Deep Dive: Opportunities in Consumer Credit Part 2
Consumer credit has been an essential and indeed constant component in society’s financial fabric throughout history. When it comes to credit cards and revolving credit, banks still control most of the credit value chain from application to distribution, simply using processors like Visa and Mastercard as payments tracks to send the credit through. In the EU, the five largest credit institutions account for 46% of assets. They are mostly protected from new entrants by the moat of consumer stickiness/inertia, low-cost funding and qualitatively and quantitatively superior data pools. Nonetheless, traditional credit providers are feeling the heat from fintechs as both are constantly looking for new ways to sustain and increase margins.
In this report, I’ll look into the different areas of innovation today, and assess where we might see the next big opportunities. I’ll focus on revolving credit (rather than point-of-sale loans or mortgages), and prioritise opportunities in TradFi, although I’ll highlight how blockchain technology applies in callout sections. See Part 1 on the Value Chain and Key Variables here.
Credit Cards and Revolving Credit
How do credit cards and revolving credit work?
Estimated to make 36% of payments, credit cards and revolving credit lines allow consumers to make purchases up to a set credit limit every month, sometimes for an annual fee. Contrary to a debit card which is linked to the consumer’s bank account, credit cards are funded by credit providers, which can be other banks or institutions. Some examples:
At the end of each month, the card issuer sends a statement of all the monthly transactions, the minimum payment due and the due date. If the bill is paid in full by the due date, no interest charges accrue, whereas the card issuer can charge interest on any balance kept month to month. And this interest — the annual percentage rate (APR) — reflects the prime rate, individual credit risk, and other costs such as annual fees. Below is a break down of consumer credit provider business models:
How do businesses like Visa and Mastercard work?
Visa and Mastercard are processors. With 336m and 231m cardholders respectively, their logos are found on most of the credit card brands listed above. Their clients are primarily issuers, such as banks and financial institutions who issue cards and the credit itself to their customers. When a cardholder purchases goods from a merchant, the processor analyses the request and approves the transaction, and the information is sent via e.g. Visa’s network to the merchant’s bank. The merchant’s bank is the acquirer. Visa charges data and processing fees and service fees from its issuer, and is not involved in the actual lending process. The issuer loans the cardholder at the time of purchase, charging interest on the loan as well as a card fee for the use of its card. The issuer also earns an interchange reimbursement fee from the acquirer. The acquirer charges a merchant discount fee from the merchant.
Because Visa and Mastercard are not involved in the lending process, they are not exposed to any credit risk. They earn revenue on the processing and service fees to the issuers, and therefore their revenue is based on the volume of transactions carried through its card.
How do credit card businesses like Amex work?
American Express, with 63m cardholders averaging 6bn transactions per year, operates a closed-loop network. Visa and Mastercard will always be linked to an issuing bank- they operate payment processing networks that enable these issuing banks to process credit card transactions. American Express does offer cards through separate issuers too, but they also have the capacity to issue their own cards through its banking subsidiaries (American Express Centurion Bank and American Express Bank, FSB). It then processes them on the American Express network. It offers credit lending in the form of charge cards and credit cards. Because it bears this financing risk, it earns interest and membership fees from customers, and charges merchants transaction fees for the network processing services.
It earns money through the discount fee charged to merchants (~65% of the company’s revenues), interest on loans issued to cardholders, and membership fees from cardholders.
So unlike Visa and Mastercard, its revenue model does not depend on the volume of transactions processed, but rather on the total amount spent by the customer. Therefore, it employs a spend-centric model, targeting affluent customers who are likely to spend more. Its model is to target affluent customers, partly because they have a lower credit risk, and partly because they have higher on average per-card spending versus competitors, offering superior value to merchants in the form of loyal customers with higher average transaction amounts.
