Fraud Detection AI · Business Case

Your Numbers.
Your Real ROI.

Model fraud loss avoided, chargeback savings, false positive recovery, labor savings, and regulatory exposure. Solution cost shown in full — no hidden denominator.

Start Here — Institution Profile

Pre-filled with industry benchmarks. Change any number and results update instantly above.

Institution Asset Size Drives platform tier

Determines base platform fee

Institution Type Auto-fills case volumes

Sets scenario volume defaults

Fraud Analyst Hourly Rate ($) Benchmark: $85

Fully-loaded including benefits & overhead

Senior Analyst / BSA Officer ($) Benchmark: $110

HITL review, SAR sign-off, complex cases

Fraud Analyst FTE Count Your team size

Caps labor savings at team capacity

Annual Transaction Volume (M) For FP recovery

Total monthly transactions × 12 ÷ 1M

Monthly Case Volumes — 10 Fraud Types

Pre-filled from institution type · change any volume · leave unused types at 0

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Fraud type	Type	Vol/mo ✦	Manual hrs	AI hrs	AI cost/case	Avg loss/case ($)	Detection lift (%)
ATO Wire Fraud	SAR	Benchmark: 12	1.5 hrs	9 min HITL	$0.03	$47,500	30%
BEC Wire Fraud	SAR	Benchmark: 4	2.5 hrs	12 min HITL	$0.04	$185,000	35%
Synthetic Identity	HITL	Benchmark: 8	1.5 hrs	11 min HITL	$0.03	$18,200	28%
Mule Network	SAR	Benchmark: 5	4.0 hrs	21 min HITL	$0.05	$47,200	32%
Pig Butchering NEW	SAR	Benchmark: 2	3.0 hrs	18 min HITL	$0.05	$340,000	40% (speed)
CNP / Card Fraud NEW	CB	Benchmark: 180	0.75 hrs	5 min HITL	$0.02	$280	35%
ACH Origination NEW	SAR	Benchmark: 6	2.0 hrs	12 min HITL	$0.04	$62,000	40%
Elder Exploitation NEW	SAR	Benchmark: 3	3.0 hrs	17 min HITL	$0.04	$92,000	30%
New Account Fraud NEW	HITL	Benchmark: 10	1.5 hrs	11 min HITL	$0.03	$14,500	28%
Check Fraud NEW	HITL	Benchmark: 8	2.0 hrs	13 min HITL	$0.03	$6,800	32%

✦ Detection lift = % improvement in cases caught. For pig butchering this is an intervention speed factor (% of victim-initiated wires intercepted before clearing), not a standard detection rate — default 40% is conservative.

Fraud Loss Avoided

The largest ROI lever for most fraud buyers — previously absent from this calculator

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Loss avoided is computed per scenario from the case mix table above (vol × avg_loss × detection_lift). The inputs below let you tune the pig butchering intervention speed factor separately — it works differently from standard detection.

Pig Butchering — Intervention Speed Factor Victim-initiated: different formula

% of wires intercepted before clearing 40%

Wire interception requires real-time detection before Fedwire settlement (typically 2–4 hour window). 40% is achievable with the agentic pipeline; 60%+ requires proactive customer outreach protocols beyond AI detection alone.

Gross Fraud Exposure Rate (% of txn volume) Benchmark: 0.15%

Gross attempted fraud as % of total transaction volume — higher than net loss rate

Annual Transaction Volume ($M) Total payment volume

Total annual transaction volume processed (not fee revenue)

Loss Preventability Factor (%) Default: 75%

Of incremental fraud detected, what % results in actual loss prevention? Accounts for partial recoveries, detection latency, and false-positive fraud cases. Industry range: 60–85%.

Labor Realization Factor (%) Default: 75%

Of freed analyst hours, what % translates to measurable savings? Covers redeployment lag, partial reallocation, and roles not directly reducible. Conservative orgs: 50–65%; progressive: 75–85%.

Current Baseline Detection Rate (%) Critical input

Your fraud team's current detection rate before CAIBots. Detection lift inputs above represent incremental improvement over this baseline. Typical range: 55–75%. Higher baseline → smaller incremental improvement → lower L2.

