Case Study Template: Reducing Fraud Losses by Modernizing Identity Verification

Hook: Why your identity stack is silently leaking revenue (and how to prove it)

Security teams and engineers know the pain: complex identity checks, noisy fraud signals, and a constellation of vendors stitched together over years. Those decisions add latency, inflate costs, and—most critically—let skilled attackers exploit gaps. In 2026, firms that treat identity as a checklist lose ground: a PYMNTS/Trulioo analysis estimates banks overestimate defenses and face multibillion-dollar gaps in loss prevention. This case study template and metrics framework helps engineering, product, and risk teams document identity modernization projects so you can show concrete ROI, defend vendor consolidation, and track lasting fraud reduction.

Executive summary (inverted pyramid: what to put up front)

Start your case study with a crisp, quantitative summary answering: what changed, why it mattered, and what you achieved. Use one short paragraph and three bullet facts for fast executive review.

• What: Replaced four legacy identity vendors with a consolidated, AI-enabled identity platform.
• Why: Reduce false positives, lower integration overhead, and block automated account takeover (ATO) attacks.
• Outcome (90 days): 48% reduction in fraud losses, 22% lift in conversion, and payback in 6 months.

Why this matters in 2026 — trends that change the stakes

Late 2025 and early 2026 accelerated three forces that make identity modernization urgent for payments teams:

• AI-driven attacks: The World Economic Forum’s Cyber Risk in 2026 flagged generative and predictive AI as forces that increase attack scale, meaning legacy heuristics fail faster.
• Data expectations and privacy: New regional regulations and enterprise data governance standards require clear traceability of identity signals—Salesforce and others documented how weak data management limits AI and security ROI.
• Consolidation and interoperability: Vendors now offer multi-modal identity signals (biometrics, device signals, cross-border KYC) via unified APIs — consolidating reduces latency and operational debt.

"Predictive AI is a force multiplier for both offense and defense in 2026 — teams must modernize identity to stay ahead." — World Economic Forum (2026)

How to use this template

This is a reusable deliverable for internal stakeholders and partners. Populate each section with your project-specific data. Use the metrics framework to compute baseline vs. post-modernization KPIs and include a one-page snapshot for executives.

Document metadata

• Project owner: Team/Lead
• Date range: Baseline period and Measurement period
• Systems in scope: e.g., onboarding flow, login flow, payments flow
• Confidentiality/classification

Template: Case study sections (with prompts)

1) Executive summary

One paragraph plus 3–5 metrics (percentages and dollar amounts). Answer: What, Why, Impact, Payback.

2) Problem statement

• Symptoms: rising chargebacks, increased manual reviews, conversion drop.
• Impact: customer complaints, ops cost, regulatory exposure.
• Business constraints: budget, timelines, integrations.

3) Objectives & success criteria

• Primary objective: reduce fraud losses by X% within Y months.
• Secondary objectives: decrease manual reviews, improve conversion, consolidate vendors.
• Acceptance criteria: defined KPIs, SLOs, and data governance tasks.

4) Baseline data

Provide 90–180 days of pre-change data. Capture both business and technical signals.

• Fraud losses ($) — total, and broken down by channel (card-not-present, ATO, KYC failure)
• Chargeback rate (%) and SARs
• Conversion rate (%) for flows with identity checks
• False positive rate (%) — legitimate users blocked or challenged
• Manual review volume and average handle time (AHT)
• Vendor list and costs (monthly/annual)
• Data completeness: percent of transactions with key signals (email, phone, deviceID, IP, identity score)

5) Solution overview

Summarize the technical changes and vendor decisions. Include architecture diagrams in the appendix.

• Vendor consolidation: who was removed/added
• New signals: biometrics, device telemetry, shared fraud graph
• AI components: models used for scoring and ensemble strategies
• Operational changes: new review queues, automated plays, SLA/TAT changes

6) Implementation details

List integration points, data flows, rollback steps, and test plans.

• API endpoints updated and versions
• Data contracts and fields added (schema diffs)
• Latency impact and mitigation (edge caching, sampling)
• Canary strategy and rollout cadence

7) Results — quantitative and qualitative

Present before-and-after numbers and attribution logic. Use confidence intervals if sampling.

• Fraud loss reduction (%) and $ saved
• Conversion lift (%) and incremental revenue
• Operational cost change (manual review FTEs, vendor spend)
• False positive and false negative changes
• Time-to-decision and latency improvements

8) ROI and payback calculation

Show the math. Below we include formulas and an example.

