fraudbankingrisk

Quantifying the $34B Gap: How Banks Should Recalculate Identity Risk

vverifies

2026-01-21

10 min read

Turn the $34B industry estimate into bank-level ELPA: map verification gaps to dollars with metrics, models, and actionable ROI.

Start with the problem: your verification math is hiding real losses

Banks and financial platforms are drowning in digital traffic while trying to keep fraud, compliance, and customer experience in balance. The result: teams default to “good enough” identity checks that reduce friction but leak fraud, costing the industry at scale. In early 2026 the PYMNTS–Trulioo collaboration estimated a $34 billion annual shortfall in how banks value their identity defenses. That number is a wake-up call, not an endpoint: the right response is to stop treating identity as a binary pass/fail and instead quantify the economic exposure caused by verification gaps.

Why recalculating identity risk matters in 2026

Three context points for 2026 that change the calculus:

AI-driven attacks: as the World Economic Forum’s Cyber Risk 2026 outlook and early 2026 industry reports highlight, generative and predictive AI is both accelerating automated attacks and enabling more convincing synthetic identities.
Regulatory pressure and fines continue to rise across jurisdictions—so the cost of KYC failures isn’t just fraud losses but regulatory remediation and reputational capital.
Data signal variety has exploded—device telemetry, behavioral biometrics, consortiumed fraud feeds—making it possible to create richer, monetizable risk scores if you instrument and model correctly.

Principle: map verification gaps to dollars with a repeatable framework

We propose a repeatable five-step framework that operationalizes the $34B estimate into bank-level exposure and prioritized remediation. Each step produces concrete metrics and data queries you can implement in weeks.

Step 1 — Measure baseline coverage and leakage

Start by measuring how much of your user flow is covered by trustworthy identity signals and how much leaks through. Two core metrics:

Verification Coverage Rate (VCR) = verified accounts / total new accounts. Use product, channel, and geography slices.
Fraud Leakage Rate (FLR) = fraud incidents attributable to unverifed or weakly verified accounts / total fraud incidents.

Practical SQL to compute monthly VCR and FLR (example):

-- Monthly Verification Coverage Rate
SELECT
  DATE_TRUNC('month', created_at) AS month,
  COUNT(*) FILTER (WHERE verification_status = 'verified')::float / COUNT(*) AS vcr
FROM accounts
GROUP BY month;

-- Monthly Fraud Leakage Rate
SELECT
  DATE_TRUNC('month', fraud_detected_at) AS month,
  SUM(CASE WHEN account_verification_score <= 0.5 THEN 1 ELSE 0 END)::float / COUNT(*) AS flr
FROM fraud_events
GROUP BY month;

Step 2 — Convert incidents to cost

Not all fraud events cost the same. Build a Cost Per Fraud Event (CPFE) taxonomy by fraud type:

Chargeback (card): average chargeback value + processing fees + interchange + recovery cost
Account takeover (ATO): average stolen funds + remediation + customer churn
KYC failure (regulatory fine): historical fines, legal, and remediation overhead

Simple CPFE calculation for a product line:

CPFE_product = (sum(loss_amount) + sum(manual_remediation_costs) + sum(chargeback_fees) + sum(legal_remediation)) / count(fraud_events)

Step 3 — Attribute fraud to verification gaps

Define Exposure-attributable-to-Verification (EV) — the portion of fraud loss you can reasonably attribute to inadequate verification. Use propensity models, A/B test lift, and post-incident forensics. A conservative model uses historical fractions:

EV = Total_Fraud_Loss * FLR

For advanced teams, run a causal model (difference-in-differences or uplift modeling) comparing cohorts with different verification sophistication to estimate marginal leakage.

Step 4 — Compute Expected Loss per Account (ELPA)

Aggregate the above into a per-account expected loss — a key unit for decision-making.

ELPA = V_unverified * FLR * CPFE
where V_unverified = proportion of accounts without high-confidence verification

Alternative decomposition for mix of verification tiers:

ELPA = Sum over tiers (share_tier * leakage_rate_tier * cpfe_tier)

Step 5 — Prioritize remediation with ROI and risk budgets

Estimate remediation ROI by comparing the cost of improving verification (API/SDK fees, latency, manual review headcount, false positive impact) to the reduction in ELPA. Build a Loss Allocation Matrix that maps product lines and channels to expected loss and cost-to-remediate.