Implied annual fraud losses

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Calculator L2 loss avoided

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L2 as % of implied losses

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Sanity check: L2 loss avoided should be 10–60% of implied gross fraud exposure. The baseline detection rate above moderates this — at 65% baseline, detection lifts apply to the remaining 35% residual fraud pool. Values above 80% suggest inputs are too optimistic. Values below 5% suggest baseline is set too high or detection lifts are conservative.

Chargeback Savings — CNP & ACH NEW LEVER

Distinct from fraud loss — chargebacks add processing + dispute cost on top of the transaction loss

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Chargeback cost is separate from fraud loss. A CNP fraud generates both the transaction loss AND a chargeback processing fee. Both are real costs; there is no double-count between L2 and L3.

Monthly CNP Transaction Volume Total card-not-present

Total CNP transactions/mo across all card products

CNP Chargeback Rate — Before (%) Benchmark: 1.2%

CNP Chargeback Rate — After (%) Benchmark: 0.5%

All-in Cost per Chargeback ($) Benchmark: $75

Processing fee + dispute handling + card brand penalties

NACHA Return Fee / ACH Fraud Item ($) Benchmark: $25

ODFI return processing cost per fraudulent ACH item

Avg ACH Fraud Items per Case Benchmark: 8

Items per phantom payroll or ACH fraud incident

False Positive Recovery

Revenue recovered from unblocking good transactions — often the largest single lever for card issuers

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False positive recovery counts margin on recovered transactions only. Do not also count customer lifetime value from the same population — that is a different metric and would double-count.

Monthly Fraud Alerts (all types) Benchmark: 2,000

False Positive Rate — Before (%) Benchmark: 12%

False Positive Rate — After (%) Benchmark: 5%

Avg Blocked Transaction Value ($) Benchmark: $185

Contribution Margin (%) Benchmark: 18%

Gross profit on recovered transaction revenue

FP Resolution Time (hrs) Benchmark: 0.5

Analyst time per false positive cleared

Regulatory Exposure — Probability-Weighted EV

Expected value of fine avoidance — supporting context for CFO, not hard ROI

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Regulatory EV carries inherent uncertainty. Present these as probability-weighted ranges, not guarantees. Apply your own judgment based on your examination history and regulatory standing.

P(SAR Filing Failure) — Before (%) Benchmark: 5%

P(SAR Filing Failure) — After (%) Benchmark: 1.5%

Avg SAR Failure Penalty ($) Benchmark: $2.5M

P(Reg E Late Credit) — Before (%) Benchmark: 8%

P(Reg E Late Credit) — After (%) Benchmark: 1%

Avg Reg E Violation Penalty ($) Benchmark: $500K

P(NACHA ODFI Violation) — Before (%) Benchmark: 6%

P(NACHA ODFI Violation) — After (%)Benchmark: 1%

Avg NACHA Penalty ($) Benchmark: $250K

P(Elder Mandatory Report Miss) — Before (%)Benchmark: 10%

P(Elder Report Miss) — After (%)Benchmark: 2%

Avg State Elder Penalty ($) Benchmark: $150K

Solution Cost — Full Stack

Platform fee + AI inference + integration. The denominator most ROI tools hide.

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Legal Entities / Business Lines

Jurisdiction

Data Residency

Contract Term

SLA Tier

Integration Complexity

Regulatory Standing

Analysis Results Live

Annual Benefits

Analyst time savings — 10 fraud workflowsHigh confidence

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Fraud loss avoidedVerify inputs

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Chargeback savings — CNP + ACHNEW

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False positive revenue recoveryMed confidence

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Regulatory exposure EVLow-med confidence

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Total Annual Benefits

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Annual Solution Cost

Platform fee (asset tier × entity multiplier)

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Jurisdiction + data residency

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Regulatory standing adjustment

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SLA tier uplift

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Term discount applied

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AI inference cost (10 scenario types)

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Integration (amortized)

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Total Annual Solution Cost

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Cost per case — Before—

Cost per case — After—

Annual analyst hours freed—

Labor Saving by Fraud Type

Benefit Composition

Sensitivity Analysis

Conservative

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−20% volume · 50% benchmark performance · +15% cost

Base Case ← Your Numbers

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As entered · Demo Brief benchmarks

Optimistic

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+20% volume · Full benchmark performance · base cost

