Consumer credit has been an essential and indeed constant component in society’s financial fabric throughout history.

From our farming ancestors who borrowed seeds issued against future harvests, to contemporary US households who have reached $16.9 trillion in household debt, we have always developed systems to make up for income variation as we need/desire constant levels of consumption. Whether as mortgages or payday-loans, revolving credit or buy-now-pay-later schemes, these systems all come down to the same basic premise. Someone has something that the other currently needs but cannot currently afford, and the creditor lends it out in return for at some future point receiving everything back that they lent in the first place, with a little bit extra to make up for the hassle (and the risk of never getting it back). But there has always and will continue to be significant innovations happening at each of these stages. 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 traditional finance and highlight how blockchain technology applies in grey boxes.

In 1800BC Iraq, the Code of Hammurabi laid out the first known set of rules on issuing and repaying loans with interest.

Socio-economic and cultural factors have shaped homogenous attitudes towards credit. Large revolving balances of unsecured lending has typically been more culturally acceptable in the UK and Turkey, for example, than in Germany which is comparatively debt-averse [source], and credit card preferences vary from 2% in the Netherlands to 24% in Spain. Historical variables are also significant here, as consumer demand shifts with wider societal needs. The Second World War, for example, shifted the West towards mass consumerism, helped by a supportive macroeconomic climate, and resulted in the explosion of revolving credit lines and the modern credit card, enabling consumers to more flexibly fund day-to-day purchases, and even be rewarded for doing so.

The first modern credit card is traced to the founding of the Diners Club in 1950

2. Macroeconomics

Fundamentally, credit products require capital to loan out. This typically comes from banks, who themselves borrow the money from other banks, or directly from the central bank. The interest rate on consumer credit, therefore, moves in tandem with the credit provider’s cost of capital- i.e. the interest rate they are charged on borrowing. In the US this is the Federal Funds Rate; in the UK this used to Be the LIBOR and has now moved to the SONIA.

These interest rates have remained at historically low rates for the past few decades. But over the last year, record-breaking inflationary pressures have built up considerably, since the loose monetary and fascal policy of governments across the world in response to the Covid-19 pandemic, coupled with supply chain bottlenecks and rising energy prices.

Source: White Star Capital’s Analysis for Macro Year Ahead

This affects consumer credit because the central bank rate is how much banks charge each other for short term loans, and so interest rates charged by lenders to borrowers tends to move in lockstep with the fed funds rate. This is a major concern for fintechs such as BNPLs who are dependent on a zero interest rates offering, particularly those not profitable and dependent on cheap capital in private markets. It also massively impacts consumers subject to variable rate mortgages (which comprise approximately 70% of consumer debt according to Bank of America research). However, we’re unlikely to see the full effects play out this year- the bigger worry comes when existing fixed-rate loans mature and a wave of new loans take place under higher interest rates.

3. Regulation

Regulation is a final key variable that impacts innovation in the consumer credit space. For example, interest rate caps restrict the maximum amount of interest that lenders can charge borrowers, and jurisdictions require different standards in credit reporting. Indeed, regulation can vary significantly by geography. One example of a country with unique consumer credit laws is Japan. In Japan, the Money Lending Business Act requires lenders to obtain a license before lending money, and there are strict regulations on interest rates and fees that lenders can charge. In contrast, in the United States, licensing requirements for lenders vary by state, and interest rate caps are set at the state level.

Open banking (the use of open APIs to enable data sharing between financial institutions and third party providers_ enables consumers to share their financial data with different lenders. It enhances transparency and security in consumer credit, by enabling fintechs to effectively access and utilise data to build customer centric products centered around customer trust.

Before and After Open Banking

Open banking laws and regulations can vary significantly by geography, with different countries adopting different approaches to promote competition, innovation, and consumer protection in the financial industry. In Europe, the Second Payment Services Directive (PSD2) has been a driving force in open banking, requiring banks to open up their customer data to third-party providers through open APIs. This regulation has led to the emergence of a new ecosystem of fintech companies and service providers, offering innovative products and services to consumers, such as personal finance management tools, payment initiation services, and account aggregation services. The PSD2 also provides a legal framework for data protection and security, ensuring that consumers have control over their financial data and that it is protected from misuse or unauthorized access.

In the United States, open banking is regulated by a patchwork of federal and state laws, with no overarching legal framework like the PSD2. However, the Consumer Financial Protection Bureau (CFPB) has issued guidance encouraging financial institutions to adopt open banking principles, such as data access and portability, in their business practices. The CFPB has also proposed a rule that would require banks to allow consumers to share their financial data with third-party providers through open APIs. However, this proposal is still under review, and it remains to be seen whether it will be enacted.

Overall, given all three variables above, the consumer credit landscape and the rate and nature of innovation therefore varies significantly by geography, with different regions adopting different approaches to promote competition, innovation, and consumer protection in the financial industry.

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.

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:

Future of Credit, A European Perspective — Deloitte

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.

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?

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.

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).

The Benefits of Open Banking

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.

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.

Indebted: consumer friendly debt collection

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.

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 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 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.

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.

Keebo, a credit card startup demonstrating the new UX norms for a digital generation…

…compared to Amex

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:

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.