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VISA launched a suite of integrated APIs and a commercial partner program to AI platforms, enabling developers to deploy VISA's AI commerce capabilities securely and at scale. With VISA Intelligent Commerce, AI agents can find, shop and buy for consumers based on their pre-selected preferences. Each consumer sets the limits, and Visa helps manage the rest." VISA Intelligent Commerce offers (i) AI-ready cards that replace card details with tokenized digital credentials, (ii) AI-powered consumer personalization (consumers share basic Visa spend and purchase insights with their consent to improve agent performance and personalize shopping recommendations), and (iii) simple and secure AI payments allowing consumers to easily set spending limits and conditions, providing clear guidelines for agent transactions.

The IMF published a chapter of the October 2024 edition of the Global Financial Stability Report that assesses recent developments in AI and Generative AI and their implications for capital markets, using new analytical work and results from a global outreach to market participants and regulators. Evidence from labor markets and patent filings suggests that adoption of AI in capital markets is likely to increase significantly in the near future, and AI could cause large changes in market structure through the greater and more powerful use of algorithmic trading and novel trading and investment strategies. AI may reduce some financial stability risks by enabling superior risk management, deepening market liquidity, and improving market monitoring by both participants and regulators. At the same time, new risks may arise, including increased market speed and volatility under stress, more opacity and monitoring challenges of non-bank financial institutions, increased operational risks as a result of reliance on a few key third-party AI-service providers, and increased cyber and market manipulation risks. Many of these risks are addressed by existing regulatory frameworks, but important new and unforeseen developments may arise.

This report examines both the benefits and risks big data and AI can bring to the insurance industry. In particular, this reports discusses how the OECD Recommendation on Artificial Intelligence, and the European Commission's Independent High-Level Expert Group on Artificial Intelligence's (HLAG AI) Ethics Guidelines for Trustworthy AI should be considered in the context of the insurance sector. The report concludes with policy areas in which policy makers may consider action in the insurance sector in relation to big data and AI going forward.

Banco Central do Brasil (BCB) is looking to integrate artificial intelligence (AI) based apps into its Drex central bank digital currency (CBDC). Specifically, it plans to incorporate instant payment methods like Pix and leverage AI technologies similar to OpenAI's ChatGPT. Central bank President Roberto Campos Neto envisions a future where multiple banking apps are replaced by a single, comprehensive super app that aggregates financial data and uses AI to offer personalized financial services.

Just as the invention of APIs enabled software by allowing systems to communicate and operate autonomously, stablecoins are becoming the financial APIs of artificial intelligence. APIs enable software to seamlessly exchange data and execute tasks without human intervention. Similarly, stablecoins provide the liquidity, programmability and efficiency AI agents need to exchange value and execute transactions in a fully autonomous world. Stablecoins like USDC bring instant, secure and programmable money into the workflows of AI systems. Autonomous agents, operating on behalf of individuals or businesses, rely on fast and efficient monetary systems to optimize their performance. Together, AI and stablecoins are creating a foundation for "self-driving economies," where value moves as seamlessly as data does today.

In China, online lenders are filling the credit gap, leveraging cutting edge technology including artificial intelligence (AI) to make lending decision in just a second. Alibaba's MyBank and Tencent's WeBank make their lending decisions using AI, processing vast amounts of user data and information gathered from the mobile payment services they operate.

"Apple Intelligence is the personal intelligence system that brings powerful generative models to iPhone, iPad, and Mac. For advanced features that need to reason over complex data with larger foundation models, we created Private Cloud Compute (PCC), a groundbreaking cloud intelligence system designed specifically for private AI processing. For the first time ever, Private Cloud Compute extends the industry-leading security and privacy of Apple devices into the cloud, making sure that personal user data sent to PCC isn't accessible to anyone other than the user - not even to Apple. Built with custom Apple silicon and a hardened operating system designed for privacy, we believe PCC is the most advanced security architecture ever deployed for cloud AI compute at scale."

