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AI agent governance takes focus as regulators flag control gaps

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Australia’s financial regulator has warned financial firms that AI agent governance and assurance practices are poorly governed. The warning comes as banks and superannuation trustees expand AI in internal and customer-facing operations. The Australian Prudential Regulation Authority said it conducted a targeted review of selected large regulated entities in late 2025 to assess AI adoption […]

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SAP brings agentic AI to human capital management

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According to SAP, integrating agentic AI into core human capital management (HCM) modules helps target operational bloat and reduce costs. SAP’s SuccessFactors 1H 2026 release aims to anticipate administrative bottlenecks before they stall daily operations by embedding a network of AI agents across recruiting, payroll, workforce administration, and talent development. Behind the user interface, these

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Canada’s Scotiabank preps for its AI future

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Scotiabank has launched an AI framework, Scotia Intelligence, for data and AI operations that joins various platforms, data oversight, and software tools into a single instance. According to a press release from the bank, the stated purpose of Scotia Intelligence is to give employees, especially client-facing teams, access to AI under the bank’s existing governance

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Strengthening enterprise governance for rising edge AI workloads

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Models like Google Gemma 4 are increasing enterprise AI governance challenges for CISOs as they scramble to secure edge workloads. Security chiefs have built massive digital walls around the cloud; deploying advanced cloud access security brokers and routing every piece of traffic heading to external large language models through monitored corporate gateways. The logic was

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Companies expand AI adoption while keeping control

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Many companies are taking a slower, more controlled approach to autonomous systems as AI adoption grows. Rather than deploying systems that act on their own, they are focusing on tools that assist human decision-making while keeping tight control over outputs. This approach is especially clear in sectors where errors carry real financial or legal risk.

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Experian uncovers fraud paradox in financial services’ AI adoption

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The same technology that financial institutions deploying is being weaponised against them. That is the core tension running through Experian’s 2026 Future of Fraud Forecast, and it’s a tension the company is in a position to name because it sits on both sides of it. According to FTC data cited in the forecast, consumers lost

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KPMG: Inside the AI agent playbook driving enterprise margin gains

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Global AI investment is accelerating, yet KPMG data shows the gap between enterprise AI spend and measurable business value is widening fast. The headline figure from KPMG’s first quarterly Global AI Pulse survey is blunt: despite global organisations planning to spend a weighted average of $186 million on AI over the next 12 months, only

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Secure governance accelerates financial AI revenue growth

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Financial institutions are learning to deploy compliant AI solutions for greater revenue growth and market advantage. For the better part of ten years, financial institutions viewed AI primarily as a mechanism for pure efficiency gains. During that era, quantitative teams programmed systems designed to discover ledger discrepancies or eliminate milliseconds from automated trading execution times.

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Glia wins Excellence Award for safer AI in banking

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Glia, a customer service platform providing AI-powered interactions for the banking sector, has been named a winner in the Banking and Financial Services Category at the 2026 Artificial Intelligence Excellence Awards. The awards recognises achievements in a range of industries and use cases, spotlighting “companies and leaders moving AI beyond experimentation and into practical, accountable

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Assessing AI powered price forecasting tools in currency markets

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As artificial intelligence becomes a driving force in financial prediction, the reliability of its forecasting tools faces increasing scrutiny. Many traders question whether claims of high accuracy translate into consistent results under live market conditions. Understanding how these AI systems are evaluated reveals important distinctions between performance in theory and practice. Few financial domains are

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