cost optimizationidentityprocurement

Modeling Cost Savings from Consolidating Identity Verification Vendors

ppayhub

2026-02-11

9 min read

Financial model and checklist to evaluate consolidating identity vendors—measure TCO, latency, coverage and fraud ROI.

Hook: Why payments teams must model vendor consolidation now

Payments teams face a familiar squeeze: rising fraud costs, fragmented identity providers, and engineering and vendor complexity that slows product velocity. Consolidating identity verification vendors can cut costs and simplify operations — but it also trades off coverage, latency, and sometimes fraud detection effectiveness. This article gives a practical financial model and a step-by-step checklist to evaluate consolidation decisions, quantify total cost of ownership (TCO) and calculate realistic ROI for 2026 and beyond.

Big picture in 2026: why consolidation is back on the table

Late 2025 and early 2026 saw three market forces that change the calculus for vendor consolidation:

Regulatory tightening and cross-border AML/KYC scrutiny increased compliance costs for fragmented stacks — regulators and competition authorities are also scrutinising large platform partnerships (see recent analysis on AI partnerships and antitrust risks).
AI-enabled orchestration matured: identity orchestration platforms and ML-based risk scoring and edge routing can combine multiple vendors with smarter routing, reducing the need to maintain many direct integrations.
Data management limits continue to cap the value of AI-driven fraud approaches — messy data and silos make multi-vendor signals harder to unify; see guidance on architecting data marketplaces and secure data flows to reduce this friction.

At the same time, independent research published in January 2026 found financial institutions routinely overestimate their identity protections. One study estimated legacy gaps cost the industry materially each year.

"When 'Good Enough' Isn’t Enough: digital identity gaps are costing firms — the industry is underestimating losses by billions in 2026." (PYMNTS/Trulioo research, Jan 2026)

Decision framework: Consolidate, orchestrate, or keep multi-vendor?

There are three practical approaches payments teams choose:

Full consolidation — single primary vendor for most checks, secondary backup for edge cases.
Orchestration platform — single control plane that routes checks to multiple best-of-breed vendors based on rules, but reduces direct vendor integrations.
Multi-vendor direct — maintain many direct integrations; maximum coverage but highest integration overhead.

Each option affects four core variables you must quantify: per-check cost, coverage, latency, and fraud reduction.

Financial model: step-by-step

This model helps you compare scenarios over a 3-year horizon. Capture your inputs, run sensitivity to latency and fraud assumptions, then produce TCO and ROI.

1) Define baseline volumes and flows

List verification flows (registration, high-risk transactions, payouts, KYC level 1/2/3). For each flow capture:

Monthly checks (volume)
Current vendor mix (vendor A 40%, B 30%, etc.)
Current per-check cost (USD)
Average observed latency (ms/sec) and % of users leaving due to latency (drop-off)
Manual review rate and cost per manual review
Fraud incidence rate, % prevented, and estimated loss per fraud

2) Fixed and variable cost buckets

Include all relevant costs:

Variable: per-check vendor fees (including document, biometric, watchlist checks)
Fixed/one-time: integration/onboarding fees, account setup, certification
Operational: manual review staffing, fraud investigations, chargebacks
Platform: orchestration platform fees (if used), monitoring/observability, SSO, audit logs
Compliance/legal: certifications, DSAR handling, data residency costs — see developer guidance on handling data for compliant training and residency scenarios (developer guide for compliant training data).
Transition: termination fees, dual-run costs during cutover

3) Model per-scenario costs

For each scenario (current multi-vendor, consolidated, orchestrated) compute annualized costs:

Total variable cost = sum(per-check price × checks).
Manual review cost = manual review rate × checks × cost per review.
Integration amortized cost = (integration cost + onboarding) / useful years.
Operational overhead = monitoring + vendor management (FTE cost × % time).
Compliance and data residency = annual fees + storage costs.

