If I Were To Launch a Decentralized Lending Operation, These Would Be My Domain Teams

In part two of this two part series (see part one here), Jason Jones asked John Donovan to consider Maker’s first key element, Elected Paid Contributors and Domain Teams*.  Specifically, Jason asked him what type of Domain Teams he would nominate if he were to launch a decentralized lending operation (DLO).  John is a co-founder and former Lending Club COO and Board Member, ex-MasterCard VP, and Fintech advisor, Board member and consultant.

*Domain Teams are specialized teams that produce products and services for the MakerDAO community. Domain teams are granted special authority in the MakerDAO governance system to oversee critical processes and mitigate risk.

When Jason asked my thoughts in terms of how I would build the appropriate teams to support a Decentralized Lending Operation (DLO), I looked at it within the light of past experiences I have had at MasterCard and Lending Club.  Money is always looking for yield, globally.  With money and yield comes lots of regulations and requirements.  And, satisfying existing regulations within existing frameworks is faster than working with governments on a new regulatory framework, such as when Lending Club registered with the SEC positioning it to eventually becoming the dominant P2P lender in the US.  There is a huge opportunity for a DLO to connect great asset originators with interested investors globally, in a safe and secure way.

The credit card industry is certainly one of, if not the, largest money lending platforms in the world, and is a truly distributed global model.  Prior to credit cards, consumers typically had charge accounts at restaurants and stores. Those merchants would underwrite their customers and handle collections.  Some did it well, some not so much, but it certainly wasn’t a core strength for most merchants.  Accepting credit cards was a more efficient way for merchants to sell more without taking on that underwriting and collections risk.  MasterCard and Visa provide the rails which are used to pay the acquiring bank and their merchant, and an assortment of data enabling that transaction.  Issuing banks would underwrite their consumers, and lend them funds as necessary to continue making purchases.  Universal acceptance meant that a Japanese consumer could travel to NYC with her MasterCard and find that most places she wanted to shop accepted that card.  Initially, the issuing banks relied on retail deposits to fund their growth but over time accessed the securitization market for more efficient and scalable institutional funds.  Over the past 60 years, this system has grown exponentially to now represent $trillions in purchases (and revolving debt) globally.

While the payments networks are critical for global commerce, they are terrible systems for consumers to borrow money.  The majority of credit card revolving debt is concentrated in a few big banks typically at higher interest rates than comparable installment loans.  Installment debt is a more responsible product for consumers as they understand exactly what they owe and exactly how long it will take for them to pay it back.  While revolving debt continues to grow, the smaller installment debt market is taking market share and is being driven by Fintechs.  According to the St. Louis Fed, Banks/CUs represented 71% of installment loans in 2013 with Fintechs representing 5%.  5 years later, Fintechs grew share by almost 8x to 38% and now represent more volume than either Banks or CUs.

When we started Lending Club in 2007, the idea was to disintermediate the banks and allow individuals to lend, via an installment loan, directly to other individuals.  Zopa was the first p2p lending company, and had already been operational in the UK for a couple years.  The idea was that a lender would earn higher yield than the funds they had in savings accounts (certainly with greater risk), and the borrower would pay less than they were paying on a credit card with a more responsible debt product.  This was a step towards decentralizing lending by more directly connecting the many sources of funds (individual lenders) with the use of funds (borrowers).

We spent a lot of time in the early days managing (or trying to) the community – listening to what they needed for the marketplace to operate efficiently.  We provided tools for the parties to directly ask questions and get information, we automated the lending process so ‘power lenders’ could use tools and eventually an API to automate the decision process, and we were completely transparent with our data and reporting not only on issued loans and payment history by loan, but also declined applications.  Payment history included successful transactions, but also failed transactions and collections efforts to recover those funds.  As with the credit card industry, Lending Club would eventually find it more efficient to rely on institutional debt to grow as opposed to individual retail investors.

What was the biggest weakness in the traditional bank / credit card lending model?  End users paying for the middlemen.  Lending Club did a study on their cost advantage vs traditional banks and estimated that they had a 400+ basis point operating advantage driven by the lack of a branch network, more efficient originations, and improved customer service, collections and anti-fraud measures.  If you break down Lending Club’s costs today (pre-Covid), their largest expense (in order) was sales and marketing,  general and administrative, engineering and product development, and lastly originations and servicing.

A DLO model has the opportunity to significantly reduce (and re-order) those middleman costs by leveraging existing tools and acting as more efficient intermediary between existing regulated entities.  The DLO compensates each party for their role and provides:  decentralized finance; a securitization protocol to optimize risk allocation; the tokenization of documents; a single source of truth for all parties; and, a protocol for storing and exchanging business documents in a private, secure, and verifiable way.  As I think about domain teams for an efficient DLO, I think about the challenges I experienced at both MasterCard and Lending Club and identified 3 primary teams:  Compliance; Risk; and, Marketing.  Over time, most of these functions should be automated or outsourced to DLO participants.

Compliance Team – Establishing a compliant regulatory framework

Any platform with the potential of handling $trillions needs to be both compliant with existing regulations and strong enough technically to satisfy new regulations which will inevitably evolve.  If you are not compliant with your first million, you will never scale.

When we launched Lending Club, we were licensed at the state level which significantly limited our addressable market and required Lending Club to deal directly with dozens of state regulators.  By partnering with a nationally chartered bank, Lending Club could issue loans nationally and rely upon the regulatory expertise of it’s partner bank.  The same certainly applied to MasterCard where an acquiring bank in one country is responsible for being properly licensed to do business, and to ensure their merchant is a legitimate business.  Cardholders making purchases at that merchant have been appropriately vetted by their respective issuing banks so that state, national and regional regulators are satisfied that appropriate commerce is taking place.