Whilst credit card processors are just one section of the value chain in revolving credit, they illustrate a wider point: profit in the credit card and revolving credit space often comes down to margins, spread, and volume. As in any such industry, opportunities will therefore come in the form of either 1) reducing costs (risk, operations, funds and acquisition costs), or 2) increasing revenue (interest rates, fees, and the size of the customer pool). And so with that, we’re ready to address: where are the opportunities in the credit card and revolving consumer credit space?
Section 1: Cost-Cutters
Nimble digital fintechs already beat out traditional banks on the operational cost basis given they can operate paperless on-boarding and underwriting processes automatically reduce operational costs. But by now this is fairly commoditised. I expect differentiating innovation to lie more in the credit decisioning and credit collection stages of the value chain.
Opportunity 1: Improving credit decisioning and risk modelling
The bulk of the profitable opportunity lies at the credit decisioning level. Banks who embed high-performance credit-decisioning models can see revenue increases of 5–15% through higher acceptance rates, lower cost of acquisition and better customer experience, as well as 20–40% improved efficiency, and 20–40% reductions in credit losses. Credit-decisioning models can vary with quantity and quality of different data pools and modelling techniques, and so this is the space where start-ups can competitively differentiate and build moats.
This is why open banking and the PSD2 framework has been pivotal to innovation in the space by enabling fintechs to model open banking data in new ways. Abound, a UK-based consumer lending service, raised a huge £500m for its tech that combines open banking data and machine learning algorithms to build what it believes is a better credit score. Last year, Visa acquired open banking developer Tink, which provides API rails for thousands of banks, for over $2bn. Another major rails provider, TrueLayer, last raised at over a $1bn valuation. Meanwhile, Token.io and Vyne are (similar to Abound) examples of start-ups building more specific applications on open banking standards (respectively person-to-person payments and merchant services).
Expect to see more innovation happening beyond utilising the data that Open Banking rules make available. There’s plenty of other data sources which fintechs differentiate by accessing and accurately modelling.
⛓️ Blockchain Opportunities: On-Chain Data for Credit Scoring
On-chain data from crypto activities could provide an alternative source of data to improve credit scoring. By using expanded data sets, such as those from crypto transactions, lenders could improve access to credit for millions of credit invisible and unscorable consumers, as well as those currently considered subprime. The data could help reduce risk and increase the customer pool, leading to higher acceptance rates, lower acquisition costs, and better customer experience.
There’s plenty of players building out in this area. Quadrata, Cred Protocol and Masa Finance are examples of crypto companies building out the decentralised identity, analytical and scoring abilities to enable on-chain credit scores for consumers.
Opportunity 2: Debt Collection
On the supply side, the debt collection industry in the United States alone is estimated to be worth $11 billion. Most debt collection agencies still rely heavily on paper-based communication and phone calls, leaving ample opportunity for automation and digitization. Moreover, credit providers stand to benefit from software improving the repayment journey and adequately anticipating late payments rather than dealing with them after the fact. Doing this effectively minimises operational expenses, as well as regulatory and reputational risks. Flexys for example, utilises machine learning and sentiment analysis to alert companies when a customer looks likely to default on payment and automates the debtor engagement tasks. TrueAccord is an incredibly interesting company that uses machine learning to power debt-collection, creating a unique experience for each consumer and collecting data points to predict consumer reactions to communication frequency, channel, and content- powerful data for any debt collector.
There is also, perhaps surprisingly, considerable potential for a customer-friendly debt collection experience provider. Automated systems can be utilized to track, predict, and remind consumers about potential late repayments. As the process advances, legal documentation generation and payment arrangements can also be digitized, streamlining the overall process. InDebted, for example, moves the entire collections process online, giving customers a portal showing the status of collections and debt, with clear and transparent (although high) pricing.
⛓️ Blockchain Opportunities: Smart Contracts
Smart contracts (self-executing contracts) have the potential to revolutionise the debt collection space by automating it- reducing the need for human intervention and minimizing the risk of errors.
