Snippings

The Swedish fintech Klarna provide an interesting case study on the deployment of AI. According to Sebastian Siemiatkowski, Klarna’s CEO, their sales and marketing expenses fell 16% compared with the same period in 2023, while customer service and operations expenses shrank 14%. Notably, it achieved those cost savings — much of which are credited to the use of AI — while generating 23% higher revenues. AI is not free, of course. Klarna’s technology and product development expenses climbed 17% over the same period. The figures show that Klarna’s cost savings more than offset the higher technology costs. Overall operating expenses fell 2% and the company posted a small profit on a net income basis in the third quarter. 

(The value of the AI cost savings is highlighted by substantial increases in other non-operating expenses. For instance, Klarna reported a 44% increase in losses from people not paying their loans back while funding costs rose 67%.)

The company has trimmed its workforce down from 5000 to 3800 over the last year, and claims that nine out of 10 employees already use AI in their daily work, but is aiming to hire over 100 engineers by 2025 in Poland. Siemiatkowski says that the new Warsaw hub will play a pivotal role in the company’s broader strategy to lead AI adoption.

Overall then Klarna was able to use AI to help to manage processing and servicing costs, which rose more slowly than revenue.

 

 

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he Coalition for Content Provenance and Authenticity, led by the Linux Foundation, is an open standards body working to establish trust in digital media. By embedding metadata and watermarks into images, videos, and audio files, the C2PA specification makes it possible to track and verify the origin, creation, and any modifications of digital content.

From: How Apple, Google, and Microsoft can save us from AI deepfakes | ZDNET.

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A digital euro would strengthen Europe’s financial sovereignty and resilience because it would be built with European technology and infrastructure. It would empower Europe to independently develop and manage digital payment solutions, supporting the further deepening of the Single Market.[
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From: From dependency to autonomy: the role of a digital euro in the European payment landscape.

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A study from Juniper Research found that the money lost to online fraud will rise from $44bn in 2024 to $107bn in 2029, an increase of 141%.

The research, Global Merchant Fraud Prevention Market 2024-2029, cites the role of AI in making attacks on the ecommerce ecosystem more sophisticated. In particular, the report credits the use of deepfakes to defeat verificatio systems as “a key threat”.

From: Ecommerce fraud to exceed $100bn by 2029.

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Key among the strategies that central banks may consider is addressing those factors that drive CBDC adoption. We identify three potential drivers – design alignment with consumer preferences, effective information dissemination, and leveraging network effects from emerging payment technologies.

From: Consumer demand for central bank digital currency as a means of payment.

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Typical of the use cases being explored by retail banks are these set out in the FT: customer help, pricing, cyber defence and financial crime detection.

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According to the payments network, the VFC is being rolled out in the U.S. with the Affirm Card (already in the market), the buy now, pay later (BNPL) provider’s debit card that allows consumers to opt to pay over time for transactions made through the Affirm app. And elsewhere, United Arab Emirates (UAE) neobank Liv is debuting VFC to enable users to switch between multiple currency accounts from one card.

The appeal of having a single payment credential able to link and toggle between several funding source, from credit to debit to checking and back again — without having to access different apps or shuffle through plastic cards — is already showing up in the numbers.

In an interview with PYMNTS’ Karen Webster, Mark Nelsen, chief consumer product officer at Visa, observed that the company has 3 million customers using VFC with the Olive card from Sumitomo Mitsui Card Company issued in the region in April of this year.

Consumers, he said, “are actively using the product, and they’re actively switching between their debit and credit cards or buy now, pay later … and we’re seeing about 70% of customers are actually ‘flipping’ to credit.” Individuals are using debit more often for everyday spend, and credit for larger ticket items.

From: Visa Flexible Credential Goes Live in US With Affirm, and UAE’s Liv.

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Today, it is not gold or silver content that determines the intrinsic value of money, but laws and institutions that underpin it. At the same time, payments utility is determined by technological advances that can outpace changes to these laws and institutions. As a result, bad money often gets bundled with good payments. People are offered cheaper, faster, more convenient, more secure, and more accessible ways to send and receive money at the expense of the underlying money being less sound. In times of relative stability, bad money may offer a comparative advantage.

Dan Awrey calls this Gresham’s New Law and lays out a blueprint to address it. His proposals recognise that money and payments increasingly pose different sets of policy challenges which bundling can blur.

From: Gresham’s New Law – by Marc Rubinstein – Net Interest.

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Europeans could save up to €250 billion more every year if they embraced digital management tools like AI to help manage their finances. (That’s £15-30 billion in the UK alone!)7.

From: Remember when we had to use our phones to pay for things? | Visa Navigate.

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AI significantly enhances personalized budgeting by leveraging data on an individual’s spending patterns and income streams. Traditional budgeting methods often rely on generalized rules and static guidelines, which may not adequately reflect the unique financial situation of everyone. In contrast, AI employs dynamic algorithms that continuously analyze a user’s financial behavior to provide tailored budgeting advice.[1] By integrating ML techniques, AI platforms can automate best practices and adjust recommendations based on real-time financial data.[2]
This personalized approach is crucial for effective financial planning, as it allows AI to balance mathematical optimization with practical applicability. For instance, AI can create budgets that account for irregular income or unexpected expenses, offering solutions that are both realistic and actionable.[3] Furthermore, AI’s ability to set up automatic savings plans based on its analysis enables users to consistently allocate funds toward specific financial goals, such as saving for a down payment on a house or preparing for retirement.[4] This comprehensive approach not only optimizes budget allocations but also provides users with adaptive financial advice, making personal finance management more effective and user-friendly.[5]

From: TRENDS Research & Advisory – Smart Finance: How AI is Shaping the Future of Budgeting and Investing.

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