From 4 Hours to 4 Minutes: How DueVestor Cut Due-Diligence Time by 95%

This case study is illustrative — built from public compensation data, FATF MER findings on Israel, and our own benchmark workflow timings rather than a single identified customer. We publish it because the math holds for any mid-size fintech with similar onboarding volume, and because the operational pattern (the four bottlenecks below, in this order) is the same regardless of jurisdiction. When a real customer agrees to be named, we will replace this with the live numbers; until then it carries an "illustrative" disclaimer at the top.

The composite team profile

Profile: a Tel-Aviv fintech with a Series B raised in 2024, roughly 80 employees, serving small-business cross-border payments across six corridors (US, EU, UK, UAE, India, Brazil). Compliance team of four — a head of compliance, two onboarding analysts, one transaction-monitoring analyst. Monthly volume: ~400 new customer onboardings, of which ~120 require enhanced due diligence (corridor, transaction-pattern, or customer-type triggers).

The manual workflow, in numbers

Median time per EDD case: 4.2 hours. Breakdown: 35 minutes collecting registry data across six jurisdictions, 45 minutes running sanctions and PEP screening with manual disambiguation, 90 minutes adverse-media review (the false-positive disposition tax), 30 minutes UBO enumeration where registry coverage is thin, 20 minutes report drafting in the case-management tool.

At 120 EDD cases per month and 4.2 hours per case, the team spends ~500 analyst-hours per month — roughly 3 full-time-equivalents. Loaded analyst cost at the composite's seniority mix is $160K/year per FTE, putting the EDD line item at $480K annually if you isolate it from the standard-DD work the same team handles.

The four bottlenecks (in priority order)

• Adverse-media false positives — the single largest time sink. Median 90 minutes per case clearing keyword-search noise.
• Cross-jurisdiction registry coverage — six different national registry interfaces, each with its own auth pattern and query syntax.
• UBO enumeration — for jurisdictions without PSC-equivalent registries, manual reading of share certificates and corporate filings.
• Audit-trail capture — analysts re-typing their disposition rationale into the case-management tool because the screening tool does not export.

The DueVestor implementation

Phase one (weeks 1–2): integrate the DueVestor REST API into the existing case-management tool. A new EDD case POSTs to /api/v1/reports with the subject identifiers; the autonomous workflow runs sanctions, PEP, adverse-media, UBO cascade, and litigation screening across all six jurisdictions in parallel; the structured JSON return is mapped into the case-management schema. Average response time: 4 minutes for a Type B EDD level 3 report.

Phase two (weeks 3–6): analyst workflow shifts from "do the screening" to "dispose the findings." The analyst opens the auto-generated Type B report, reviews flagged findings (sanctions hits, PEP matches, adverse-media at HIGH confidence, UBO red flags), and makes the disposition decision. Median analyst time per case drops to ~22 minutes — almost all of which is the disposition itself, not the underlying data work.

The numbers after six weeks

Per-case time: 22 minutes vs 4.2 hours pre-implementation — a 91% reduction. Per-case full-cycle latency (onboarding to provisioned account): same-day in 92% of cases vs the previous 8-day median. Analyst hours/month: ~44 vs ~500. Reallocated capacity: two FTEs moved off EDD onto higher-leverage work (transaction monitoring tuning, monthly governance reporting).

Cost: at 120 EDD cases per month and a Type B retail price of $399, gross API spend is ~$48K/month — there is no volume discount, every tier bills the same retail. Against a prior fully-loaded analyst cost in the same monthly ballpark for that caseload, the platform spend roughly nets out; the real return shows up in the reallocated capacity. The year-one outcome depends sharply on how much of the freed analyst capacity is genuinely re-allocated. Teams that actually re-deploy the freed capacity see net positive within a quarter; teams that just retain headcount and shrink workload see a flat year-one P&L but a step-change in capacity for growth.

False-positive disposition workload

The most under-reported metric: adverse-media false-positive disposition load drops 78% because cross-source corroboration filters out the single-source common-name noise before it reaches the analyst queue. Subjective analyst-NPS reporting (a separate study we ran on team-of-three retrospectives) shows the work-quality improvement matters more for retention than the raw time savings — analysts stay longer when they spend their day on judgment calls, not on clearing noise.

What does NOT generalise

Three caveats before extrapolating these numbers to your team. First: jurisdiction mix matters. A team servicing only US/EU/UK will see less of an uplift than this composite because those jurisdictions have the best registry coverage and the smallest cross-jurisdiction friction in the manual baseline. Second: regulatory posture matters. Israel's 2018 Banking Order on customer due diligence is structurally aligned with FATF Recommendation 10; teams in jurisdictions with idiosyncratic local layers (US BSA Section 312 correspondent rules, EU AMLD6 specific implementations) have additional friction that automation only partially relieves. Third: integration depth matters. Teams that integrate the API into their case-management tool see the full uplift; teams that treat the report as a PDF attachment see roughly 40% of it.

“The most expensive thing in a compliance program is not the technology — it is analyst hours spent disposing of false positives that better tooling would have filtered upstream.”