Making the $34B local: a worked example

Use this simplified example to illustrate how an individual mid-sized retail bank could translate an industry figure like $34B into its own P&L exposure.

Bank processes 10M new account events per year.
Current VCR = 70% (i.e., 30% low/weak verification).
Observed FLR = 65% (65% of fraud events trace to weakly verified accounts).
Average CPFE across products = $900.

Compute ELPA:

Unverified share = 0.30
ELPA = 0.30 * 0.65 * $900 = $175.50 per new account
Total expected loss = 10,000,000 * $175.50 = $1.755 billion per year

That single bank would be responsible for ~5% of a $34B industry gap. Scale across peers and product variations, and the PYMNTS–Trulioo number is feasible. The value in this exercise is not the exact match: it’s the discipline of mapping behavioral and verification gaps to dollars so leadership can fund fixes.

Data sources and signals: what to instrument now

A practical identity cost model requires linking signals across sources. Prioritize:

Internal systems: account events, KYC pass/fail, manual review logs, fraud event tables, chargebacks, SARs, customer support tickets.
Telemetry: device fingerprinting, browser headers, IP reputation, TLS fingerprints, time-of-day, velocity features. Make sure you capture on-device signals where possible to improve real-time decisions.
Behavioral signals: mouse/typing patterns, session duration, multi-step journey anomalies.
Third-party ID and risk feeds: Trulioo, LexisNexis, Experian, identity consortiums, phone/email reputation, dark-web signals.
Consortium and shared fraud networks: interchange data, industry watchlists, shared device hashes—instrument these via real-time APIs and feeds; consult integrator playbooks for reliable pipelines (real-time API integration).
Adversarial indicators: fraud bot signatures (captcha bypass), automation patterns, synthetic voice signals.

Instrument these signals into a single identity event (UID) and maintain a unified identity profile for each customer to enable downstream scoring and causal attribution. Consider the operational lessons from resilient transaction flow design when you build tolerant, auditable pipelines.

Designing the predictive model that powers ELPA

Move from descriptive reporting to predictive analytics using an ensemble approach:

Labeling: positive = confirmed fraudulent events; negatives = validated legitimate accounts. Use a lookback window (90–180 days) to capture ATO and delayed chargebacks.
Feature engineering: combine static identity attributes, device and network telemetry, behavioral time-series aggregates, and third-party risk scores. Create drift detection for features impacted by adversarial AI.
Modeling: blend gradient-boosted decision trees for tabular signals with sequence models for behavioral signals. Use Bayesian calibration for probability outputs so ELPA = predicted_prob * CPFE is reliable.
Explainability: add SHAP values or feature attribution to produce audit trails for compliance and manual review augmentation. Tie model outputs into your monitoring stack for drift and performance alerts—see guidance on modern monitoring platforms.

Example Python pseudocode: compute predicted ELPA for a cohort

# pseudocode
pred_prob = model.predict_proba(X)[:,1]
cpfe = estimate_cpfe_by_product(X.product_id)
elpa = pred_prob * cpfe
cohort_elpa = elpa.mean() * cohort_size

Operationalizing the numbers: dashboards and thresholds

To make ELPA actionable, build a small set of dashboards and KPIs:

ELPA by product, channel, and geo
VCR and FLR trendlines with anomaly detection
Cost-to-remediate estimates per remediation tactic (enhanced ID verification, device challenge, manual review)
Lift experiments (A/B) showing delta in fraud incidence and conversion

Define risk budgets for each product: e.g., maximum allowable ELPA per 1,000 accounts. If ELPA exceeds the budget, trigger staged mitigations—tighten onboarding checks, raise challenge rates, or route to higher-trust channels. Use hybrid hosting and edge strategies to keep telemetry low-latency and reliable (hybrid edge hosting), and bake the operational playbook into your creator/ops runbooks for reproducibility.

