the private debt market? Discover our products and solutions here!