In a smart contract-based debt collection system, the terms of the debt agreement would be encoded in the contract, and the contract would automatically execute certain actions when certain conditions are met. For example, if a borrower misses a payment, the contract could automatically trigger a late fee and send a notification to the borrower. They also allow for more transparency and accountability in the debt collection process. The terms of the contract would be publicly visible on the blockchain, and all parties would be able to see the status of the debt and any actions taken, helping to build trust between lenders and borrowers and reduce the risk of disputes.
Aave, a decentralised lending protocol, allows users to lend and borrow without an intermediary. It uses smart contracts to manage the lending process, including loan origination, underwriting, and repayment. Goldfinch also provides loans to underserved communities around the world, equally relying on smart contracts ot manage the process. Centrifuge allows users to tokenise real-world assets and use them as collateral for loans. All three have zero collection costs as smart contracts automatically liquidate the borrower’s collateral to cover outstanding debt.
Section 2: Revenue Drivers
Were a start-up to ever compete in the payments processing space it would likely follow Amex’s model. In other words, it would likely raise capital, benefiting from a cost of capital — and therefore margins — standpoint compared to lending from the bank’s balance sheet or offering a pure-processor play. But this would be a huge undertaking as it requires going against giants and building all the payment rails, whilst convincing merchants to sign up to yet more instalment and interchange fees. The route to getting there would be finding a new way to target or reward affluent customers. Alternatively, another route is going after a new segment entirely.
Opportunity 1: Increasing the Customer Pool by Scoring the Unscoreable
An untapped market for lenders is the population of consumers who are currently credit invisible or unscoreable, such as students and immigrants. Established lenders typically avoid this group due to the associated credit risk and the greater profitability of more affluent creditworthy users. This leaves a significant opportunity for those who can accurately assess and price credit risk for this population segment.
Lenders can leverage expanded data sets, including rental payments, trended data, and utility information, along with advanced analytics in their decision-making process to improve access to credit for the 46m credit invisible and unscorable consumers, as well as millions of consumers currently considered subprime.
Migrants
Migrants who arrive in new countries, cash-rich and with an impressive credit history soon discover that their creditworthiness and credit history become irrelevant and untransferable. The issue lies with the fragmented nature of credit bureaus across different countries, which, astonishingly, operate in silos and do not coordinate credit files. This situation leaves migrants facing disproportionately arduous application processes and higher borrowing costs even if they meet the qualifications for credit facilities or cards.
Fortunately, a few enterprising start-ups have identified this predicament as a pain point and are working to address it.
For example, Pillar is a super exciting start-up with an impressive founding team, looking to provide immigrants with credit and a credit card, using an open banking-led data and analytics engine. They give subscribing consumers unsecured and secured lending products depending on the amount of credit data they can retrieve, allowing them to build up a credit score that can be used for other credit products. Nova Credit, targets the painpoint through a different route, by instead providing a cross-border credit reporting agency, connecting lenders to international credit data from bureaus like CRIF, Experian, and Transunion, enabling immigrants to access credit scoring and reports necessary for credit. And Bloom focuses on adapting the user experience to migrant cultures, replicating box money whereby ‘circles’ of communities exist in which users pay in a certain amount per month and can cash out lump sums at periodic intervals.
Gig workers
Gig economy workers can also be cash rich, but often their income is highly variable month to month, making accessing credit lines (amongst other financial products) difficult. In general, freelancers and contractors who are compensated in varying amounts, often through cash transactions, face difficulties establishing a credit score and qualifying for credit products. The consequences range from inconvenient to actively harmful if it means paying for unexpected emergencies and big ticket items becomes impossible for a whole class of workers. Again, there are a few different approaches that startups have been taking to this issue.
While Mansa requires an application with a lending decision in up to 2 days, Steadypay uses a subscription model where for £4/week, workers can access short term loans if they fall up to £250 below their average earnings. Meanwhile, Canadian fintech Moves Financial is another with really interesting positioning. It seeks to structurally overhaul credit for gig workers, by building them digital credit union that offers cash advances to smooth income, as well as the ability to earn stocks from the platforms they work for, such as Uber, Lyft and Doordash.