Quantifying remediation ROI: an example decision rule

Suppose a vendor offers a biometric identity verification service that reduces FLR by 40% for accounts it covers. The vendor charges $2.50 per verification call. For a product with 200K annual new accounts, 30% unverified, CPFE $900:

Current expected loss (unverified cohort) = 200,000 * 0.30 * 0.65 * $900 = $35,100,000
Coverage by vendor = 70% of unverified cohort = 42,000 calls * $2.50 = $105,000
Loss reduction = 40% * previous cohort loss = $14,040,000 saved
Net benefit = $14,040,000 - $105,000 = $13,935,000
ROI ≈ 132x

That kind of math makes procurement decisions straightforward.

Handling the operational tradeoffs: false positives and onboarding conversion

No verification is free. Higher verification strictness creates friction and false positives. Include conversion impact into the model by estimating lost lifetime value (LTV) for customers blocked or dropped during onboarding.

Adjusted ROI = (FraudSavings - VerificationCost - LostLTV) / VerificationCost

Run micro-experiments with progressive KYC: allow lower initial verification to shorten conversion, then progressively increase verification for risky behaviors or higher balances. Use risk-based thresholds and step-up authentication to minimize false positive costs.

Addressing bot attacks and adversarial AI (practical controls in 2026)

Bot and synthetic identity attacks continue to escalate in 2026. Mitigations that materially reduce ELPA include:

Real-time device telemetry correlated with behavioral models to detect automation.
Adaptive challenge flows driven by model uncertainty (entropy-based triggers).
Consortium signal sharing to detect reuse of identity fragments across institutions.
Predictive AI defenses that prioritize high-likelihood threats for fast, automated blocking—referenced in recent industry guidance and PYMNTS coverage early 2026. Incorporate attack simulations and adversarial testing with modern edge AI workflows to validate detection rates.

Build attack-simulations into your risk testing: generate synthetic identities with generative AI and measure your pipeline’s detection rate.

Governance, compliance, and auditability

For KYC/AML teams, ELPA is not a replacement for regulatory validation but a complement. Ensure models and cost allocations are auditable:

Document data lineage for each risk feature.
Version-control models and store rationale for thresholds.
Log decision metadata to support SARs and remediation post-incident. For platform and regulatory alignment see our guidance on regulation & compliance for specialty platforms and apply privacy-by-design patterns in your APIs and data pipelines.

Advanced strategy: loss allocation and cross-subsidy

Large institutions can use ELPA to create internal chargebacks and cross-subsidies. For example, allocate expected fraud loss to product P&Ls monthly so high-risk business units pay for incremental verification or headcount in central teams. This converts identity risk from an abstract security problem into a measurable cost center.

Quick checklist to implement this framework in 90 days

Collect the data: centralize account, fraud, chargeback, SAR, and verification logs.
Compute baseline VCR and FLR by product/channel.
Estimate CPFE by fraud type from historical P&L and vendor invoices.
Implement a calibrated predictive model to output per-account fraud probability.
Calculate ELPA and dashboard by product; run remediation ROI scenarios.

Actionable takeaways

Stop treating identity as binary. Use tiered verification coverage and map each tier to leakage and cost.
Measure ELPA. Expected loss per account is the single metric that aligns security, product, and finance.
Prioritize by ROI. Fund high-impact verification where the ratio of prevented loss to cost is highest.
Instrument for adversarial AI. Simulate synthetic attacks and add drift detection to features and models.
Govern for auditability. Maintain data lineage, model explainability, and remediation logs for compliance.

The PYMNTS–Trulioo $34B estimate is a diagnostic: the true value is in turning that macro-number into bank-specific ELPA figures you can act on.

Final recommendation: turn discovery into decisive investment

If your leadership hears a $34B industry figure and treats it as someone else’s problem, you will keep losing both dollars and customers. Use the framework above to quantify your local share of that gap, run a few quick experiments (vendor A/B tests, progressive KYC, behavioral telemetry), and create an ELPA-backed business case. In 2026, identity is a product lever and a financial line item—treat it like one.

Next steps and call to action

Ready to operationalize ELPA at your organization? Start with a two-week diagnostic: we’ll help you extract VCR, FLR, and CPFE from your data, run a baseline ELPA calculation, and produce prioritized remediation options with projected ROI. Contact our identity analytics team for a structured starter kit and a workshop that maps the PYMNTS–Trulioo findings directly to your P&L.

verifies

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.