From leveraging data to DATA LEVERAGE
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.
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.
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