Opportunity 2: Charging UX Premiums
Ultimately, the key to becoming a major player in this space is providing a new and superior offering for more affluent consumers, given that this industry comes down to thin margins on high volumes. But how?
Consumers have begun to increasingly expect instant and fully digital customer service, rewards and benefits, and an engaging UX- none of which are to be found optimised for in stuffier traditional financial institutions. Take a look at the likes of Keebo and Yonder, compared to Amex and CapitalOne and the differences are stark.
Beyond the interface, consumers are increasingly looking for the best benefits. Amex has established itself in the travel rewards package through its relationships with hotels and airlines (notably British Airways), but there is room for start-ups to target new demographics and secure acquisition channels through highly targeted reward schemes. Yonder has tackled the rewards angle, offering ‘rewards that you’ll actually want to use’, like restaurants, bars, gigs, and events.
More so than a revenue channel, finding unique rewards schemes for credit-worthy but underserved consumers is a powerful customer acquisition channel that is yet to be completely explored by players in the space. Some opportunities to consider:
- Freelancer and creator economies, perhaps with perks of related tools like Adobe subscriptions or cameras. This would be similar to revenue-based financing of businesses, or student loans financing businesses like SoFi that take a cut off of future earnings by funding the initial educational investment.
- The rising class of ‘digital nomads’, perhaps with perks in the tech and travel rewards field.
- International workers, particularly blue collar workers, who are mostly paid in cash and struggle to get salaries, let alone credit, to their families abroad.
⛓️ Blockchain Opportunities
Given the permissionless and decentralised nature of blockchain technology, no one is excluded from building their credit history on chain. No data is siloed in credit bureaus or other disparate third parties. That is the democratising magic of blockchain. On the UX front however, there is plenty more to be done, which I’ll leave to one side for everyone’s sake…
Final Thoughts
So for me, the big opportunity for credit card start-ups at a high level lies in 1) the debt collection and 2) the credit score pipes space. The former is an area often underestimated in size and painpoints, leaving it ripe for a disruptor that improves both the collector processing and consumer experience of debt management and repayment. I expect this will heat up with buy-now-pay-later offerings where point-of-sale credit increasingly infiltrates consumer habits and becomes more difficult to track and manage.
And with credit scoring, this really comes down to players speeding up the stages from application to distribution for a new pool of customers for which there is a) unique access to b) precise and c) reliable data that can be d) manipulated into new and effective decision models.
Opening up credit products to new consumers is risky- they’re unscored for a reason. Intuitively, though, lending to consumers like migrants where it is merely a case of retrieving existing credit history from siloed and complex data pipes is a better risk-adjusted return than targeting those with low or non existent credit scores, like gig economy workers or students. If the value lies in the former, then we should be looking for where and how to collect and connect existing credit data and what type of consumer would need this. So, as touched on, I’ll be looking for products that develop specifically for unscored yet affluent workers- where it’s more about retrieving or recreating an existing credit-worthiness. I expect this pool to comprise of international workers such as pilots, diplomats, influencers or ‘digital nomads’- those who fall between the credit cracks as they juggle different credit accounts, jurisdictions and currencies.
Lastly, one aspect I didn’t go into here is the significant B2B2C opportunity here. There must be new ways of using the rich data pools that will come from targeting a specific demographic. One such example would be selling aggregated and modelled data to insurance companies. Companies like Pillar and Nova Credit have could offer aggregated data about migrants, including information about their movement and spending habits, while Bloom could provide valuable insights into social and community spending data. This is especially relevant in the case of gig workers, where employers like Deliveroo or Fiverr could benefit from gaining insights into the credit and spending habits and history of their employees.
This is certainly a space to watch, and as ever am more than happy to discuss further over email at marthe@whitestarcapital.com
