Agentic AI systems orchestrate multi-step banking workflows autonomously — research, analysis, compliance review, and customer onboarding — using AI agents that plan and execute complex tasks end-to-end.
In banking, Agentic AI & Autonomous Workflows is represented by 10 published case-study records and 0 linked vendors in this directory. 10 records retain cited source URLs. The largest concentration is Investment & Capital Markets, with Document Processing & Automation the most common use case. Outcomes are attributed to each record's source when available rather than independently verified.
Limitation: Missing linked evidence is unknown and does not prove absence of capability.
Agentic AI represents the frontier of banking AI deployment: systems that don't just respond to queries but autonomously plan and execute multi-step workflows. A McKinsey report on agentic AI in banking described it as a 'paradigm shift' — moving from AI that assists individual tasks to AI that owns end-to-end processes. JPMorgan's internal AI deployments, described in their annual reports, increasingly incorporate agentic architectures where AI agents coordinate across systems to complete complex analytical and compliance tasks.
The banking workflows most amenable to agentic AI are those requiring multiple information-gathering steps followed by synthesis and action. AML investigation is a prime example: when a transaction monitoring alert fires, an investigation agent could automatically pull the customer's transaction history, retrieve news on relevant entities, check sanctions lists, review the customer's risk rating, look up similar cases, draft a preliminary investigation memo, and either close the alert or escalate with a detailed evidence package — all without human intervention for the majority of low-complexity alerts. Similar agentic architectures apply to loan origination, compliance monitoring, and customer service resolution.
The governance challenges for agentic AI in banking are significant. Unlike deterministic software, agents can take unexpected action paths, make mistakes in multi-step reasoning, and produce outputs that are hard to audit. Regulatory requirements in banking — audit trails, human accountability for material decisions, model validation requirements — shape how banks design agentic systems. The most mature deployments use 'human-in-the-loop' architectures where agents complete analytical work and present conclusions for human review before taking action.
Leading banks are deploying agentic AI with human-in-the-loop checkpoints for any material action. Agents operate within defined permission scopes — they can access read-only data, draft documents, and generate recommendations, but material actions (filing a SAR, approving a loan, executing a trade) require human authorization. Full audit trails of agent actions and reasoning are mandatory for regulated banking applications. The SR 11-7 model risk management framework is being extended to cover agentic AI, though regulators are still developing specific guidance. Banks that move fastest on agentic AI are investing heavily in AI governance infrastructure before deploying at scale.
Unnamed Neo Bank (Asia)
Goldman Sachs
Unnamed Caribbean Offshore Bank
Fiserv
Jefferies
FORUM Credit Union