Credit is a complex transaction. Just dig a layer underneath it and you witness the interweaving of many different entities that make a single transaction happen.
In the previous post, we built the business case for a platform to embed credit. They facilitate the ‘pull point’ that initiates an instance of a borrower applying for credit.
Once the borrower initiates the process, a set of credit facilitating entities get set in motion and without them, we won’t be able to provide the best form of credit, in the best manner possible.
This post is about these credit facilitating entities and in particular :
How these entities form ‘the embedded lending value chain’
What they bring to the table, in terms of their value-add
How they can create a conducive credit environment to begin with, apart from just facilitating a credit transaction
Who stands to unlock most of the profit pools with this paradigm shift
The embedded lending value chain :
Here is a brief introduction of the entities involved :
Borrower - In our embedded lending context, it is the user of a marketplace, platform or a tool that embeds credit as a service.
Embedding Platform - The platform that wants to facilitate credit as a service for its customers.
Fintech Infra Providers - They provide the rails and the plumbing to enable embedded lending in our context.
Data Aggregators - The aggregators of different data types that enables credit underwriting and monitoring.
Derived Data Providers - They derive useable data from raw data sources to help contextualise and normalise it for credit assessment
Payments Services Providers - They provide the payments related plumbing to disburse and collect credit
Credit Bureaus - They are a type of Derived Data Providers but command a separate recognition given their prominence in underwriting today. They will have to keep up with the pace of improvement in underwriting as new age lenders can potentially make credit decisions without relying on them. The abundance of data to underwrite and its ease of accessibility will only increase rapidly and they will have to be on their toes.
Underwriting Modellers - They help the lenders by creating new underwriting models that can help them carry out smarter underwriting.
Lender - the source of capital that can vary from depository lenders to private, wholesale capital. Private companies lending off their own balance sheets is another possibility, albeit an inefficient and non-scalable one.
What they bring to the table
Please note that there’s bound to be an overlap in the scope of roles that the entities undertake.
However, what we observe is that regardless of the different hats they put on, in the context of embedded lending, their value addition is around one or more of the following :
Capital
Distribution
Data
Technology
There is a many to many mapping that emerges when you map the values the different entities provide, and it varies from one credit product to another.
The Borrower also provides new input when they fill in the loan application, that the marketplace won’t know of, like SSN!
Creating a conducive credit environment
As a platform engages with the different entities and assesses what they bring to the table, it is important to not look at the entities and their roles retrospectively, but from the onset to ‘craft customised credit products’
As an embedding platform, to create the credit products your customer needs, to improve conversion for loan applications and fetch better rates, basically to de-risk the transaction, it should configure :
The way the data is assessed for underwriting
The way the Borrower utilises the credit
The way the Borrower re-pays the credit and interest accrued
For example, tying up with a ‘Payments Services Provider’ to utilise an escrow facility for repayment of loans to using a ‘Stored Value Account’ line a pre-paid debit card can de-risk the credit transaction for the lenders to a great extent.
Who stands to unlock most of the profit pools with this paradigm shift
From Clayton Christensen’s 2003 book :
Formally, the law of conservation of attractive profits states that in the value chain there is a requisite juxtaposition of modular and interdependent architectures, and of reciprocal processes of commoditization and de-commoditization, commoditization, that exists in order to optimize the performance of what is not good enough. The law states that when modularity and commoditization cause attractive profits to disappear at one stage in the value chain, the opportunity to earn attractive profits with proprietary products will usually emerge at an adjacent stage.
Someone I admire deeply, Ben Thompson wrote about the Law of Conservation of Attractive Profits a while back : https://stratechery.com/2015/netflix-and-the-conservation-of-attractive-profits/
More broadly, breaking up a formerly integrated system — commoditizing and modularizing it — destroys incumbent value while simultaneously allowing a new entrant to integrate a different part of the value chain and thus capture new value.
- Ben Thompson
From Ben’s post above :
I have been curious to see how this law fits in the context of embedded lending and while this has to be a different post altogether, I’d like to park it with you to marinate for a bit.
The key realisation in the ‘embedded lending’ paradigm is that as many marketplaces and platforms emerge to embed credit as a service, the loan origination points become ubiquitous. This means that the incumbent online lenders’ distribution gets commoditised.
The traditional online lending marketplaces and especially the ones that had distribution as the key competitive advantage, now compete with any marketplace or platform that has a cluster of borrowers that it serves already. In effect, ‘Open vs Embedded’ marketplaces emerge as an option for the borrowers to choose from.
The net satisfaction for borrowers with online lenders has dropped over the past few years, as described in this Fed Report. There is a case to be made why we have a ‘high trust integration’ with an embedding platform as opposed to a ‘low trust integration’ with some of the traditional lending marketplaces.
The reason is that an embedding platform’s incentives are strongly aligned with the end borrower. They can be a true agent of the borrower and would ideally not enable access to credit for their customers, just for the credit’s sake.
The high trust integration further stems from an already established relationship, and the embedding platform’s deep understanding of its customers’ credit needs which gets reflected in new custom, differentiated credit products.
It’s important though to note that these open marketplaces have accrued tacit credit know - how of building and servicing credit products.
Even when many of them have struggled in the past year with Covid, their teams are in high demand by other FinTechs to recruit from!
They will continue to exist but will have to re-orient their strategy in the new embedded lending landscape.
And in all this, while the embedding platforms will gain massively from the new way to enable credit for their customers, the large chunks of profit pools will accrue to Fintech Infra Providers, Derived Data Providers and Underwriting Modellers.
It will be fascinating to observe how the incentives in this value chain get aligned over the next few years. A part of that alignment has already begun.
it would be great if you can also share examples of actors in the value chain by taking one type of embedded lending like say BNPL, thanks