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- Indexed articles, last 90 days
- 10
- Latest publication
- Sep 30, 2026
- Outlet visibility, for American Banker
- Top 1M sites
- Earliest in this view
- Jul 14, 2026
Latest articles
Musings about Muse, Meta's red-hot new AI agent (opens the original)
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Meta's new AI agent, Muse, took just 10 days to reach No. 1 on the U.S. free iPhone app chart. Its promise is easy to appreciate: An assistant that can shop and make phone calls on our behalf could relieve us of some everyday chores. For those of us following AI adoption in financial services, however, developments surrounding Muse's launch are more instructive than the consumer hype might suggest. For banks, consumer-facing apps like Muse serve as a crucial test for the operational and security
AI ROI estimates tend to leave one thing out (opens the original)
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A big mistake to avoid when calculating the ROI of an AI investment is failing to account for costs created when the system gets things wrong. A new risk survey conducted by American Banker's Market Intelligence unit underscores why the downside matters. Eighty-one percent of respondents characterized AI model risk as critical, high or at least moderate. About 60% also considered a significant loss from AI-enabled social engineering or deepfake fraud somewhat or very likely over the next 12 mont
Benchmarking AI adoption: What US Bank's playbook tells us (opens the original)
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Six actions outlined by U.S. Bank's Chief AI Officer Prashant Mehrotra offer a real-world framework for turning AI experimentation into measurable, scalable and accountable value. Mehrotra's approach – outlined at American Banker's 2026 Digital Banking Conference – includes the following steps: Pick the right work, define outcomes, productize and reuse, invest in skill development, scale responsibly and prepare for agents. This analysis will map Mehrotra's six actions to a two-dimensional framew
Three simple steps for banks to measure AI's ROI (opens the original)
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Lloyds Banking Group has set an ambitious target for its next phase of transformation. Under Accelerate 2030, its "simplify to outperform" strategy includes a digital and AI productivity push and about £2 billion in gross cost saving. The harder question is how much of those savings can ultimately be attributed to AI. Banks can make that easier by doing three things before a major AI project starts: Define the business outcome, establish the baseline and make one cross-functional team accountabl
Beyond ROI: How banks can better measure AI impact (opens the original)
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Banks face three interconnected challenges as they move from experimenting with AI to integrating it into their businesses: making AI adoption measurable, scalable and accountable. The measurement challenge is coming to the fore as rising costs force executives to explain what their growing portfolio of AI initiatives actually produces. The early objective of generative AI adoption was to encourage experimentation. Banks tracked how many employees had access, how often they used the tools and ho
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