Financial Services Cloud
AI pipelines for
Financial Services
Automate underwriting decisions, brief advisors before every client meeting, and triage insurance claims at intake — with full audit trail and governor compliance built in.
Loan Underwriting Automation
The Problem
Underwriters manually gather credit data, employment verification, and property appraisals from 5–8 separate systems. A single application takes 3–5 days to reach initial decision. High-volume periods create backlogs measured in weeks. Manual process produces inconsistent risk scoring across reviewers.
Why not DIY?
The circuit breaker pattern alone — gracefully handling credit bureau outages without failing the entire pipeline — takes 2–3 weeks of Apex to build correctly. FlowMason ships FMCircuitBreaker as a first-class stage.
Pipeline Stages
FlowMason Components
Realistic Outcomes
| Metric | Before | After |
|---|---|---|
| App-to-decision time | 3–5 days | 4–8 hours |
| Underwriter capacity | Baseline | +60% more apps |
| Scoring consistency | Variable by reviewer | Standardized criteria |
Illustrative based on observed patterns. Your results depend on your data and implementation.
Advisor Client Briefings
The Problem
Advisors spend 30–45 minutes per client meeting pulling account history, portfolio performance, open cases, and recent interactions from multiple systems. Prep is skipped on back-to-back days. Clients notice when advisors aren't prepared — it costs AUM.
Why not DIY?
Merging outputs from parallel async calls (portfolio API + CRM) into a single coherent briefing requires a CombinerNode pattern — a non-trivial orchestration primitive that FlowMason ships out of the box.
Pipeline Stages
FlowMason Components
Realistic Outcomes
| Metric | Before | After |
|---|---|---|
| Prep time per meeting | 35 min | 3 min |
| Data sources covered | 2–3 | 7 |
| Advisor satisfaction | Baseline | Qualitative improvement |
Illustrative based on observed patterns. Your results depend on your data and implementation.
Insurance Claims Triage
The Problem
Claims adjusters manually categorize and prioritize incoming claims. Simple auto claims sit in the same queue as complex liability disputes. Adjusters with specialty expertise are assigned generalist work. SLAs are missed because priority is never established at intake.
Why not DIY?
Extracting structured data from claim PDFs (DocumentProvider) and routing to the correct queue (ConditionalFlowControl) are each 2–3 week builds individually. FlowMason composes them with a JSON stage definition.
Pipeline Stages
FlowMason Components
Realistic Outcomes
| Metric | Before | After |
|---|---|---|
| Triage time | 2–4 hours | Under 10 min |
| Adjuster match accuracy | Baseline | +40% |
| SLA breach rate | Baseline | -55% |
Illustrative based on observed patterns. Your results depend on your data and implementation.
Chat with your Financial Services data.
Beyond the pipeline patterns above, FlowMason ships a chat surface admins drop on any Lightning page. Financial Services users type plain-English questions; FlowMason generates SOQL, validates through the 8-gate sanitiser, runs under FLS, returns rows. Read-only by default.
Try asking:
- › "list accounts with FATCA exposure above k"
- › "show me cases pending KYC review > 5 days"
- › "which advisors have unsigned suitability forms?"
FLS-aware. Allowlist-governed. Permset-gated. No data leaves the org.
Ready to build Financial Services pipelines?
Tell us your use case. We'll show you exactly which pipeline pattern fits.
Talk to us about Financial Services