⚠ Important Limitations — Read Before Presenting

This calculator models efficiency gains, fraud loss reduction, and partial revenue/risk uplift from the 5-agent CAIBots Fraud Detection pipeline. The following components are not calculated and must be assessed separately:

Model degradation cost of the current system over 24–36 months without upgrade (cost of inaction)
Adversarial adaptation value — speed of containing novel attack waves before losses compound
Customer retention impact from false positive reduction (CLV uplift — avoid double-counting with Section 04)
SR 11-7 independent model validation: $50K–$200K — not included in solution cost
Time-to-benefit: shadow mode and pilot validation mean full benefits may not be realized until Month 6–9
When ROI exceeds 300%, the driver is typically L2 (fraud loss avoided) — validate case volumes and detection lifts with your fraud team before presenting to the CFO

Illustrative. Actual results vary by institution, case mix, regulatory environment, and operating model. Benchmark figures derived from FinCEN examination statistics, NACHA annual reports, and industry publications. Not a performance guarantee. Not legal or financial advice.

Key Default Assumptions — Fraud Detection

Assumption	Default Value	Source / Basis
Baseline fraud detection rate	65%	Industry median (Aite-Novarica 2024); range 55–80% by fraud type
Detection lift (AI incremental)	Applies to residual undetected pool only	Conservative: AI can only detect within the currently-missed 35% — not re-detect already-caught fraud
Prevention / recovery factor	75%	Not all detected fraud is prevented in time; adjust per your ops model
Labor realization factor	75%	Slider in Section 01; industry standard workforce automation discount
Year 1 benefit ramp	70% (hard-coded)	Reflects pilot + shadow-mode period; add ramp slider for client customization
FP revenue recovery margin	User-defined (default 2%)	Net margin on unblocked good transactions; institution-specific
Regulatory EV	Probability × penalty (user-defined)	Expected value; verify penalty ranges with your compliance team
Revenue growth assumption	5% per year	Applied to 5-year IRR projection; conservative; adjust to your forecast

See This Applied to Your Institution →

30-minute architecture session · We map this pipeline to your fraud platform, data infrastructure, and analyst workflow

Legal Disclaimer & Material Assumptions

For informational and illustrative purposes only. This calculator generates forward-looking financial estimates based solely on the inputs you provide. It does not constitute financial advice, investment advice, legal advice, or a binding commercial commitment. CAIBots makes no representation or warranty, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of outputs generated by this tool.

Actual results will vary. Projected savings, ROI, payback periods, IRR, and NPV are estimates derived from user-supplied inputs and publicly available industry benchmarks. They are not guarantees of future performance. Realized benefits depend on actual transaction volumes, staffing levels, integration complexity, regulatory environment, model validation timelines, and organizational factors not fully captured by any calculator.

Financial methodology. ROI = (Net Annual Benefit − Total Annual Cost) ÷ Total Annual Cost. Payback via cumulative monthly cash-flow simulation; Year 1 benefit ramp default 70% (adjustable). IRR uses Newton-Raphson iteration on a 5-year cash-flow series: Year 0 = one-time implementation cost only; recurring platform and API fees deducted from each future year. IRR figures for SaaS models are directional — a small one-time Y0 capex relative to large recurring savings produces high percentages. Compare to your internal hurdle rate; do not interpret absolute value. 3-Year NPV discounted at 8% WACC (adjustable). Nominal USD; no inflation adjustment.

Sensitivity scenarios. Conservative: −20% volume · 70% of benchmark savings · +15% cost · 60% Year 1 ramp. Optimistic: +20% volume · 100% benchmark savings · base cost · 80% Year 1 ramp. These parameters are identical across all CAIBots ROI calculators to enable consistent cross-product comparison.

Benchmark sources. Default inputs derived from: FFIEC examination statistics, FinCEN SAR/CTR annual reports, NACHA ACH network data, Celent/Aite-Novarica/Oliver Wyman industry surveys, and aggregated anonymized data from CAIBots client engagements. CAIBots strongly recommends replacing defaults with your institution’s own volume, cost, and rate data before presenting results to executive leadership, boards, or procurement committees.

Your Numbers.Your Real ROI.

Your Numbers.
Your Real ROI.