9) Lessons learned and next steps

• What surprised the team (data quality, user behavior)
• Where to iterate next (model retraining cadence, telemetry gaps)
• Governance: who owns the identity score and SLOs

10) Appendix

• Data schema diffs and sample payloads
• Vendor contract summary and termination clauses
• Dashboards and queries used for reporting

Metrics framework: what to measure and how

Below is a compact metrics list with definitions, formulas, and recommended reporting cadence. Treat these as the minimal contract for any identity modernization case study.

Core KPIs (business-facing)

• Fraud Losses ($): Total dollars lost from fraudulent transactions in scope per 30/90 days.
• Chargeback Rate (%) = (Number of chargebacks / Number of transactions) * 100
• Conversion Rate (%) = (Successful transactions / Transaction attempts) * 100
• Average Order Value (AOV) and Revenue Lift ($) from changes in conversion

Operational KPIs (ops and engineering)

• False Positive Rate (FPR): Legitimate user rejections / total legitimate attempts
• False Negative Rate (FNR): Fraudulent transactions missed / total fraud attempts
• Manual Review Volume and AHT
• Vendor Cost: total paid for identity vendors (monthly/annual)
• Latency (ms): time added to transaction flow by identity checks

Model & data health metrics (technical)

• Identity Match Rate: percent of transactions with full identity match across signals
• Score Distribution: percent of traffic in low/medium/high risk buckets
• Drift metrics: population shift for key features month-over-month
• Ensemble AUC/Precision-Recall and threshold sensitivity

Reporting cadence

• Daily: score distribution and obvious spikes
• Weekly: operational metrics and manual review volumes
• Monthly/quarterly: business KPIs, ROI, and vendor spend

ROI calculation: formulas and worked example

Use this section to make the financial case. Capture all cost and benefit lines.

Basic formulas

• Fraud savings = Baseline fraud losses - Post-change fraud losses
• Revenue uplift = (Conversion_post - Conversion_baseline) * Transaction volume * AOV
• Operational savings = (Manual review FTE_baseline - FTE_post) * FTE_cost_annual
• Total benefits = Fraud savings + Revenue uplift + Operational savings
• Total costs = Implementation cost + New vendor fees + Ongoing maintenance
• Payback period (months) = Total costs / (Total benefits per month)
• ROI (%) = ((Total benefits - Total costs) / Total costs) * 100

Worked example (90-day post period)

Hypothetical mid-market payments company baseline:

• Transactions/month: 200,000
• AOV: $60
• Baseline fraud losses/month: $150,000
• Baseline conversion: 3.5%
• Post-change conversion: 4.3%
• Manual review FTEs reduced from 10 to 6, FTE cost $8,000/mo
• Implementation cost: $120,000 (one-time). New vendor fee: $10,000/mo. Old vendor spend: $18,000/mo (savings $8,000/mo)

Compute benefits per month:

• Revenue uplift = (4.3% - 3.5%) * 200,000 * $60 = 0.8% * 200,000 * $60 = 0.008 * 200,000 * $60 = $96,000/mo
• Operational savings = (10 - 6) * $8,000 = $32,000/mo
• Vendor savings = $8,000/mo
• Fraud savings (observed) = $72,000/mo (48% reduction from baseline $150k)
• Total benefits = $96k + $32k + $8k + $72k = $208,000/mo

Costs:

• Monthly net vendor cost = $10k (new) - $18k (old) = -$8k (i.e., saving) — we counted vendor savings above
• Net monthly costs = $10k (new vendor) but old vendor terminated — handled via vendor savings calculation
• One-time implementation cost amortized: $120k / 6 months = $20k/mo (if you want 6-month payback)
• Total net monthly costs (including amortized implementation) = $20k
• Payback = ($120k) / ($208k - $20k) ≈ 0.625 months (note: immediate payback due to heavy monthly benefits)
• ROI (annualized) = ((($208k * 12) - $120k) / $120k) * 100 ≈ extremely high — demonstrates compelling economics

Reality note: use conservative assumptions and include sensitivity analysis for seasonality and attack waves.

Vendor consolidation: what to document and checklist

Consolidation is often the highest-yield lever — fewer integrations, centralized scoring, and lower costs. But the transition has risk. Document these items for any vendor moved off-platform.

