What might the AI bank of the future look like?; Visa and Mastercard can now be used on China’s biggest payment apps;

In this edition:

1?? Why Apple Pay Later is Gaining Market Share So Quickly—And Who Should be Concerned

2?? Visa and Mastercard can now be used on China’s biggest payment apps

3?? Dimon confirms JPMorgan’s plan to launch digital bank in Germany

4?? How big banks like JPMorgan and Citi Want to put Wall Street on a blockchain

5?? What might the AI bank of the future look like?

6?? Microservices-based architecture: Foundation for platform banking

7?? The global fintech ecosystem slows down in Q2’23

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News

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Why Apple Pay Later is Gaining Market Share So Quickly—And Who Should be Concerned

According to J.D. Power survey respondents, Apple Pay Later has been used by more consumers than established brands such as Sezzle and Zip since its launch. Nearly one-fifth (19%) of BNPL customers used Apple Pay Later in its first three months. PayPal was still the most-used BNPL brand over the same period (39%), with Afterpay (33%) as the next-most used brand.

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Visa and Mastercard can now be used on China’s biggest payment apps

Starting Friday, visitors are able to link their Visa and Mastercard accounts to China’s most popular mobile payment platforms, allowing them to book taxis, ride the subway and pay for goods and services at millions of outlets across the near-cashless country.

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Dimon confirms JPMorgan’s plan to launch digital bank in Germany

JPMorgan Chase CEO Jamie Dimon confirmed for the first time Friday his bank’s plan to launch its digital platform, Chase, in Germany and other European countries.

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Bunq has raised $111M at a flat $1.8B valuation to break into the US

Bunq -- a Dutch startup that provides banking, savings, payments, card and other services to consumers with a focus on people who might need such services in more than one country across Europe -- has raised €100 million in equity funding ($111 million at today's rates), €44.5 million that just closed today, and a previously undisclosed €55.5 million earlier this year.

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Insights

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How Big Banks Like JPMorgan And Citi Want To Put Wall Street On A Blockchain

Investors at the New York Stock Exchange trade upwards of a billion shares in a single day, but many of those trades take days to settle. Big banks like JPMorgan and Citi think they have a solution, and they need to borrow a tool from crypto to make it happen—blockchain. Citi thinks tokenizing assets on the blockchain could be a $5 trillion dollar industry by 2030. But tight regulation of markets, and a crackdown on crypto from the SEC could slow adoption.

Source CNBC

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What might the AI bank of the future look like?

To meet customers’ rising expectations and beat competitive threats in the AI-powered digital era, the AI-first bank will offer propositions and experiences that are intelligent (that is, recommending actions, anticipating and automating key decisions or tasks), personalized (that is, relevant and timely, and based on a detailed understanding of customers’ past behavior and context), and truly omnichannel (seamlessly spanning the physical and online contexts across multiple devices, and delivering a consistent experience) and that blend banking capabilities with relevant products and services beyond banking. Exhibit 3 illustrates how such a bank could engage a retail customer throughout the day. Exhibit 4 shows an example of the banking experience of a small-business owner or the treasurer of a medium-size enterprise.

Internally, the AI-first institution will be optimized for operational efficiency through extreme automation of manual tasks (a “zero-ops” mindset) and the replacement or augmentation of human decisions by advanced diagnostic engines in diverse areas of bank operations. These gains in operational performance will flow from broad application of traditional and leading-edge AI technologies, such as machine learning and facial recognition, to analyze large and complex reserves of customer data in (near) real time. The AI-first bank of the future will also enjoy the speed and agility that today characterize digital-native companies. It will innovate rapidly, launching new features in days or weeks instead of months. It will collaborate extensively with partners to deliver new value propositions integrated seamlessly across journeys, technology platforms, and data sets.

Source McKinsey & Company

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How to separate the Hope from the Hype from where something is actually Happening

When you hear about a generative AI use case in financial services… is it real?

Through our conversations, we’ve learned how to separate out the hype from the hope from what’s actually happening, and here’s how you can, too:

- Hype: Companies tend to only have broad pronouncements around the intent to use generative AI, or the value of generative AI, or our favorite — the value of others using generative AI. Startups may include generative AI in a pitch deck, but can’t answer first order questions around the what, where, when, why, and how.

- Hope: Companies make announcements with specificity, such as the use case, the department, the leader, the data source, the users, and/or the partners (e.g., which LLM, which tools in the model stack, etc). Startups can articulate the components of their generative AI tech stack, and detail why they’ve selected each component, but struggle to identify why customers will choose them vs others with the same stack.

