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Enterprise AI for Financial Services

Enterprise AI for Financial Services

Financial services firms sit on enormous document volumes and face regulatory pressure that only grows. We build AI systems that handle the repetitive, high-stakes work so your teams can focus on judgment.

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Up to 70%
faster document review in underwriting
Up to 62%
of AML alerts auto-closed
3-6 wks
from kickoff to production pilot

What We See in Enterprise AI for Financial Services

1

Commercial loan underwriters spend 60 to 70% of their time re-keying data from tax returns, K-1s, and borrower financial statements into nCino or Encompass, then reconciling the same numbers against LexisNexis and D&B pulls before a deal memo can even start drafting.

2

AML and sanctions teams in most mid-cap banks are still clearing 600 to 900 SAR-adjacent alerts per analyst per month inside Actimize or Verafin, and roughly 95% of those alerts close as false positives after 40 minutes of manual case investigation.

3

KYC onboarding for new commercial clients stretches 45 to 90 days because identity verification, beneficial-ownership confirmation, and document collection run across Salesforce Financial Services Cloud, a separate CLM, and three vendor portals with no shared case record.

4

Finance and risk functions pull board-pack inputs from Hyperion, a separate FIS general ledger, SAS for risk, and a dozen Excel attachments, so a single month-end package consumes 80+ analyst hours and still arrives with reconciling items.

How We Help

Commercial Loan Underwriting Automation

We deploy AI agents that pull income documents, tax returns, and bank statements into a normalized credit file, spread financial statements, and pre-populate nCino with structured data plus a preliminary risk recommendation. Underwriters work inside their existing deal memo, reviewing exceptions the agent flagged instead of re-keying line items. The system grounds every extracted number to the source page so credit committee can audit the trail.

✓ 70% reduction in document review time per file and 3x underwriter capacity without new headcount

AML Alert Triage and Narrative Drafting

Our agents read every Actimize or Verafin alert, pull transaction history, counterparty data, and KYC context from the core, and auto-close clear false positives with a documented rationale. For alerts that survive triage, the agent drafts a SAR-ready narrative grounded in case evidence so investigators validate and file instead of writing from a blank page. Every decision is logged against your FFIEC exam playbook.

✓ 62% fewer alerts reaching analyst queues and 41% faster SAR narrative drafting

Regulatory Change Monitoring

Agents ingest daily feeds from the CFPB, OCC, FinCEN, and state regulators, extract the provisions relevant to your business lines, and map each change to the specific section of your policy library or control narrative it affects. Compliance officers receive a change packet with gap analysis attached instead of a 40-page rulemaking notice, with a one-click workflow to open a remediation ticket in your GRC platform.

✓ 48-hour detection on material rule changes vs. 3-week baseline

Board and Regulatory Report Generation

Rather than analysts spending five days assembling the monthly risk and finance pack, we build a pipeline that reads from your data warehouse, runs the standard cuts in SQL plus Python, and drafts narrative commentary using your prior-quarter language and disclosure conventions. Analysts edit a 90% draft in Word rather than rebuilding from PowerPoint and Excel each cycle, and variance explanations cite the source GL entries automatically.

✓ Monthly pack cycle drops from 5 days to under 6 hours

Relationship Manager Co-Pilot

Ahead of every client meeting, an agent synthesizes CRM history, portfolio performance, recent Salesforce activity, and open credit reviews into a one-page brief with suggested product conversations and flagged KYC or covenant items. Calls are transcribed and follow-ups get written back to the CRM automatically so RMs finish the day with a clean pipeline instead of three hours of admin.

✓ 11 hours/week reclaimed per RM and 25% larger book coverage

Our Services for This Industry

AI Agent DevelopmentView →
Multimodal RAG SystemsView →
Agentic AutomationView →
compliance-monitoringView →

Engagement shape

Timeline

A typical financial services engagement runs six to ten weeks. Weeks one and two are discovery: data-access interviews with the business owner, second line, and IT, plus a written integration pattern for the core, CRM, and any vendor platforms in scope. We build the eval set in week two by labeling 2,000 to 6,000 real historical cases so accuracy targets are grounded in your own data rather than a vendor benchmark.

Weeks three and four are build. The agent runs against the eval set daily and we share a scorecard every Friday. Weeks five and six are shadow mode with real users, paired review, and MRM documentation drafting. Weeks seven and eight are production cutover, with a controlled ramp on a single team, runbook handover, and a 30-day hypercare period. Broader rollouts across additional use cases follow the same pattern in parallel once the first agent is live.

