AI & Machine Learning × Financial Services
Production-grade AI agents for retail banks, commercial banks, and capital-markets operators: KYC triage, AML investigation, dispute resolution, branch-banker copilots, and customer-service automation. Built by CORTEX dept leads against eval frameworks, tool-use approval gates, and the audit trails your second-line risk and regulator will actually accept.
The problem
Banking buyers come to AI agents with the same ask every quarter: deflect tier-1 service volume, accelerate KYC and AML investigations, summarize dispute cases, and put a relationship-banker copilot in front of every advisor. The pattern that fails them is a chatbot bolted onto a retrieval layer: confident, untested, and unable to evidence its decisions when a model-risk officer or supervisor asks. The first time the agent waives a fee it shouldn't, the program goes to the freezer.
Production banking agents need three things consumer-grade AI doesn't have: a tool-use boundary that's auditable line-by-line (every action logged, every approval gate documented), an eval framework calibrated against ground truth from your actual case files, and a model-risk frame (SR 11-7, OCC bulletin 2011-12) the second line will sign. We design those in from week one, not as a phase-2 cleanup after the regulator asks.
Where it ships
Concrete applications where ai & machine learning unlocks measurable value inside financial services delivery constraints.
55%
analyst time saved
Reviews onboarding packages, flags identity-document mismatches, runs sanctions and PEP screens, and drafts the analyst note. Tools: ID-verification APIs, sanctions feed, document store, case-management write with approval gate.
3.2×
alerts cleared per analyst
Triages transaction-monitoring alerts: pulls counterparty history, surfaces typology matches, drafts SAR-narrative candidates. Bounded to read-only on core systems; SAR filing stays human.
Handles cardholder disputes: pulls transaction history, matches against chargeback rules (Visa / Mastercard / network), drafts resolution. Issues credits within policy thresholds; escalates exceptions.
In-branch and RM-facing agent: customer-360 summarization, next-best-action suggestions, regulatory-disclosure prompts. Read-only on customer data; suggestions require banker confirmation before customer contact.
Tier-1 retail-banking service: balance, transaction history, card replacement, simple service requests. Tools: core-banking read APIs, card-services, IVR / chat platform integration. Approval gate on monetary transactions.
Agent for ops and complaint teams: case-summarization, regulatory-categorization (Reg E / Reg Z / CFPB taxonomy), root-cause clustering. Inside Pega / NICE / ServiceNow workflows; not a separate UI.
How we engage
Each phase has a deliverable, an owner, and an acceptance criterion calibrated to financial services delivery.
Three-week discovery: candidate use cases, customer-impact tier per use case, model-risk classification (SR 11-7 tiering), data-residency and consent frame, second-line stakeholder map. Output is a prioritized roadmap with approval gates and HITL checkpoints designed before any code.
Build the eval set from real case files: labeled by your senior analysts, not synthetic data. Metrics: accuracy, refusal rate, escalation rate, false-positive cost. Agent ships against the eval; no eval, no production.
Tool-use whitelist with explicit approval gates on monetary actions, customer contact, and regulatory submissions. Per-call logs with prompt + tool calls + decisions + reviewer-of-record. Drift monitoring against the eval set; alerting wired to risk and ops.
Model-risk documentation (SR 11-7-aligned) handed to the second line. Weekly performance review with risk + business; monthly model-update gate; quarterly model-risk recertification. The cadence isn't optional in regulated banking; we build for it.
Capabilities
Stack
Compliance overlay
Every financial services engagement carries the evidence collection that procurement and audit teams expect on day one.
Every agent we ship into a regulated banking workflow is documented as a model: development data, evaluation methodology, ongoing monitoring, and effective challenge by an independent function. We hand the documentation to your second line in their format, not a vendor's.
Customer-data access bounded by purpose-of-use and consent. Per-call data lineage logged. Data-export, right-to-deletion, and right-to-rectification flows tested. Data residency configurable per jurisdiction (EU, US, GCC).
AML agents support, never replace, the BSA officer's judgment. Reg E dispute-handling agents track timeline obligations explicitly. Reg Z disclosures required during banker-copilot interactions are surfaced and logged. The agent doesn't violate a regulation by accident; the design prevents it.
Selected work
Where this fits
Common questions
It will if you scope it correctly. The framing that works: the agent is a model, it gets documented as a model (SR 11-7 / OCC 2011-12), and the tool-use boundary defines what decisions are model-driven vs. human-of-record. Refund-issuance, SAR-filing, and credit decisions stay human; the agent triages, summarizes, and recommends. With that scoping, model-risk approval is achievable. We've shipped it.
Three guardrails. (1) Retrieval-grounded: the agent answers from your case data, policy, and product disclosures, not open generation. (2) Refusal-first: out-of-scope queries escalate; the agent doesn't guess. (3) Eval-checked: accuracy and policy-conformance rubrics catch regressions before they ship. Hallucination is a design problem, not an inherent property of LLMs.
Per-call data lineage is logged; data residency is configurable per jurisdiction (we deploy in Azure / AWS regions appropriate to your customer base). Consent is wired into the tool layer: agents can't access customer data outside a documented consent envelope. Data-export, right-to-deletion, and right-to-rectification flows are tested as part of acceptance.
Yes, and this is often where the value is largest. Agents can summarize a quarter of cases for a regulatory exam, draft the response to a Matter-Requiring-Attention, or generate the consistency-check view across a Reg E queue. The agent doesn't sign the response; the regulatory-relations function does. But the prep time falls 60–80%.
On AML alert triage: 2.5–4× alerts cleared per analyst within nine months; false-positive review time falls 60–75%. On dispute resolution: 40–55% cycle-time reduction. On KYC: 50–70% analyst-hours saved on standard-risk onboarding. Customer-service tier-1 deflection: 50–65% with CSAT held vs. human baseline. Economics work above ~50,000 monthly customer interactions or ~3,000 monthly alerts.
Discovery + eval-set build: 4–6 weeks, $80K–$160K. Production agent build (single use case): 5–8 months, $400K–$1.0M. Multi-agent estate (KYC + AML + dispute + service): 9–14 months, $1.2M–$3.0M. Managed services post-launch: $30K–$120K monthly retainer covering eval iteration, drift monitoring, model updates, and the SR 11-7 recertification cadence.
Talk to us
A senior engineer plus the CORTEX department lead joins the first call, both with prior financial services delivery experience.