Author: Marco Masotto

Data leverage and securitizations

From leveraging data to DATA LEVERAGE

What is data leverage and how can you use this concept to accelerate your business growth? Read this article to find out more about the process of leveraging data.

Imagine being a bank, an asset manager, or an investment fund with a well-established franchise in the loan industry that wants to expand its activity by tapping into new markets, products or geography. You would have quite a large database covering your exciting activity in terms of both clients and loans.

But what about a new market you want to target? Do you need to start the activity with a blind eye and collect information from scratch putting your capital at stake and waiting for months or years before having a solid base of data that can help you succeed?

Is there an alternative way?

So far financial institutions have been very vigilant in optimizing the use of their capital: from regulatory capital for banks to equity capital for more unregulated entities the key target is to extract the highest return. The most common way to increase the return of available capital is through leverage: gaining access to extra resources allows to amplify ROE.

What is data leverage

During this time the concept of leverage has been extended to other fields, among which data. The expression “leveraging data” indicates the process to turn raw information into valuable actionable insights.

Leveraging available information is a good practice and companies should take advantage to make data-driven decisions at a strategic and operational level.

Is this all?

What if instead of just maximizing the use of available data, one could actually increase the amount of data available?

I am not talking about expanding the information available via data enrichment or the use of so-called alternative data (which have quite a hype at the moment) as they would only give more dimensions to look at but not increase the actual data available.

Neither am I talking about buying external datasets.

Data leverage and securitizations

I guess it’s about time I get to the point…

Have you ever thought to apply the concept of leverage to data? Not in terms of leveraging available data but actually increasing the data available through an approach that literally mimics the concept of financial leverage: apply a multiplier on the data a financial institution has available.

The concept is very simple: every time a financial institution extends a loan to a counterparty (or buy a bond) it gets data about a single counterparty (sector, geography, rating/FICO score, financial ratios, income, etc.) plus we can track the performance of the transaction over time (delays, renegotiation, defaults, recoveries, etc.). To increase the datapoint available, the financial institution should increase the number of transaction loans lent, or…..

It can invest into a SECURITIZATION!

By buying a note (or just part of it) into a securitization, investors gain exposure to the whole underlying portfolio of loans. And portfolio positions range from THOUSANDS for the smaller SME loan pools to HUNDRED OF THOUSANDS for pools made of consumer loans.

How long and what would it take to originate the same volumes in terms of time, costs, capital, organization, and all the rest? A rhetorical question really as it is not only time-consuming but resource-intensive.

All clear then? But can I just invest €1 into a AAA senior note and get loads of data?

Unfortunately, it’s not so easy…

Notwithstanding the requirements of the Securitisation – Regulation 2017/2402 gives investors the right to receive loan-level information on each transaction, however, in most cases, there is not a button (or a magic wand) that allows an investor to retrieve data in a standardized format nor in a timely manner. Data are indeed typically made available quarterly via pdf reports (or excel in the best scenarios), making it very hard for investors to translate them into meaningful and actionable information.

So data leverage is just a (nice) theory but cannot be realized in the real world?

Actually, there are solutions now!

Nowadays there are technologies, like CARDO AI that facilitate data retrieval from multiple sources with a data health check, as well as standardize them.

This process requires just a couple of clicks and takes just a few seconds (don’t even try to compare it with your excels!).

Now that I showed you how to leverage the data (which might have become BIG DATA with the appropriate multiplicator), here you have some examples on how to use them:

– Data can be used by servicers to adjust investment decisions in dynamic transactions (e.g. those with a ramp-up or reinvestment period or with a higher turnover such as trade receivables) to optimize the risk-return profile of the pool.

– They can be used by risk management departments to assess the rating of a transaction (e.g. applying scenarios deriving from data of comparable pools) or assessing the possible impact connected to particular events that affect a sector (e.g. tourism during the pandemic) or geography (e.g. after an earthquake, a flood or a particularly cold winter).

– They can be used to drive decision-making on new investments, including comparing scenarios provided by the arrangers, assess the impact of a new transaction on the diversification of the overall portfolio, negotiate a price that is more in line with the risk profile of a particular pool of loans.

– In the future, data will likely be used to assess the ESG profile of a securitization’s collateral pool and compare it with that of other transactions


What are the challenges related to the ESG incorporation in securitised products? PRI (Principles for Responsible Investment – the world’s leading advocate for sustainable investing, founded on a United Nations initiative) has recently published an interesting report on the incorporation of ESG in securitization products.

On one side regulators are increasing transparency requirements on sustainable related information on investment products. On the other, client demands and risk management are driving demands for considering the long-term impact and sustainability of investment choices. As a result of these forces, investors and asset managers are widening and improving ESG policies, but few of those are tailored for securitization products.

ESG information wanted by investors

Source: PRI

For ESG incorporation in securitized products to be effective, a holistic, multi-pronged approach needs to be developed. Compared to other asset classes the securitization market shows some additional complexity though including:

Transaction structure: which implies a multi-level assessment of practices and policies including Sponsor and/or Issuer, Originator, Servicer, Deal structure, Loans, Collaterals or Guarantees. This is further complicated by the fact that parties can occupy multiple roles (e.g. servicer and originator) or involve private entities, which tend to be less transparent.

