3 FINTECH NEWS STORIES
#1: Better Infrastructure for Private Credit
What happened?
Arc, a neobank and capital management platform for fast-growing businesses, launched an AI tool for the private credit industry:
Arc … today announced the launch of Arc Intelligence, the first AI platform designed to automate complex financial tasks for private credit. Arc Intelligence’s initial product, the AI Private Credit Analyst (“AI Analyst”), empowers credit teams to make more confident investment decisions faster, with 99% accuracy on complex financial analyses. It allows investors to ingest unstructured private company financial data and generate detailed credit memos, transforming a process that would typically take deal teams several days to a matter of minutes.
Credit funds have historically relied on offline and manual processes to collect, enrich, and analyze borrower data before producing cleanly-formatted reports. This archaic model of slow data ingestion and repetitive financial analyses means that credit analysts spend days developing a basic picture of the businesses they’re evaluating. Arc Intelligence changes that.
And Cardo AI, a technology provider to the private credit industry, raised a $15M Series A:
The firm operates within the $40 trillion asset-based finance and private credit market, which has traditionally relied on outdated systems, manual procedures, and dispersed data. As the sector evolves with increasingly sophisticated investment strategies, Cardo AI is at the forefront of modernizing these operations.
The platform offers advanced portfolio modeling and collateral data management, enabling efficient investment decision-making and portfolio monitoring for various industry players, including investors, banks, and servicers. It integrates sophisticated software workflows, a robust data engine, and predictive AI algorithms, further aiding servicers, trustees, and fund administrators in enhancing their operational efficiency and reducing costs.
So what?
I’ve written a lot about the private credit market over the last year, and for good reason — according to Morgan Stanley, the private credit market has grown 50% over the past three years and is projected to grow to $2.8 trillion by 2028.
What’s important to understand about private credit is that it’s a market that includes many different types of loans.
One type, which represents the vast majority of the industry, is direct loans. These are loans made between private credit funds and small-to-medium-size companies (or the private equity firms trying to buy those companies).
Another type, which is a small but growing portion of the private credit market, is asset-based loans. As the name suggests, these are loans to companies where the companies’ assets are pledged as collateral. Those assets can be physical goods (real estate, aircraft, etc.) or financial assets (loans, receivables, royalties, etc.)
The offline, highly manual processes that Arc and Cardo AI are describing are how private credit loans have traditionally been underwritten. This worked reasonably well in the old days of private credit, back when the overall lending volume was low (because banks still played a big role in the market), and most of the activity was direct lending to companies.
However, as private credit has become faster-paced and more competitive, and as volume has shifted towards asset-based finance, in which lenders are evaluating a portfolio of assets rather than a single company, the need for a more efficient underwriting process has grown.
Generative AI is obviously helpful here. As Arc points out — much of the data that drives these lending decisions is unstructured, which makes it a poor fit for traditional machine learning techniques but an excellent fit for the flexible summarization capabilities of LLMs.
I’ll also be curious to see where Cardo AI goes, as one of its investors is FINTOP Capital, which counts roughly 100 regional and community banks as LPs. I’m honestly not sure what interest regional and community banks have in private credit infrastructure, but I want to find out!
#2: Maximizing Value for Issuers
What happened?
Knot, a fintech company that provides APIs to connect merchants and card issuers, introduced a new product:
Knot AccountUpdater allows card issuers … to update cardholder details (card info, name, address, phone) across Knot-connected merchants with a single API call, requiring no effort from the cardholder.
Now, card issuers can seamlessly control the payment instrument on file, whether they want to update users from a debit to a credit card, change processors, or more.
Network solutions handle lost, stolen, and reissued cards effectively, and Knot AccountUpdater excels at cross-network updates and BIN range transitions.
So what?
Full disclosure — Workweek is an investor in Knot and I know Rory and the team at Knot very well.
AccountUpdater is cool. The card networks already have solutions that ensure that cards will continue to work in case they are lost, stolen, or need to be reissued for some other reason. Knot’s solution takes that functionality to a new level, enabling issuers to make more significant changes — upgrading to a higher card tier, switching customers from debit to credit, or switching banks, processors, or networks — without disrupting end customers’ ability to transact.
What’s most interesting to me is that AccountUpdater is a solution for issuers that the card networks would likely never build themselves. While everyone in the card payments ecosystem wants end customers to transact without interruption, my guess is that there’s a limit to how seamless merchants would want that to be in pursuit of helping issuers maximize their interchange rates. That limit would likely discourage the card networks from enabling this level of account updating functionality themselves.
There has always been tension between card issuers and merchants (which card networks have done a mostly great job managing). However, that tension is ratcheting up as fintech innovators enable more sophisticated capabilities through APIs and digital wallets.
#3: A Few New Companies to Supervise
What happened?
The CFPB finalized a rule defining “larger participants” in the market for general-use consumer payment applications, making qualified firms subject to the CFPB’s supervisory authority:
While the CFPB has always had enforcement authority over these companies, today’s rule gives the CFPB the authority to conduct proactive examinations to ensure companies are complying with the law in these and other areas. Supervision can prevent harm by detecting problems early. Supervision also is an important tool for the CFPB to assess risks that can emerge rapidly in this market, including from outages and other issues that could lead to millions of consumers losing access to their funds.
In the final rule, the CFPB made several significant changes from its initial proposal. The transaction threshold determining which companies require supervision is now substantially higher, at 50 million annual transactions. Given the evolving market for digital currencies, the CFPB also limited the rule’s scope to count only transactions conducted in U.S. dollars.
So what?
A couple of points to clarify right off the bat:
- All of the companies that would be covered by this rule — Apple, PayPal, Block, Google, etc. — are already subject to regulatory supervision, either by state regulators (if they hold money transmission licenses) or by state and federal regulators (if they partner with banks).
- All of these companies are also already subject to the enforcement authority of state and federal regulators (including the CFPB, which recently took direct action against Apple for its failures in the Apple Card program).
- The final rule raised the qualifying transaction limits from 5 million transactions per year to 50 million transactions per year and limited the scope to transactions conducted in US dollars, which excluded cryptocurrencies and digital assets.
I’ll be honest — I’m not in love with this rule.
Apple, Google, PayPal, and Block (and whichever other companies end up getting covered by this rule) are already well-supervised by regulators (both in financial services and outside of it). The CFPB may not have the type of unfettered access to those companies that it would like (outside of investigations brought through its enforcement authority), but I’m not sure I see a huge advantage to consumers in enabling that level of access (although I don’t, in any way, feel sorry for these companies having to deal with the added compliance burden … they can certainly afford it).
Also, what’s with the CFPB’s allergy to supervising crypto companies? If you’re looking for consumer harm, especially over the next four years, that’s the place to dig in.
2 FINTECH CONTENT RECOMMENDATIONS
#1: Cross-border payments in ~1,000 words (by Matt Brown) 📚
Cross-border payments has become a more popular topic in fintech over the last 12-18 months because of the increasing interest from the market in stablecoins.
(Editor’s Note — It’s very apt for crypto that interest in a technology [stablecoins] is driving interest in a job-to-be-done [cross-broder payments] rather than the other way around, but I digress …)
As always, these “X in 1,000 words” posts by Matt are top notch.
#2: When Siri Becomes a Deposit Broker (by Todd Phillips) 📚
Todd is doing some excellent work at the intersection of generative AI and financial services regulation.
1 QUESTION TO PONDER
Which personal financial management (PFM) apps that are still active in the market today have collected the most data on how consumers manage their finances?
