What is one practical use case where agentic AI will deliver real value in financial services this year?

Agentic AI will provide value this year in fraud operations by acting as a case triage and action agent. Analysts are often overwhelmed with alerts that require the same checks to be repeated across multiple systems. This process slows down response times and increases false positives. By deploying an AI agent, we can pull the relevant evidence for each alert and create a decision packet. The agent will verify recent device patterns and transaction context before recommending the next action based on policy. If confidence is high, it can lock the session or request step-up authentication. If confidence is low, it will close the case with notes and supporting evidence. This approach saves time per alert, reduces queue time and improves consistency, allowing for faster containment and better focus for analysts.

An AI agent can monitor cash positions across accounts and forecast short-term needs based on real-time inflows. It can then create a daily funding plan and suggest transfers according to rules set by the treasury team. Humans remain in control by approving these suggestions. This helps avoid surprises at the end of the day and reduces manual spreadsheet work. The value of this system appears quickly because it eliminates the need for constant updates. The agent can explain each recommendation and highlight the risks if no action is taken. It can also simulate different stress scenarios, like delayed settlements. This helps banks and fintechs avoid overdrafts and reduce idle cash, which is especially useful in a volatile rate environment.

What is one practical use case where agentic AI will deliver real value in financial services this year? An extremely useful use case here is intelligent underwriting collaboration in non-QM mortgage and investor lending. In most loan origination surroundings, the bottleneck isn't document availability it's reconciling income streams, rental proforma scenarios, bank statements, entity set-ups and appraisal data with a cogent narrative of creditworthiness. An AI which is able to be proactive with the borrower, but also identify missing documentation, cross reference cash flow assumptions and create an underwriting summary for review can save huge amount of cycle time. It's not in replacing loan officers or underwriters at all; it is about relieving them of administrative assembly to concentrate on risk judgment and structuring. DSCR and multi-property financing in particular, where income analysis can be stacked and driven by entity, this level of intelligent coordination increases consistency and throughput into the same fiscal period. Standardization and responsiveness with underwriting This in turn leads to more predictable capital deployment to support revenue growth as well as borrower experience.

This year, the biggest advantage for agentic AI automating the first-stage 'eyeball exam' of SARs and Fraud investigation. There is now agentic AI built to autonomously perform the leg work of investigating instead of AI simply summarising the data. Rather than having a human analyst pull historical transaction data from older systems manually and compare it against external watch lists, an agent can do all the investigative work and produce the paperwork to be filed for review. Historically, in financial services, the primary bottleneck has been that data does not flow easily between different systems ('swivel-chair integration') due to the need of moving data from one system to another by a human being. The agentic AI serves as the connector between functions and runs across the workflow. The organisations achieving the highest return on their investment view these agents like they would view a junior-level analyst under with clear guardrails and do not give them ultimate decision-making authority. The goal is to shorten the time it takes to resolve a dispute from days to minutes, while maintaining a human as the final stop for compliance purposes. It's easy to get seduced by the hype of completely autonomous finance but, the immediate and practical benefit from this technology is retrieving the considerable time an analyst spends collecting data. When you automate the collection and processing of data, you free up your highly valued resources to focus on providing judgements - which is where you'll see the greatest reduction in risk. Implementing this technology requires a re-think of how we perceive risk, i.e., shifting from periodic audits to real-time monitoring. This can be a daunting transition for traditional institutions and there is no sure way of building appropriate levels of trust and technology maturity other than to begin the process with a small, high-friction workflow.