Adequate data: Practitioners consider the ESG information in current deal documentation, marketing materials, and underlying portfolio disclosures insufficient to comprehensively analyze most securitized products.

– No ESG reporting standards for servicers/originators: Relevant ESG information on collateral often lacks uniformity and is not comprehensive.

– A diverse pool of underlying assets: the complexity and diversity of underlying collateral (and the sectors covered) make it difficult to build proprietary ESG frameworks that can be used for assessment.

– A lack of coverage by third-party ESG information providers: ESG information providers have limited coverage of securitized products. This is not surprising given that responsible investments originally developed in equities and only recently expanded to debt capital markets. Moreover, the leveraged finance market includes a high proportion of privately owned and smallcap companies that tend to disclose less information

– Lack of a clear ESG premium: differently from the so-called greenium that typically applies to green bonds, securitization transactions do not show meaningful price differentiation when incorporating ESG criteria[1]

As a result, of the 2,000 signatories that reported on their investment activities to the PRI in 2020, only 215 indicated how they incorporate ESG factors into their securitized product investments.

ESG incorporation in securitized products is at a very early stage

Source: PRI

To find a solution to the complexity above and sustain more ESG driven securitizations, PRIs have identified data quality, availability, and consistency as the main solution: a combination of robust in-house and third-party data sources is likely to drive investor confidence in ESG incorporation across securitized credit markets.

For further information please refer to the following link:

[1]Based on European ESG CLOs that were issued between March 2018 and August 2020 versus traditional CLOs

No industry for late data

The need for real time transaction data amid regulators’ requirements and investors’ need for higher transparency

Let’s picture this for a second, a world where Spotify had no real time data and used no AI and ML algorithms. You would need to send an email to the support center, indicate a list of your past songs and genres that you like, wait a couple of hours or a few days based on how busy the client support is and only then receive a recommendation for listening to a new song. Crazy, isn’t it?

This is exactly what is happening in the financial institutions. Old processes, old systems, late and old data. Today, many financial institutions continue to take decisions involving millions of Euros (if not billions) on the basis of outdated (and often inconsistent) data deriving from manual processes, usually processed using excel.

Regulators are well aware of the risk in using aged (e.g. year old financial reports) and not updated and homogenous data (coming from different sources and based on different definitions) when assessing new investment opportunities.

An example is the new definition of defaults set by the CRR (Capital Requirement Regulation). In September 2016, EBA published final guidelines on the application of Art. 178 related to the definition of default and Regulatory Technical Standards on the materiality threshold of past due credit obligation.

Paragraph 106 – Timeliness of the identification of default states that “Institutions should have effective processes that allow them to obtain the relevant information in order to identify defaults in a timely manner, and to channel the relevant information in the shortest possible time”.

This RTS does not only require a fast process but also indicates that the identification of default should be performed on a daily basis. This is becomes paramount for the industry as it requires to move processes and procedures to the next level in order to comply with this requirement.

On 1 January 2021, all of this will be real, and  credit institutions and investment firms using both IRB or Standardized approach will be required to comply with  the above.

Taking as an example the  securitization industry, credit originators or vehicle servicers report data on monthly (if not quarterly) basis using excel, or in some cases  PDF files. This requires a relevant amount of time to manipulate data (cleaning fields, merging files, linking items, standardizing output) and extract relevant information making investor constantly running behind data.

Regulators are clearly pushing the financial industry to set advanced technological solutions to improve the way they manage data. Another example is the Draft Regulatory Technical Standards on the prudential treatment of software assets published on October 2015[1]that directly support investments by financial institutions in these solutions.

Another need for new and improved technologies to manage data comes from the increased volatility of financial markets (that became even more evident  with the Covid-19 pandemic) requiring prompt reactions even in private markets. But how could you react fast in your portfolio if your date are one month old?

The  buy-side industry (including Asset Manages, Pension Funds, Investment Funds, etc.) requires additional level of transparency when it comes to financial data. To establish trust among investors, managers of securitization vehicles are asked to provide detailed information that goes far beyond the publishing of a monthly report but encompasses asset level information to be provided on a daily basis. This requires  a rethinking of the reporting processes of securitisations, leaving aside excel and pdf files and starting to embed technologies that allow all stakeholders involved to access real time data 24/7.

Technology is now available off-the-shelf also to small players, not only the top ones. Thanks to fintech developments and use of cloud computing, any actor (small or big) can take advantage of advanced ready to use technology propositions. This will in turn, avoid the large and risky project-specific capital expenditures.

The Tortoise and the Hare

What makes the difference is the time to market in terms of adoption of such new technology propositions, not the deep pockets to invest in the development of any proprietary tool as it is was still the case a few years ago.

[1]Draft Regulatory Technical Standards on the prudential treatment of software assets under Article 36 of Regulation (EU) No 575/2013 (Capital Requirements Regulation – CRR) amending Delegated Regulation (EU) 241/2014 supplementing Regulation (EU) No 575/2013 of the European Parliament and of the council with regard to regulatory technical standards for own funds requirements for institutions

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