The European Investment Fund and the European Commission have put up €100 million for a dedicated investment scheme that will make capital available to AI and blockchain projects via VC funds or other investors.

The Monetary Authority of Singapore (MAS) has launched two programmes to promote the responsible adoption of artificial intelligence and data analytics for financial institutions. The two projects are aimed at developing fairness metrics in credit risk scoring and customer marketing, and helping financial institutions validate their data-based systems. https://www.mas.gov.sg/news/media-releases/2020/fairness-metrics-to-aid-responsible-ai-adoption-in-financial-services

Chavanette Advisor's Hubert Knapp ran through some of the applications of artificial intelligence (AI) to central bank digital currencies (CBDCs). These include liquidity management at the micro and macro level, cross-border payments, and fraud detection.

"BloombergGPT represents a significant leap forward for the financial and AI communities. Currently, the model is not available publicly and there is no API, much less a chat interface, to access it. It is unclear when or if public access will be available or even the current incarnation of BloombergGPT will still see further revisions. The BloombergGPT team concludes in their paper that "we err on the side of caution and follow the practice of other LLM developers in not releasing our model" and will not make the model available to the public."

Elliptic collaborated with MIT-IBM Watson AI Lab researchers using machine learning software to analyze 203,769 bitcoin node transactions worth roughly $6 billion in total. They found that only 2% of the 200,000 bitcoin transactions in the data set were deemed illicit, although roughly 77 percent remained unclassified.

"Artificial intelligence, such as the Bank of England Bot, is set to take over an increasing number of central bank functions. This column argues that the increased use of AI in central banking will bring significant cost and efficiency benefits, but also raise important concerns that are so far unresolved."

David Longworth, former Deputy Governor of the Bank of Canada on the major macro-level risks arising from current and future digital financial innovation and what the implications are for Canadian regulators. Regulators should enact a number of policies, including requiring the use of Explainable AI (XAI) in lending decisions, taking care not to rush into open banking regulations, extending the coverage of stress tests to include stresses relating to borrowing from non-bank financial intermediaries, and increasing the amount of data on lending and short-term financing from lenders, whether regulated or not.

The Monetary Authority of Singapore is working with financial industry partners to create a framework for financial institutions to promote the responsible adoption of Artificial Intelligence and Data Analytics.

Suncorp ran Watson silently alongside its consultants for six months, with the AI returning the most accurate insurance claims assessments.

Distributed ledger technology (DLT) is one of 20 focus areas on the U.S. National Security Council (NSC) shortlist of critical and emerging technologies. The NSC's strategy calls for investing in, developing, adopting and promoting the priority technologies. Also on the shortlist: AI, data science, quantum computing and "space technologies," weapons of mass destruction mitigation technologies, and others. https://www.whitehouse.gov/wp-content/uploads/2020/10/National-Strategy-for-CET.pdf

As Shannon taught us, effective communication is not just about capacity-it's about encoding information to suit the channel. By leveraging multimodal interaction, predictive AI, and hybrid approaches to interface design, we can create intelligent systems that amplify human capabilities. As such, it's likely that the next era of human-computer interaction will not be about replacing GUIs with conversational interfaces, but harmonising the two into systems that are greater than the sum of their parts.

What if governments concede that it's both impossible and unnecessary to formally identify poor people at the on and off-ramps? What if they accepted an AML model that treats the endpoints as unidentified nodes and, drawing on these new analytic tools, actively managed access to networks based on behavior not identity? Here, ongoing research in machine learning and high-performance computing by the MIT-IBM Watson AI Lab in collaboration with Elliptic could be a catalyst.

The US House of Representatives passed the "Advancing Innovation To Assist Law Enforcement Act" to require the Financial Crimes Enforcement Network to conduct a study on the use of blockchain, AI, and other technologies by the agency and provide a report to the House Financial Services Committee and the Senate Banking Committee.