Cost model

Most financial services engagements fall between $95k and $280k for the first production use case. The main drivers are integration count, whether MRM validation artifacts are in scope, and the depth of compliance documentation required for your regulators and exam cycle. A single-system AML triage agent sits near the bottom of that range. A commercial loan underwriting agent with nCino, Encompass, core, and three bureau integrations lands near the top. Ongoing platform and inference costs typically run $6k to $25k a month once in production, which we size and quote upfront before the SOW is signed.

Frequently Asked Questions

How do you handle data residency and SOC 2, FFIEC, and GLBA requirements?+
Every deployment starts with a written data-flow diagram your InfoSec and second line sign off on before any code runs. We support on-premises, VPC, and private-cloud deployments so customer data, account numbers, and credit bureau pulls never leave your security boundary. Inference runs against models hosted inside your tenant. Your data is never used to train third-party models. We produce SOC 2 control mappings, GLBA safeguards documentation, and FFIEC IT Handbook alignment for your exam file. Most of our bank customers route our architecture through their existing third-party risk review in 3 to 5 weeks.
Will your AI systems need regulatory approval before production use?+
For AML, credit, and fair-lending use cases, the answer depends on what the model is doing. We deliberately design agents that assist a human decision-maker and produce explainable, auditable outputs rather than fully autonomous decisioning. That design keeps most deployments on the right side of SR 11-7 model risk guidance, Reg B adverse action requirements, and current state AI rules. We also work with your model risk management group during scoping so independent validation is part of the timeline rather than a surprise, and we hand off full documentation for MRM's effective challenge.
How do you integrate with our core banking platform and nCino, Encompass, or Salesforce FSC?+
We've built against FIS, Fiserv DNA, and Jack Henry cores, plus nCino, Encompass, Salesforce FSC, Actimize, and Verafin. Integration is usually a mix of REST APIs, event streams through Kafka or MQ, and file-based batch when a system is older. We map the exact integration pattern during discovery and write the contract tests before any agent is deployed. Write-back is gated by your change management process, and we never bypass existing maker-checker or segregation-of-duties controls already enforced in the underlying platform.
What does a pilot cost and how long does it take?+
A focused bank pilot on one use case, for example AML alert triage or commercial loan spreading, runs 6 to 8 weeks from kickoff to production with a small group of users. Pricing for that first pilot typically lands between $95k and $180k depending on integration count, regulatory documentation scope, and whether you want validation artifacts delivered alongside. Multi-use-case rollouts run 3 to 6 months. We write a fixed-fee statement of work before kickoff so procurement, finance, and the business all sign off on the same number, and we quote production run-rate costs upfront.
Where does our data live and what goes to third-party AI vendors?+
Your transaction, customer, and underwriting data stays inside your tenant at rest and in use. Inference runs against large language models hosted either in your own Azure or AWS account, or in a dedicated private deployment we manage with zero data retention and zero training on your prompts. Where a public API is strictly better for a narrow task, we use it only on de-identified or non-PII content and document that choice in the architecture review. We also hand you the complete list of outbound endpoints so your network team can lock egress to exactly those destinations.
Who's accountable when the AI makes a bad decision?+
The accountable human is the same person accountable today, whether that's the underwriter, the BSA officer, or the CCO. Our systems surface recommendations with supporting evidence, cite source documents, and log every input and output for audit. We deliberately avoid building autonomous decisioning for anything that affects credit, suitability, or regulatory filings. For AML, the case investigator owns the close or the SAR. For credit, the underwriter owns the decision. Our job is making the human faster and more consistent, not removing them from the loop, and our contracts reflect that allocation of responsibility.
How is this different from what our Big Four advisor or core vendor already pitched?+
Big Four engagements usually deliver a 60-slide roadmap and 18 months of staff augmentation. Core banking vendors pitch a product roadmap where your specific use case ships in a release two years from now. We build and ship production agents inside your environment in weeks, own accuracy and eval results against your real data, and leave you with code and model artifacts you control. We're a smaller team, we get closer to the actual workflow, and we don't have a motivation to keep a bench of 40 consultants on-site for a year. We also integrate with, rather than replace, the core and origination platforms you already own.
What's the hand-off between the AI and our people, and how do we measure ROI?+
Every workflow has a defined confidence threshold. Above it, the agent acts or drafts. Below it, the case is routed to a human with the agent's analysis attached so the reviewer starts from a summary, not a blank file. ROI is measured against a baseline we capture in discovery: alerts cleared per analyst per day, cycle time per loan, hours per month-end pack, dispute handle time. We publish a dashboard at go-live that tracks those same metrics weekly. Most bank deployments pay back inside 9 months on loaded labor cost alone, and separately lift revenue where faster underwriting or retention shows up.

Let's build your AI system.

Production-grade AI for Enterprise AI for Financial Services. We deploy in weeks, not quarters.

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