- Happening: Use cases actually in production, and with known and identified measures of impact, e.g., cost savings, higher pricing, new customers, time to value for new customers, cross-sell rate, etc. Startups have assembled the stack, are building the product, and have early proof of winning against similar competition due to defensible advantages.

For those organizations interested in proceeding from hype to hope, where do you start? We’ve seen that financial services companies and fintechs have had the most success integrating generative AI within very specific workflows or tools that they’re already using (vs. inventing wholly new use cases).

In summary, the Gartner hype cycle is a useful way to look at the advance of new technologies, including generative AI in financial services. The skeptics say it’s all hype. The believers say it’s changing the industry forever. Both are true. More than anything, we know it’s a time to be humble: In most cases when you hear about a generative AI use case in financial services or fintech, it’s just too early to tell. We recognize that most of the home-building has to happen below ground before we see the frame rise above ground.

Source Bain Capital Ventures

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Microservices-based architecture: Foundation for platform banking

For most banks, successful adoption of platform banking standards will require substantial reengineering of current core banking application architecture and infrastructure. It will also call for an enterprise-wide transition toward microservices-based architecture, which is a critical enabler that allows efficient and accelerated integration with third parties, which can become the chief competitive differentiator in the platform banking ecosystem.

The current core banking architecture of a bank will have a significant bearing on the approach and level of technology transformation required to support either of the platform banking business models. While banks with legacy core banking architectures, monolithic applications with multiple point-to-point integrations and batch processing, can transform in a phased manner, while minimizing risk, through a deliberate approach with near-term and long-term objectives. Whereas banks with modern cores, typically with service-oriented and mature API-based architectures, can transform through a big-bank approach owing to their mature IT organizations.

Figures 3A and 3B illustrate a microservices-based conceptual architecture, along with the three key components, namely 1. API Gateway, 2. Service mesh, and 3. Microservices-based core, that banks need to deploy to be able to build and sustain an ecosystem of external partners. These three components are foundational for platform banking that will enable banks to integrate and provide access to third parties with open standards, data security, and scalability.

Source Delloit

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The global fintech ecosystem slows down in Q2’23

Following a spike in funding in Q1’23 (driven by Stripe‘s $6.5B round), global fintech funding decreased 48% quarter-over-quarter (QoQ) in Q2’23 to $7.8B. Deal count also fell for the fifth straight quarter to hit 845.

Using CB Insights data dig into key takeaways from our State of Fintech Q2’23 Report, including:

1. Global fintech funding falls by nearly half to $7.8B, its lowest level since 2017.

2. Funding from $100M+ mega-rounds totals $2B — a 6-year low.

3. Payments funding falls 75% QoQ, the biggest decrease across all fintech sectors.

4. LatAm & the Caribbean funding more than doubles, making it the only region to see funding grow QoQ.

5. All 5 of the quarter’s IPO exits come from fintechs based outside of the US.

Global fintech funding fell 48% QoQ to hit $7.8B in Q2’23 — the lowest level since 2017. The steep drop was due in part to Stripe’s $6.5B deal in March, which inflated funding for Q1’23.

Without the Stripe deal in Q1’23, this quarter’s funding would have been down 9% QoQ.

Fintech deal count also fell 22% QoQ to 845 in Q2’23. That decline was worse than the venture ecosystem as a whole, where deal count fell by 16% QoQ.

Funding from $100M+ mega-rounds fell 78% QoQ to hit $2B in Q2’23 — a 6-year low. Mega-rounds represented just 26% of total funding in the quarter, a far cry from the recent high of 67% share in 2021.

The number of mega-rounds also fell to its lowest level in 5 years, dropping 20% QoQ to 12 rounds in Q2’23. The quarter’s top deal went to brokerage platform Clear Street, which raised a $270M Series B round.

Funding to payments companies fell 75% QoQ in Q2’23 to $2B, marking a 6-year funding low for the sector and the biggest QoQ decrease across all fintech sectors. Deal count also declined, dropping by 14% QoQ to 148.

Every other fintech sector — including banking, insurtech, wealth tech, and capital markets — saw funding declines. Digital lending experienced the biggest drop after payments, down 44% to $1B.

Meanwhile, early-stage players dominated fintech deal volume. In 2023 so far, early-stage companies have received 72% of all deals — a 5-year high if the trend holds through the end of the year.

Source CB Insights