• Contract termination windows and penalties
• Feature parity matrix: which signals each vendor provides
• Data retention and portability clauses
• Mapping of API fields between old and new vendors
• Dependency graph: services that rely on vendor X
• Backup/rollover plan: how to revert in case of regression

Attribution and confidence: how to prove the change caused the improvement

Attribution is the hardest part. Use A/B tests, canaries, and regression-adjusted time-series analysis. Key tactics:

• Run parallel scoring for a sample of live traffic and compare decisions
• A/B test conversion impact with traffic splits and identical UX
• Use interrupted time series with external controls (seasonality, marketing spend)
• Log decisions with feature snapshots so you can retroactively analyze false positives/negatives

Data governance: the backbone of sustainable identity

Most modernization projects fail to lock long-term value because the data contract is weak. In 2026, Gartner and Salesforce emphasize data trust and lineage as prerequisites for AI-driven defense. Make these governance items mandatory:

• Field-level lineage: source, transformation, retention — map this to your edge auditability and decision planes.
• Model governance: owner, training cadence, evaluation metrics — treat model control like product governance (don’t let AI own your strategy).
• Access controls and auditable decision logs (for compliance and disputes)
• Pseudonymization strategies to enable analytics without exposing PII; consider lightweight, privacy-first edge hosts for analytics like pocket edge hosts when appropriate.

Case study snapshot (example you can paste)

Copy-paste this one-page snapshot into your internal comms or partner decks.

One-line summary

Consolidated identity vendors and deployed AI-driven ensemble scoring — achieved 48% reduction in fraud losses and 22% lift in conversion within 90 days.

Key metrics (90 days)

• Fraud losses: $450k → $234k (48% reduction)
• Conversion: 3.5% → 4.27% (+0.77pp, +22% relative)
• Manual reviews: 1,200 → 480 per month (-60%)
• Vendor spend: $18k/mo → $10k/mo (net saving $8k/mo)
• Payback: 6 months (including one-time migration costs)

Common pitfalls and mitigation

• Underestimate data cleanup: schedule at least 30% of project time for ETL and mapping
• Overtrust a single vendor: retain a lightweight fallback scoring during rollouts
• Neglect UX: avoid increasing challenge friction—test with real users
• Ignore regulatory edge cases: validate PII flows for every jurisdiction

Actionable next steps checklist (30/60/90-day plan)

Days 0–30

• Assemble cross-functional owners (security, payments, product, legal)
• Extract baseline metrics and export a 90-day dataset
• Map vendor capabilities and create a parity matrix

Days 31–60

• Integrate new vendor in parallel for a 10–20% traffic sample
• Run A/B tests for conversion and false positive impact
• Iterate thresholds and ensemble logic

Days 61–90

• Roll out fully with canaries and monitoring alerts
• Begin monthly ROI reporting and adjust remediation plays
• Start vendor offboarding and knowledge transfer

Final thoughts: measuring impact in 2026

Identity modernization is no longer a siloed risk project—it's a cross-functional revenue and trust play. In 2026, attackers scale with AI; your defenses must scale with data quality, model governance, and vendor consolidation. Use this template to build a repeatable, auditable story that convinces product, finance, and executives. Track the right KPIs, show the math, and automate attribution where you can.

Call to action

Ready to convert your identity modernization into measurable business impact? Use this template for your next project, or contact our team for a tailored workshop: we’ll help map vendor parity, define KPIs, and build the dashboards you need to prove ROI. If you need runbooks for incident handling during migration, see our Incident Response Template for Document Compromise and Cloud Outages.

Related Reading

• The Evolution of Site Reliability in 2026: SRE Beyond Uptime
• Serverless Data Mesh for Edge Microhubs: A 2026 Roadmap
• Edge Auditability & Decision Planes: An Operational Playbook for Cloud Teams in 2026
• Password Hygiene at Scale: Automated Rotation, Detection, and MFA
• Newsletters for Niche Medical Audiences: Building Trust and Monetizing Carefully
• Place the Robot: How to Arrange Your Kitchen So Your Vacuum Actually Cleans
• CES 2026 Garden Tech Roundup: 7 Gadgets That Could Transform Your Yard
• Scent and the Placebo Effect: Why 'Custom' Fragrances Sometimes Feel Better
• Commuter Backpacks for Dog Owners: Smooth Transitions from Park to Office