How I Would Drive This as a Principal TPM

Author: Uday Tamma | Document Version: 1.0 | Date: January 06, 2026 at 11:33 AM CST

Program Scope & Ownership

The Principal TPM is directly accountable for:

• Real-time fraud decisioning platform for Telco/MSP payments and service transactions
• Evidence, disputes, and economic attribution loop to finance and risk teams
• ML-assisted risk scoring and policy experimentation, including safe rollout and governance
• Operational excellence: SLOs, incident management, and long-term reliability

Ownership spans problem definition, system design, execution orchestration, and post-launch optimization - not just project management.

Overview

This document outlines the cross-functional execution strategy for the Telco Payment Fraud Detection Platform from a Principal TPM perspective. It covers stakeholder management, decision frameworks, execution sequencing, and risk mitigation approaches.

Cross-Functional Partners and Engagements

Stakeholder Map

RACI Matrix (Key Decisions)

Decision Frameworks

Trade-off 1: Risk vs. Approval Rate

The Core Tension: Every percentage point of fraud blocked potentially blocks legitimate customers.

Framework: Expected Value Analysis

For each transaction:
  Expected_Loss = P(fraud) × (amount + chargeback_fee + penalty)
  Expected_Gain = P(legitimate) × (revenue + customer_LTV_fraction)

  If Expected_Loss > Expected_Gain × risk_tolerance:
    → Apply friction or block
  Else:
    → Allow

Operationalized as:

Governance:

• Finance owns the risk tolerance parameter
• Product owns the customer experience thresholds
• Fraud Ops can adjust within guard rails without engineering
• Changes require replay testing before production

Trade-off 2: Detection Speed vs. Accuracy

The Core Tension: More sophisticated detection takes more time, but payments cannot wait.

Framework: Latency Budget Allocation

Decision Rule:

• Any component change must model latency impact
• New features require latency benchmarking before merge
• P99 > 150ms triggers architecture review

Trade-off 3: Manual Review vs. Automation

The Core Tension: Manual review is more accurate but does not scale and adds friction.

Framework: Confidence-Based Routing

High Confidence (>90%):
  → Automate decision (ALLOW or BLOCK)
  → No manual review
  → Post-hoc sampling for quality

Medium Confidence (60-90%):
  → Automate with audit trail
  → Sample 5% for manual review
  → Feedback loop to improve model

Low Confidence (<60%):
  → Queue for manual review
  → SLA: 4 hours for >$500, 24 hours for <$500
  → Capture analyst decision as training data

Target Distribution:

Execution Sequencing and De-risking

Rollout Strategy

Week 1-2: Shadow Mode
├── Deploy to production infrastructure
├── Process 100% of traffic in parallel
├── Log decisions but do not act on them
├── Compare to existing system decisions
└── Validate: Latency, accuracy, stability

Week 3: Limited Production (5%)
├── Route 5% of traffic to new system
├── Remainder continues to legacy
├── Monitor: Approval rate, fraud rate, complaints
├── Kill switch: Route back to legacy if issues
└── Validate: No regression on key metrics

Week 4-5: Gradual Ramp (25% → 50% → 100%)
├── Increase traffic weekly
├── Hold each level for 48+ hours
├── Document any anomalies
├── Business sign-off at each gate
└── Full cutover only after 50% stable for 1 week

Safety Rails

Safe Mode Behavior

When safe mode activates:

1. ML scoring disabled (if enabled)
2. Rule-based scoring only
3. Default decision: FRICTION (not ALLOW)
4. Blocklist checks still active
5. Alert on-call immediately
6. Automatic recovery when component healthy for 5 minutes

Stakeholder Communication Plan

Regular Cadence

Escalation Path

Severity 1 (Revenue Impact):
  → Immediate: On-call Eng + SRE
  → 15 min: Eng Lead + Product
  → 30 min: VP Eng + VP Product
  → 1 hour: C-level if unresolved

Severity 2 (Metric Degradation):
  → Immediate: On-call Eng
  → 1 hour: Eng Lead + Fraud Ops
  → 4 hours: Product Lead
  → 24 hours: VP if unresolved

Severity 3 (Non-urgent):
  → Next business day review
  → Track in sprint backlog

Risk Mitigation Matrix

Technical Risks

Operational Risks

Business Risks

Success Metrics and Governance

Phase 1 Success Criteria (Go/No-Go)

Ongoing Governance

Incident Readiness & Runbooks

The platform is treated as a Tier-1 service with clear incident protocols.

Incident Playbooks

Post-Incident Review

• Quantify impact (loss, approvals, CSAT tangents)
• Feed learnings back into policy, detectors, and tooling
• Update runbooks with new scenarios

Key TPM Artifacts

Documents I Would Produce

1. Technical Requirements Document (TRD) - Detailed specifications for each component
2. Integration Runbook - Step-by-step PSP integration guide
3. Rollout Plan - Week-by-week execution schedule with gates
4. Risk Register - Living document of risks and mitigations
5. Metrics Dashboard Spec - KPI definitions and visualization requirements
6. Incident Response Playbook - Severity definitions and response procedures
7. Post-Launch Review Template - Structured retrospective format

Meetings I Would Run

1. Architecture Review - Cross-functional technical decision forum
2. Rollout Readiness Review - Go/no-go checklist walkthrough
3. Weekly Metrics Review - KPI trends and action items
4. Incident Post-Mortem - Structured learning from failures
5. Quarterly Business Review - Executive summary with ROI analysis
6. Fraud Governance Council - Monthly cross-functional policy approval forum

This document demonstrates Principal TPM execution thinking: stakeholder management, decision frameworks, risk-aware sequencing, and structured governance.