4) Quantify revenue and fraud impact

Changes in latency and friction influence conversion. Estimate:

Conversion delta per 100ms of latency (use internal A/B or industry benchmarks; e.g., 100ms = 0.5% drop in conversion for critical flows)
Fraud prevented delta — percentage change in fraud acceptance rate between scenarios
Average order or lifetime value (LTV) to convert conversion improvements into revenue

5) Compute TCO and ROI

Annual TCO = total variable + fixed amortized + operational + compliance. Compute 3-year NPV if you want discounting.

ROI = (Net benefits — TCO change) / Transition cost. Net benefits include reduced fraud losses + recovered revenue from improved conversion + lower manual review costs.

Plug-and-play numeric example (simplified)

Use this example to sanity-check assumptions. Adjust volumes and prices for your business.

Assumptions:

Monthly checks: 100,000
Per-check price (multi-vendor average): $0.60
Per-check price (consolidated): $0.45 (volume discount)
Manual review rate (multi-vendor): 1.5% at $12/review
Manual review rate (consolidated): 1.2% at $12/review (improved scoring)
Fraud losses (accepted fraud): $40,000/year (multi), $30,000/year (consolidated)
Transition one-time cost: $60,000 (integration, testing, dual-run)
Conversion uplift from latency reduction: 0.6% annual revenue gain; avg order $100; monthly conversion baseline 2%

Calculations (annual):

Variable cost (multi): 100k × 12 × $0.60 = $720,000
Variable cost (consol): 100k × 12 × $0.45 = $540,000 (savings $180,000)
Manual review cost (multi): 100k × 12 × 1.5% × $12 = $21,600
Manual review cost (consol): 100k × 12 × 1.2% × $12 = $17,280 (saving $4,320)
Fraud reduction benefit: $40,000 — $30,000 = $10,000/year
Conversion revenue uplift: baseline monthly conversions = 100k × 2% × $100 = $200,000/month revenue → $2.4M/yr. 0.6% uplift = $14,400/yr

Net first-year benefit (excluding transition cost): $180,000 + $4,320 + $10,000 + $14,400 = $208,720. Subtract transition $60,000 → Net Year-1 = $148,720. Payback under 1 year; 3-year cumulative savings > $500k.

This simplified model shows consolidation can pay off quickly. But the result is sensitive to assumptions about coverage loss, latency increase, and fraud detection quality. Run sensitivity analysis on ±20% for per-check price, ±0.5% on conversion effects, and ±$10k on fraud benefit. For modelling outage and incident impact on business metrics see our cost impact analysis guide.

Coverage, latency and fraud tradeoffs — how to quantify risks

Consolidation reduces vendor management but can reduce geographic coverage and alternate data sources. To quantify risks:

Map coverage by country/signal: compute percent of checks currently resolved successfully per region. Simulate the consolidated vendor coverage and compute delta in unresolved cases.
Estimate fallback cost: unresolved automated checks increase manual reviews or false declines. Model cost = unresolved × manual review rate × review cost + revenue lost from false declines.
Measure latency impact: request vendor SLA latency percentiles. Model conversion loss per latency delta using your flow sensitivity.
Model fraud delta: derive expected fraud acceptance rate difference from vendor benchmarks or PoC results. Multiply by expected loss per fraud to get monetary impact.

Checklist: Evaluate vendors for consolidation (practical, engineering-first)

Use this practical checklist when comparing vendors or an orchestration partner. Score each vendor and weigh the business impact.

Coverage and data sources: country list, document types, phone and identity graph coverage.
Proof quality: biometric liveness, document forgery detection, watchlist access.
Accuracy metrics: false positive/negative rates and how they were measured (benchmarks, datasets).
Latency and SLAs: p95/p99 latency, uptime SLA, backpressure behavior during spikes.
Pricing model: per-check tiers, bundling, volume discounts, hidden fees (data calls, storage, re-checks).
Integration developer experience: SDKs, API docs, sample code, test harnesses, sandbox quotas.
Orchestration & routing: native rules, dynamic routing, API to adjust routing in real-time.
Observability: websocket/webhook events, logs, metrics, dashboards, SLI/SLO support — include secure workflows and vaulting where appropriate (see secure workflow reviews).
Compliance & certifications: ISO27001, SOC2, PCI when relevant, local data residency options, contract clauses for breach notification. For platform security best practices see security guidance.
Vendor stability: customers in your industry, financial health, rate limits and support SLAs.
Exit & portability: data export, data retention policies, portability fees, support for migration pilots.
Legal & indemnity: liability caps, indemnification for breaches or mis-verifications, PII processing addenda.

