Table of Contents
If your platform uses identity enrichment (phone / email / name / profiles) to power AI reports, risk routing, or automated decisions, the biggest question isn’t “which provider?” — it’s when to run enrichment.
Run it too early and you hurt conversion.
Run it too late and you eat fraud/risk.
Run it everywhere and you burn budget + create false positives.
This guide shows a simple, product-friendly way to place enrichment so you improve outcomes without adding unnecessary friction.
The 3 “moments of truth” in most products
Most platforms have three places where enrichment can be triggered:
Signup / onboarding
First payment / first transaction
Payout / withdrawal (or any money-out action)
Each stage has different goals, tolerance for friction, and downside risk.
Stage 1: Signup (highest volume, lowest tolerance for friction)
What is the reason that teams want enrichment here
stop obvious abuse early
reduce manual review noise
protect downstream workflows
Why it often backfires
signup is where your product is most sensitive to friction
enrichment signals can be ambiguous → false positives
blocking at signup can reject legitimate users and reduce growth
Best practice at signup
Use enrichment to route, not to block.
A safe signup routing model:
High confidence → auto-approve
Medium confidence → step-up (light verification)
Low confidence / conflicting → manual review only if needed
Avoid at signup
hard blocks based on a single weak signal
heavy lookups that cost a lot and don’t change routing
Stage 2: First payment (better signal, still conversion-sensitive)
Why this stage is powerful
a user who attempts a payment is “more real” than a signup
you can justify slightly more friction
it’s a natural place to protect merchants and limit abuse
Best practice at first payment
Enrichment + behavior signals → a decision about how much trust to grant.
Typical outcomes:
allow payment normally
allow but apply limits (rate/amount)
step-up verification
route to review
Avoid
treating enrichment as a verdict instead of context
expensive lookups for low-value transactions
Stage 3: Payout / withdrawal (highest leverage, easiest to justify)
If you can only do enrichment in one place, start here.
Why
the risk is highest when money leaves the system
users accept more checks at payout than at signup
ROI is easier to measure (loss prevention)
Best practice at payout
Make enrichment part of a “money-out checklist”:
enrichment signals (context)
account history (age, changes, velocity)
payment method behavior
destination behavior (new bank/wallet, changes, repetition)
Then route:
approve
step-up
review
delay/hold (if your product allows)
A simple rollout plan that protects conversion
If you’re building an AI-driven reporting platform (or enrichment layer for customers), this rollout order is the safest:
Start with payout/withdrawal
Expand to high-risk events (ATO, suspicious changes, escalations)
Add first payment checks
Add signup routing last (and keep it lightweight)
This order reduces false positives and keeps growth stable.
The “one rule” that prevents conversion damage
Don’t turn weak signals into hard decisions.
Instead, use a tiered outcome model:
High confidence
Signals consistent + match the user story
→ auto-approve / low friction
Medium confidence
Some signals, but uncertainty exists
→ step-up verification (best for conversion)
Low confidence
Conflicting/ambiguous/volatile identifiers
→ review or restrict depending on workflow
This keeps legitimate users moving while still catching the cases that matter.
What enrichment is best at (and what it’s not)
Enrichment is great for:
adding context to thin identifiers
improving routing (approve / step-up / review)
supporting investigation narratives in reports
Enrichment is not good at:
being the only reason to block someone
making identity “certain” from one weak identifier
A 7-day implementation checklist
If you want this live quickly:
Day 1–2
choose one workflow (payout or first payment)
define 3 outcomes (approve / step-up / review)
Day 3–4
implement async enrichment job pattern
store request id + normalized signals
Day 5
add caching (TTL) + budgets
Day 6
add a small “confidence tier” layer
Day 7
measure: conversion impact, review rate, prevented loss (or reduced noise)
Resources (for implementation)
Quickstart (15 min): https://espysys.com/irbis-api-quickstart-15-min/
Tutorial (async jobs + polling + caching + normalization): https://espysys.com/api-tutorial/