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

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.

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.

What is one practical use case where agentic AI will deliver real value in financial services this year? One practical place agentic AI will show real value fast is in underwriting support and deal triage for small business and real estate lending. Not the basic stuff like scanning PDFs, but the kind of system that notices what is missing, asks for it, matches numbers across tax returns, bank statements, and credit reports, and flags anything that does not line up. It can also surface risk concentrations and draft a clean credit memo that a human can review and challenge. In most lending shops, the slowdown is not a lack of data, it is the effort it takes to pull scattered inputs into one clear story about risk and repayment. When an AI agent can run those loops quickly, recheck cash flow assumptions, and pressure test projections as new information comes in, decisions get faster and more consistent. The win is not replacing credit teams, it is letting them spend their time on judgment instead of assembly. When that happens, capital moves more efficiently, and the impact shows up in throughput and decision quality within the year.

One practical use case is using agentic AI to review loan contracts and identify personal guarantee exposure for business owners. I work with business owners making mortgage and financing decisions every day, and many founders sign guarantees without realizing they become personally liable. Agentic AI can scan documents, highlight guarantee clauses, and clearly identify who is on the hook and the scope of liability. That insight lets founders make different borrowing choices and prompts them to ask lenders about limiting or negotiating guarantees. In short, automated guarantee review is a near-term application that helps entrepreneurs understand and manage personal liability risks.

I overhauled our fintech onboarding when manual KYC checks bogged us down, causing two-day delays per client. To solve this, I deployed agentic AI that autonomously pulls documents from emails and cross-checks them against global databases. Unlike basic automation, these agents chain decisions end-to-end—approving 80% of cases solo and only flagging edge cases for human review. A/B tested over 3 months, this system slashed our onboarding time by 70%, dropping the wait from 48 hours to just 4 hours. We achieved 95% approval accuracy while saving $50,000 yearly on administrative overhead. The breakthrough is clear as agentic AI eliminates the need for "babysitting" data. By allowing AI to handle the cognitive heavy lifting of verification, we transformed a major bottleneck into a scalable competitive advantage.

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.

I am a Fintech CEO who has saved banks $28M in compliance costs, and I believe the biggest win for AI this year isn't chatbots. It's the Agentic AI Investigators. We are moving away from simple alerts to autonomous agents that can actually "think" through a fraud case. The main Problem is the "False Positive" trap. Right now, compliance teams spend about four hours on a single Anti-Money Laundering (AML) review. To make matters worse, 73% of those alerts are false alarms. It's a massive waste of human talent. The AI agent delivers real value in certain ways. Instead of just flagging a transaction, the AI agent gathers all the evidence. It pulls ID documents, checks IP addresses, and analyzes 47 different data points on its own. Instead of just flagging a transaction, the AI agent gathers all the evidence. It pulls ID documents, checks IP addresses, and analyzes 47 different data points on its own. Because the AI can cross-reference data much faster than a human, we've seen false positives drop by 68%.

Agentic AI will deliver real value this year in financial crime operations, especially AML alert triage and investigation. Instead of analysts spending hours hunting across systems, an AI agent can pull the relevant customer and transaction context, assemble a case summary, draft the investigation narrative, and escalate only the genuinely risky exceptions for human sign-off. That is high impact because it removes coordination work, shortens response time, and improves consistency without handing over the final compliance judgement.

The practical use case involves an agent that helps prevent bill shock and overdrafts before they occur. Instead of sending generic alerts, it forecasts cash flow based on recurring patterns and recent anomalies. It then proposes a solution that the customer can easily accept. For instance, it might suggest moving a transfer by two days, splitting a payment, or activating a temporary buffer. The agent also drafts a message to the customer that feels timely and personalized. This approach reduces frustration caused by fees and helps lower churn. It works by focusing on small, everyday moments where customers feel stress. Brands build loyalty when they help customers avoid issues and not when they apologize after fees are charged.