This Compliance Team will ensure the DLO is appropriately licensed, and that the connected entities have the appropriate governance and licensing for their respective jurisdictions.  This includes lending, investing and related industries (B/D, eMoney, Money Transmitter, etc).  Every DLO debt offering relies upon the lender and borrower being appropriately documented in a way that would satisfy the relevant regulatory authorities.

This team will also enforce compliance when certain players do not live up to their agreements including collections and legal actions.  One of the biggest challenges in adoption of decentralized solutions is ‘garbage in, garbage out’ – we need a system that verifies that the appropriately licensed parties are submitting the correct documents in an enforceable way.  This would be that verification capability which the decentralized ledger would then support.  The regulatory domain would ensure that a licensed broker/dealer who has the rights to a given debt offering can tokenize that offering and allow platform investors to purchase rights.

This work will be managed by the Compliance Domain Team, with some outsourced to local legal firms.

Risk Team – Building trust through transparent open data, and limiting risk by using professional underwriting and anti-fraud efforts.

Risk management has evolved more in the past 10 years than almost any other area, particularly with the ability to leverage mass amounts of third party data with better analysis and understanding through integrated AI/ML tools.  When credit cards were first being issued via mass marketing in the 1980s, banks were sending applications to random names which often were pets or children.  The 1990s brought higher tech ‘monoline’ banks that were able to access much more detailed credit bureau information.  Capital One, and others like MBNA and Providian, leveraged that data to identify and classify risk thereby improving marketing efficiency.  Today, lenders have access to more enhanced credit bureau information from more sources, and they also have access to bank transactions, asset and financial information, income and tax reporting, student verification, and social data.  There has never been this much data, which is just at the early stages of supporting automated decisioning.

Ultimately, the models and scorecards attempt to answer ‘the 5 Cs’ of credit, namely:  character, capacity, capital, conditions, and collateral.

  • Character is the credibility of the borrower or their desire to repay the loan.  What does their credit look like? What does their payment history look like? Do they have references and appropriate credentials? What is their reputation?
  • Capacity is their ability to repay the loan.  They may have great character, but the business simply doesn’t have the cash flow to repay the loan.
  • Capital is the amount of cash and other financial assets in the business.  It can also include machinery, buildings and equipment.
  • Conditions is the market environment of the business. Is the business growing? What are the funds to be used for?  What is the state of the local economy, trends and other factors that might affect repayment?
  • And finally, Collateral.  Managing collateral risk is driven by the requirements in the loan documentation, but may include pricing (including mark-to-market) and various reporting requirements.  Documentation should define the collateral, any margin call parameters, any close-out and termination clauses, as well as valuation methodology and reuse options.  Pricing might include high level parameters like rating, maturity, collateral currency, and, mark-to-market parameters (or other similar pricing calculation).  Accurate and timely reporting will reduce disputes and keep all parties informed.

This team would work to automate data verification, data sharing with distributed modeling and scoring teams, and all related processes and procedures working closely with technology and data science teams. This information, along with repayment information, will form the basis of a framework to constantly improve the safety and transparency.

The Risk Team will ensure the platform can support third party data access, as well as numerous credit models and risk scorecards so that each collateral offering is represented by the asset originator as well as enabling third party underwriters to access data and provide risk guidance.  This data would reside in the Maker Vault giving participants real-time access to records of all securities listed, purchased, and a continuous reconciliation at all stages of the transaction.  This team would work with all parties to understand their data requirements and, working with compliance, ensure that agreed data is contractually provided.

A separate team will also build effective anti-fraud tools and monitor fraud risk.  This will primarily be automated tools, but those tools will be built after the team conducts a risk assessment including:  identifying risk factors; identifying potential schemes and prioritizing anti-fraud tools based on likelihood and impact; mapping existing system controls and identifying any gaps; testing ongoing effectiveness; and, assessing the likelihood and business impact of an incident.

Marketing – scaling growth by balancing supply and demand

When we started Lending Club it took a long time to figure out the marketing, and it was very challenging the first few years.  We initially launched as an app inside the walled garden of Facebook which was a very young audience (at the time) and severely limited our potential borrowers.  The average FICO of someone under 25 was something like 637 and our minimum FICO requirement at the time was 640.  We focused on identifying recent college graduates as borrowers and older alumni as lenders, which was working well until a scandal at Columbia University caused all of our colleges to cancel their agreements. It became clear that we could not rely too much upon any single channel and that managing supply and demand was one of the larger challenges.  Over time, Lending Club developed thousands of channels to attract customers so that they did not have to rely upon one acquisition approach.

The last team for the DLO would be a marketing and sales function.  They are responsible for balancing investor supply of funds with borrower demand.  When the DLO is not balanced, their job would be to utilize existing incentivisation tools, identify new channels and partners, develop new products and support overall platform growth.

This team would work transparently with DLO participants to manage expectations with a focus 3-6 months in the future.  They would identify target customers including exchanges, originators and investors.  Much of this work would include understanding money flows and yield expectations.  If a US Freight Forwarding Invoice Debt offering was oversold with significant interest from Japanese investors, marketing would identify other similar offerings.  Alternatively, if an offering was not fulfilled marketing would understand the breakdown and resolve by either finding appropriate investors or focusing on more interesting originators.  Efforts would include customer onboarding, customer service, and overall awareness efforts.

To wrap things up, a DLO is a natural solution allowing investors and borrowers globally to connect in a transparent, cost-efficient, compliant, real time way.  It provides accessibility to an opaque industry with tough barriers of entry.  What started as local charge accounts in the 1950s, became revolving debt in the 1990s.  P2P lending took it to the next level with a more responsible national debt product which attracted a global investor base.  A DLO can now allow any borrower platform to connect with any investor platform anywhere, seamlessly.