AI & Machine Learning × Financial Services
End-to-end intelligent automation for retail banks, commercial banks, insurers, and capital-markets operators: document understanding, decisioning, exception handling, and orchestration. Built by CORTEX dept leads to clear the bar pilot RPA never could: auditable lineage, regulator-defensible decision logs, and the model-risk frame the second line will sign.
The problem
Banks and insurers come to intelligent automation with a backlog of operational pain: trade-confirmation reconciliation, claims FNOL processing, mortgage-document indexing, KYC-package handling, complaint-routing, dispute-resolution workflows. The pattern that fails is point-tool RPA: fragile bots that break when a vendor changes a screen, with no audit trail the second line can defend, no decisioning the auditor can replay, and no path from pilot to enterprise scale.
Production intelligent automation in regulated finance needs three things commodity RPA doesn't have: document understanding that doesn't break on layout drift, decisioning models that are documented to SR 11-7 / OCC 2011-12, and orchestration that integrates with the existing core / case-management / risk stack instead of working around it. We design for those from day one, and we ship with the audit trail and the BCP runbook the regulator will actually read.
Where it ships
Concrete applications where ai & machine learning unlocks measurable value inside financial services delivery constraints.
78%
STP rate on confirms
Document-understanding on incoming confirms (PDF, SWIFT, fax-scanned), exception-flagging against trade book, auto-resolve within tolerances, escalation otherwise. Replay-able decision lineage on every action.
55%
cycle-time reduction
Intake of the mortgage document package, extraction of borrower / property / income data, validation against underwriting criteria, auto-route to underwriter with pre-decision recommendation. Underwriter decides.
Insurer claims first-notice-of-loss intake, document indexing, coverage validation, fraud-signal screening, auto-adjudication within policy thresholds. Adjuster reviews exceptions and high-value claims.
Reg E / Reg Z / CFPB-aligned complaint intake, categorization, regulatory-timeline tracking, evidence aggregation, draft response generation. Compliance reviews and signs every regulator-facing reply.
Onboarding and periodic-review document intake, identity validation, sanctions / PEP screening, source-of-wealth narrative drafting, and risk-tier recommendation. Final-review stays human.
Cross-system reconciliation across core, sub-ledgers, and external feeds. Break detection, classification, and resolution within tolerance. SOX-control evidence captured automatically; audit-ready by design.
How we engage
Each phase has a deliverable, an owner, and an acceptance criterion calibrated to financial services delivery.
Three-week discovery: process inventory, exception taxonomy, regulatory-control map (Reg E / Reg Z / SOX / FFIEC), failure-mode analysis on existing bots. Output is a prioritized roadmap that prioritizes processes where document drift, decisioning judgment, or audit lineage make pure RPA fail.
Document-understanding eval set built from real (de-identified) examples labeled by your senior ops staff. Decisioning eval calibrated against historical case outcomes. Metrics: extraction accuracy, decisioning agreement with senior reviewer, false-resolve rate, replay-correctness.
Per-decision lineage: every extracted field, decision factor, model version, prompt, and outcome logged with replay capability. SR 11-7-style model-risk documentation handed to second line. SOX evidence captured at the control level. BCP runbook for failure scenarios: auto-failover, manual-fallback, and reconciliation paths.
Weekly performance review with risk + ops in the first 90 days; monthly thereafter. Quarterly model-risk recertification. Annual control-effectiveness review with internal audit. The cadence is built in; we don't bolt it on after the regulator asks.
Capabilities
Stack
Compliance overlay
Every financial services engagement carries the evidence collection that procurement and audit teams expect on day one.
Decisioning components documented as models: development data, evaluation methodology, ongoing monitoring, effective challenge by independent function. Documentation handed to your second line in their format, not a vendor's.
Every automated control captures evidence at execution: input, decision logic, output, reviewer-of-record, timestamp. Annual control-effectiveness review supported with replay-able lineage. FFIEC IT-handbook expectations on change management, vendor risk, and resilience built into the operating model.
Regulatory-timeline obligations tracked explicitly within the orchestration layer (not just monitored externally). Complaint and dispute workflows aligned to CFPB taxonomy. GLBA customer-data access bounded by purpose-of-use; per-call lineage logged.
Selected work
Where this fits
Common questions
RPA records-and-replays a human's clicks; it works until a screen changes or a document layout drifts. Intelligent automation combines document understanding (handles layout drift), decisioning (handles judgment), and orchestration (handles workflow). The two are complementary: RPA is fine for fixed screens; intelligent automation handles the cases RPA can't. We often modernize RPA estates by lifting the brittle judgment-and-document parts into intelligent automation while keeping the stable click-replay parts in RPA.
Yes, when scoped correctly. The decisioning components are documented as models (SR 11-7 / OCC 2011-12). The orchestration components are documented as automated controls under SOX. Every decision is replay-able. Internal audit gets the lineage they ask for; model risk gets the development-and-monitoring methodology in their format. We've shipped through model-risk and SOX-audit cycles at multiple banks; the pattern is well established.
Three pivots. (1) Scope to processes where the value compounds: confirms, claims, complaints, KYC, not isolated single-screen automations. (2) Build the audit and BCP layer from week one, not as phase-2 cleanup. (3) Stand up a CoE with second-line and ops representation that owns model-risk recertification, drift response, and incident review. Pilots stall when these are skipped; programs scale when they're built in.
Three layers. (1) Confidence-based routing: low-confidence cases auto-route to a senior reviewer; high-confidence within-policy auto-resolve; everything in between routes to the standard reviewer. (2) Replay capability: any decision can be replayed with the original inputs and model version, so misfires are diagnosable. (3) BCP runbook: if a model component fails, the orchestration layer falls back to manual queueing with documented SLAs. The regulator asks about this; we ship the runbook with the system.
Trade confirmations / reconciliation: STP improvements of 30–45 percentage points; ops productivity 2–3×. Mortgage / loan processing: 40–60% cycle-time reduction. Claims FNOL: 30–50% touchless adjudication on standard claims. KYC periodic review: 50–70% reviewer-hours saved. Economics work above ~10,000 monthly volume on a single use case, or earlier when audit / regulatory cycle time is binding.
Discovery + eval / control mapping: 4–6 weeks, $100K–$200K. Production build (single use case): 6–10 months, $500K–$1.4M. Multi-process estate (3–5 processes): 12–18 months, $1.8M–$4.5M. Managed services post-launch: $40K–$160K monthly retainer covering model-risk recertification, control-evidence audit, drift monitoring, and ops support.
Talk to us
A senior engineer plus the CORTEX department lead joins the first call, both with prior financial services delivery experience.