The ecosystem for European whole loan sales is rising
It’s widely known that banks are under pressure. New players like large technology companies, challenger banks, and “state-of-the-art” FinTechs are tackling the traditional banking business models from many sides.
To be “fit for survival”, according to a PwC / FinLeap report, banks must find new ways to generate profit growth and strengthen their efficiency. The report titled “Outsourcing 3.0”, outlines the new frontier of outsourcing for banking, which goes beyond IT and Business Process one. Outsourcing 3.0 entered the German market in 2018 and now it’s also known as Full-fledged Product Outsourcing.
The report defines it as a new wave of outsourcing archetype. It encompasses everything that is needed to deliver lean, flexible operations across the value chain; it accelerates product launch, improves risk management, operations, compliance, and IT. Most importantly, Outsourcing 3.0 allows variable balance sheet management making, thus, Asset and Liability Management (ALM) for banks more efficient.
In order to put in place an effective ALM strategy, it is key for the bank to properly optimise its funding channels. The list of all the possible options banks have today when it comes to funding may appear to be rather long. So let’s focus on the most well-known of them, namely retail, wholesale, and asset-based funding sources like asset securitisation.
Securitization, in particular, is the one that most resembles the concept of outsourcing, helping banks convert their assets into cash. Packages of already-originated loans are sold to institutional investors to generate more cash to be given out as loans once again. Securitization is essentially, but not only, a great financing tool and it helps banks diversify their funding channels.
Unfortunately, the world has come to associate the word securitisation with the 2007/08 crisis. In the aftermath of the largest and most dreadful financial crisis ever seen, investors suddenly realised that the packages of assets they had been buying were not only opaque but also of very low quality (subprime). According to AFME data, EUR 819.2 billions of securitised products (ABS) were issued in 2008, while in 2013 the new issuance was just EUR 180.2 billions. In other words, investors lost trust in these financial instruments and stopped buying them.
Transparency plays a key role in banking and finance as it is directly connected to trust. Transparency is linked to the availability of data, which is necessary but not sufficient. Data must be of good quality and standardised, otherwise, it cannot be compared and benchmarked. This was the idea behind the European Central Bank-sponsored Loan-level initiative. In 2012, with the aim of improving transparency and indeed reviving the whole ABS industry, the Eurosystem established loan-by-loan information requirements specifically for the ABS accepted as collateral in Eurosystem credit operations.
Consequently, more than 300 European banks have started investing time and resources in updating their core banking systems in order to programmatically upload ABS loan-level data no later than one month following the due date for payment of interest on the ABS. Apart from being a headache for both banks’ board members and their IT departments, this additional requirement resulted in an industry-wide standardisation exercise.
ECB loan-level data templates became de facto a market standard for ABS reporting. In fact, the new Regulation 2017/2402 (a.k.a. Simple Transparent and Standardized (STS) regulation) recently entered into force is largely based on ECB loan-level data templates. It is worth noting that even in the European Commission’s third progress report on the reduction of non-performing loans (NPLs) it is mentioned the potential to set up a European NPL transaction platform largely inspired by and based on similar data templates and data warehousing principles.
To summarise, today most of European (and UK) financial institutions, ABS investors and leading credit rating agencies are periodically interacting with each other using ECB loan-level data template. It has become their common language to exchange information on loans or securities collateralized by loans.
ECB loan-level data templates may also be used for disclosing the whole loans portfolios loan originators want to sell to investors. Whole loan sale is a meaningful financing alternative to retail deposit or the cost of small-scale securitization. Moreover, besides selling whole loans in the secondary market to generate cash or to rebalance the loan book, loan originators know that some specific credit asset classes, such as alternative credit, are becoming increasingly compelling to institutional investors for a number of reasons.
For instance, according to a Morgan Stanley report, alternative lending can potentially provide a combination of better yield and low duration that stands in contrast to the traditional fixed income world of today, whose underlying credit exposure is primarily corporate or government-based. This type of consumer-based lending has exhibited attractive absolute, and risk-adjusted returns and it is also more varied compared with other asset classes. Unlike them, it reflects a highly diversified opportunity set due to the fact that the volume and variety of strategies have been flourishing in recent years, providing multiple axes for diversification (e.g. by loan segment, credit quality, ticket size and duration).
However, the report also calls attention to the lack of a liquid secondary market with observable prices. In this regard, Algoritmica was born to help market participants solve this issue and to encourage them to invest in these new credit asset classes in a more technological way. Algoritmica’s core product, DeepLoans, is an AI-based predictive modelling platform compatible with ECB loan-level data templates.
It is designed for institutional investors, like hedge funds, loan funds, and bank investors who focus on investing in residential mortgages, consumer loans, SME loans, auto loans and credit card loans portfolios. Investors using DeepLoans can directly leverage on its Web User Interface (UI) and API-first architecture to source and upload loans of their partner loan originators into the platform. Once uploaded, the platform users can run DeepLoan’s AI algorithms to perform pool analysis, predict the risks of each single loan within the portfolio, and forecast pool cash flows.
While it is true that there are still a lot of opportunities to increase the liquidity of the secondary markets, it is also true that the seed has already been planted and the sprout has started to grow. I believe it reasonable to foresee that in the near future more value-added solutions will be growing on top of ECB loan-level data templates and providing analytical tools like DeepLoans does now. It is my solid opinion that legal services will also emerge to securitize or sell assets rapidly, easily, and securely (a process that right now requires costly management “offline”).
Even though yearly ABS issuance levels are still subdued if compared to pre-crisis figures, I believe both investors and loan originators are benefiting from this tendency. The new data-driven asset managers will seamlessly integrate with loan originators’ core banking systems, giving the advantage of relying on automation to keep their operation costs down. The most innovative loan originators will leverage this ecosystem to periodically free their balance sheets or rebalance their loan portfolios efficiently, resulting in a new, valuable, and flexible funding channel.