Meta CTO on What's Ahead in Artificial Intelligence / DAY 1
Andrew 'Boz' Bosworth, Chief Technology Officer & Head of Reality Labs at Meta spoke about harnessing engineering talent and his leadership priorities at Meta, including what's next for a hardware rollout in the AI era with Bloomberg's Tom Giles at Bloomberg Tech in San Francisco. (Source: Bloomberg)
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Forbes
36 minutes ago
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What Could Future Banking Look Like If AI Takes Over?
Alex Kreger, UX Strategist & Founder of the financial UX design agency UXDA, designs leading banking and fintech products in 39 countries. getty The imminent integration of AI into daily routines promises to dramatically reshape our lives over the next five years, propelled by advancements akin to ChatGPT, Gemini, Grok, etc. This shift is driven by the recognition that human capacities, while remarkable, cannot match the vast research and creative and analytical potential of artificial intelligence (AI). As we project into the future, it becomes clear that AI will also redefine digital banking experiences and grant individuals with financial capabilities that were once unimaginable. As a design strategist developing financial services for leading banks and fintech providers in 39 countries, I'm curious to envision how AI will overhaul the typical banking experience for everyday consumers. Although the complete adoption of AI across the financial sector has yet to unfold, it is crucial to anticipate its eventual impact. The question is no longer 'What if?' but rather 'How?'—and how best to brace ourselves for the changes that lie ahead. Banks already maintain enormous stores of customer data, but unlocking its true power demands cutting-edge technology. AI may well be the solution that helps institutions tackle customer demands with speed and accuracy. By channeling this data effectively, banks can provide individualized products at precisely the right time—an endeavor impossible for standard processes alone. The current data stockpile is merely a starting point. As digital tools evolve, financial institutions will gather much more data from smartphones, social networks, public service APIs, open banking APIs and IoT devices through 5G. This explosion of information calls for a robust, near-superhuman capacity to sift through the noise and pinpoint what truly matters—something AI might deliver within the next decade. In the coming years, the most significant AI-driven breakthroughs are likely to include: • Personalized Offers: Data-rich approach makes customized proposals more precise and simultaneously mitigates risks by matching the ideal product to the ideal customer. • Investment: By eliminating human biases, AI could evaluate a multitude of market and business variables to foresee investment success. • Security: AI could expedite verification by reducing the constant need for identity confirmations. • Financial Advisory: With the aid of big data and personal profiling, AI could illuminate each client's needs, generating in-depth forecasts and healthier financial practices. • Support: AI-powered bots could offer prompt, tailored solutions, greatly enhancing customer service. • Alternative Processing: AI-powered voice, gestures, neurotechnology, VR and AR interfaces will enable banking transactions beyond conventional channels. With Statista expecting generative-AI spend in banking to rocket to $85 billion by 2030, it's time for leaders to start by putting AI into their strategic plan—not just the tech roadmap. Hire a senior executive (Chief AI) who owns value creation and AI risks and spin up a cross-functional 'AI initiatives' that groups stakeholders, data scientists and product designers that move to an API-first, event-streaming service architecture so models can surface predictions (e.g., 'potential cash shortfall Friday') in real time. Early adopters are showing where the value sits, and leaders should take note. J.P. Morgan's Quest IndexGPT can generate investable indices; Morgan Stanley's Debrief can summarize adviser meetings; NatWest's Cora+ can handle nuanced customer queries. At the same time, Wall Street majors—from Goldman to Citi—are scaling internal LLM-powered co-pilots for drafting IPO documents, surfacing research or searching policies. Customer-facing assistants are already setting the bar. Bank of America's Erica has served 20 million active users, Wells Fargo's Fargo went from 21 million interactions in 2023 to 245 million in 2024 by using a privacy-first pipeline that strips PII before any LLM call. On the insight side, RBC's NOMI Forecast crunches account data to predict the next seven days' cash flow; more than 900,000 clients have generated 10 million interactions since its late-2021 launch. Generative models excel at turning trillions of events into the next best micro-experience. Commonwealth Bank of Australia's Customer Engagement Engine, for example, ingests 3.1 trillion data points and runs 2,000 real-time models, lifting loyalty with recommendations so much that mobile users now log in 67 times a month on average. The key is to couple a real-time feature store with small language models that handle intent, then let a larger model draft the personalized nudge or insight. Start with one or two journeys where better prediction or conversation will be felt within weeks—fraud alerts or an SME cash-flow coach. Ship, measure, retrain and fold the learning into a reusable component library so subsequent squads stand on the shoulders of the first. The biggest headwind is regulation: Europe's AI Act is already in force and will classify credit-scoring, KYC, trading and robo-advice models as 'high risk' by August 2026. Finding talent and culture is also an ongoing challenge. Banks are hiring aggressively, yet even Deutsche Bank admits the scarcity of seasoned AI professionals and the difficulty of embedding them in legacy teams. Third, security and trust: four in five bank leaders say they fear AI-enabled cyberattacks, and front-office chatbots can still hallucinate or breach privacy if left unsupervised. Mitigate by adopting zero-trust data-access patterns, embedding red-teaming into MLOps, and running 'constitutional' or retrieval-augmented QA layers that force a model to cite source documents. Initially, AI's role is to automate foundational tasks. Over time, however, I expect that it will evolve to deliver comprehensive solutions across all industries, including finance. After two decades of digital self-service in finance, AI can restore the conversation—context-aware, always on, and scaled to every customer. AI's full potential is truly immeasurable, and its effects on banking customer experience—and countless other sectors—will be transformative. By merging technological advancements with thoughtful user experience design, forward-looking companies can build a future where AI not only empowers individuals but also redefines entire industries. The era of AI-driven finance is fast approaching, and now is the time to prepare for its far-reaching influence. 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