Same fraud data. Four different workflows.
Tuesday you saw the code. Today you see how real team members actually use it. Each role has different needs, different views, different wins.
Team Workflows
See how different roles use the same system to transform their daily work.Click each role below
Before Automation
With Automation
Dashboard Metrics
Impact By The Numbers
"I finally manage the team, not the spreadsheets."
— Fraud Manager, 11 years banking
How Roles Work Together on One Case
Watch how the system routes a real fraud case through all four roles in 6 minutes.
High-Value Wire Transfer ($127K) - Suspicious Pattern
✨ Scroll here to watch the workflow
Team-Wide Impact
| Metric | Before | After | Improvement |
|---|---|---|---|
| Average Case Resolution Time | 25 minutes | 4 minutes | 84% faster |
| Daily Cases Processed | 120 cases/day | 380 cases/day | 3.2x volume |
| Fraud Detection Rate | 76% | 94% | +18 points |
| False Positive Rate | 28% | 8% | 71% reduction |
Getting Your Team On Board
Analysts worry AI will replace their judgment
Show that AI handles data gathering (boring), analysts make final calls (interesting). Frame as 'AI is your research assistant, you're still the detective.'
Analysts love having more time for complex cases. Retention improves because work is more engaging.
Managers concerned about losing control
Run parallel for 30 days: manual + automated. Show managers they have MORE visibility (real-time dashboard vs end-of-day reports).
Managers see they can actually manage proactively instead of reactively. Trust builds through transparency.
Operations worried about compliance gaps
Show audit trail: every AI decision is logged, every human review is timestamped, full chain of custody maintained.
Compliance actually improves. Regulators love the documentation (better than manual notes).
Team thinks setup will disrupt operations
Pilot with 5 analysts on 20% of cases. Prove value before full rollout. Keep manual process running in parallel.
Zero disruption. Team sees results in week 1, asks to expand pilot by week 2.
Executives concerned about ROI timeline
Calculate current cost: 10 analysts × $60K salary × 40% time on data gathering = $240K/year wasted. Show 30-day payback.
CFO approves immediately when seeing hard numbers. ROI is obvious.
Investment & ROI
Typical payback in 28-35 days through analyst time savings
Pricing
ROI Calculator
Proven Results
From Demo to Live in 4 Weeks
From demo to production in just 3 weeks
- Connect to transaction monitoring system
- Import historical fraud cases (baseline ML training)
- Configure role-specific dashboards
- Set up case management integration
- Train each role on their workflow (4hr sessions)
- Configure fraud patterns specific to your bank
- Set up escalation rules and SLA alerts
- Test integrations with parallel manual process
- Run pilot on 20% of daily cases
- Compare AI vs manual detection rates
- Gather feedback, adjust configurations
- Refine fraud patterns based on results
- Roll out to all analysts
- Decommission manual processes
- Daily check-ins for first week
- Measure baseline metrics vs new performance
Enterprise deployments with custom ML models may take 6-8 weeks
2026 Randeep Bhatia. All Rights Reserved.
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