Pilot plan & engineering playbook

Before consolidating, run a controlled pilot and validate your financial assumptions. Use this engineering-focused playbook:

Run a 60–90 day dual-run: route 10–30% of traffic to the consolidated vendor while keeping the incumbent to measure head-to-head in production.
Instrument everything: capture per-check latency, reason codes, payload size, and success/failure subcodes.
A/B test conversion impact: randomize users to measure real conversion delta, not just latency proxies.
Track manual review queue build-up and resolution times to estimate staffing changes.
Measure fraud outcomes: monitor chargebacks, fraud acceptance rate, and downstream fraud signals for 90–180 days.
Run resiliency tests: simulate vendor degraded states and confirm fallback flows and user messaging.

Advanced strategies to capture more savings

Beyond the basic model, use these strategies to maximize ROI while controlling risk:

Smart orchestration: route low-risk, high-volume checks to the lowest-cost vendor and route edge/complex flows to a specialist provider.
Adaptive pricing negotiations: use aggregated volumes across flows or multi-year commitments to negotiate deeper per-check tiers.
Shared risk contracts: some vendors accept revenue-share or SLA credits tied to false accept rates and uptime — similar commercial models appear in payments gateways (payments gateway reviews discuss shared-risk billing structures).
Data centralization: normalize identity signals into an internal identity graph so ML risk models improve regardless of vendor count. If you’re exploring local model deployments and signal consolidation, see notes on running local LLMs and labs (local LLM lab builds).
Periodic re-evaluation: revisit vendor performance quarterly and re-run TCO as volumes and fraud patterns evolve.

When consolidation is the wrong choice

Consolidation can backfire if:

You rely on unique local data sources or watchlists only available from specific vendors.
Latency increases materially and you cannot offset conversion loss.
Your fraud profile requires multiple orthogonal signals concurrently to detect attacks.
You lack the internal data plumbing to unify signals — consolidating to a single vendor could reduce ensemble detection that currently finds fraud.

Putting it together: a recommended project plan

Quarter 0: Audit current state, collect metrics, build baseline financial model.
Quarter 1: RFI/RFP and shortlist vendors; negotiate PoC terms with explicit metrics and data sharing for verification.
Quarter 2: Run dual-run pilot; instrument for latency, conversion, fraud, and manual reviews.
Quarter 3: Analyze pilot, run sensitivity, negotiate commercial terms and exit clauses.
Quarter 4: Go-live with staged cutover, monitor KPIs closely for 90 days and be ready to revert partial flows.

Key takeaways and practical next steps

Consolidating identity verification vendors can deliver substantial TCO improvements and operational simplicity — but only when you quantify the full economics and risks. Follow this short checklist to get started:

Inventory flows and capture accurate per-check metrics.
Model three scenarios (current, consolidated, orchestrated) with sensitivity analysis.
Run a production dual-run pilot to measure real-world latency, conversion, and fraud outcomes.
Negotiate commercial terms that align vendor incentives with fraud reduction and uptime.
Invest in data centralization to improve ML-based detection and make consolidation safer.

Why this matters in 2026

Industry dynamics in late 2025–early 2026 mean identity decisions now have amplified downstream effects. Regulatory scrutiny, faster AI-driven orchestration, and persistent data management gaps raise both the potential savings and the risks. A disciplined financial model and engineering-first checklist turn consolidation from a vague cost-savings idea into a measurable business decision.

Call to action

If you want a ready-to-use Excel model and a vendor scoring template tailored to payments teams, request our free Consolidation Toolkit. It includes a 3-year TCO workbook, sensitivity tabs for latency and fraud, and a downloadable vendor checklist to run PoCs faster. Click to get the toolkit and start a 30-day pilot blueprint for your next vendor consolidation evaluation.

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