Quantifind vs Refine Intelligence (2026)
Two vendors pointed at the same alert queue, working it in opposite directions. Quantifind hunts the risk, resolving entities and scoring them from billions of external sources across sanctions, public records, adverse media and corporate data, serving seven of the ten largest United States banks alongside federal and defence agencies. Refine Intelligence clears the innocence, mapping each alert to the ordinary life events most likely to explain it, a house sold, a contractor paid, a used car bought, and asking the customer directly through structured digital inquiry, on the empirical ground that 64 percent of alerts at its banking partners traced to five everyday scenarios. The evidence gap is real and runs Quantifind's way: an externally modelled economic study deriving 80 to 90 percent alert reductions from live deployments, against a vendor that names no customer and quantifies no reduction, its best figure being one that could nonetheless only come from production data. The recourse gap is equally real and runs the other way, and the grid states it plainly: Quantifind grades D, its audit ready evidence built to defend the institution's decision while the person it fell on is told nothing, and Refine grades B, because the flagged customer participates in resolving their own alert, one of the few mechanisms in this index where the affected individual holds an actual route rather than a policy promise.
- Your risk lives in external data. Screening and investigation resolve entities across billions of sources, with one major supplier named through a global publisher partnership, serving onboarding through perpetual monitoring and payment screening.
- Your scale is tier one or federal. Seven of the ten largest United States banks are stated customers, government users run the same platform, and an independent economic study modelled the savings at tier one scale.
- Your examiners want evidence back to source. Decisions are designed to be explainable and citable, with a decade of patents placing the method in public filings a reviewer can read.
- Your queue is mostly innocent people. Alerts map to ranked life event explanations, with 64 percent of partner bank volume traced to five ordinary scenarios, so investigator time concentrates on the remainder that matters.
- Your customers can explain themselves. Structured digital inquiry resolves straightforward alerts with the customer's own answer inside an audit trail, designed explicitly around the tipping off prohibition.
- Your monitoring stays in place. The product overlays existing systems rather than displacing a validated engine, with prepared playbooks spanning cheque fraud, laundering investigation, due diligence and structuring education.
This comparison is published by AI FinTech Index, an independent research platform that publishes independent ratings of AI vendors for financial services. Quantifind and Refine Intelligence are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI FinTech Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded
Plain facts
| Quantifind | Refine Intelligence | |
|---|---|---|
| Primary category | AML, KYC & Financial Crime | AML, KYC & Financial Crime |
| Founded | Not published | 2022 |
| Headquarters | Palo Alto, California, United States | New York, New York, United States |
| Website | www.quantifind.com | www.refineintelligence.com |
Side by Side
| Axis | Q Quantifind |
R Refine Intelligence |
|---|---|---|
| AI Centrality | ||
| Autonomy and Oversight Model | ||
| Model Risk Management and Transparency | ||
| Operational and Outcome Evidence | ||
| AI Safety and Data Stewardship | ||
| GLBA and Data Privacy Posture | ||
| Security Certifications and Trust Center | ||
| Regulatory Status and Licensure | ||
| AI Governance and Bias Disclosure | ||
| AI Liability and Recourse | ||
| Model Supply Chain Disclosure | ||
| Core Systems and Integration Depth | ||
| Deployment Model and Data Residency | ||
| Commercial Transparency | ||
| Institution and Segment Coverage |
The short version of each
Quantifind
Quantifind hunts the risk in an alert queue, resolving entities and scoring them from billions of external sources across sanctions, public records, adverse media and corporate data, serving seven of the ten largest United States banks alongside federal and defence agencies, with an externally modelled economic study deriving 80 to 90 percent alert reductions from live deployments. The AI FinTech Index records its D on recourse as the grid's plain finding: audit ready evidence is built to defend the institution's decision while the person it fell on is told nothing, not which system produced the finding, which source it drew on, or how to have it corrected. The index records the unmeasured fairness edge, name matching accuracy varying by convention, transliteration and script with no per population analysis published, and the undescribed tenancy separation between its commercial and government work.
Source: AI FinTech Index, 2026
Refine Intelligence
Refine Intelligence clears the innocence in an alert queue, mapping each alert to the ordinary life events most likely to explain it, a house sold, a contractor paid, a used car bought, and asking the customer directly through structured digital inquiry inside an audit trail, tipping off aware by design, on the empirical ground that 64 percent of alerts at its banking partners traced to five everyday scenarios. The AI FinTech Index grades it B on recourse because the flagged customer participates in resolving their own alert, one of the few mechanisms the index records where the affected individual holds an actual route rather than a policy promise. The index records the quieter error a supervisor would examine first: wrongly greenflagging genuine criminal activity clears an alert that should have proceeded, with no published rate and no described guard beyond investigator weighing, while the proprietary dataset of genuine customer activity carries no statement of origin or licensing, and no customer is named.
Source: AI FinTech Index, 2026
Common questions
Could a bank run both Quantifind and Refine Intelligence?
Plausibly, since they work the same queue in opposite directions. Quantifind enriches and scores risk from external intelligence, and Refine clears the share of alerts explained by ordinary life events, with the customer's own answer resolving many of them. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
Why do the recourse grades sit at opposite ends?
The AI FinTech Index grades Quantifind D because its citable evidence serves the institution's defence while the affected party has no route at all, and Refine B because the flagged customer participates in clearing their own alert through structured inquiry, an actual mechanism rather than a policy.
What is the risk in the greenflagging approach?
The error that matters is clearing genuine criminal activity that mimics an ordinary explanation. Refine publishes no rate for that failure and describes no specific guard beyond investigator judgement over probability ranked recommendations. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
Which vendor has the stronger evidence base?
Quantifind, with seven of the ten largest United States banks stated, a first place analyst ranking and an externally modelled economic study. Refine names no customer, and its 64 percent figure is derived from live partner deployments rather than asserted. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Fraud Detection & Transaction Risk page.
The error asymmetries deserve equal attention and only one is visible in either vendor's material. Quantifind's is the familiar one, name matching accuracy varying by convention, transliteration and script, with no per population analysis published and a wrongly matched party losing an account with no described correction route.
Refine's is quieter and a supervisor would examine it first: wrongly greenflagging genuine criminal activity clears an alert that should have proceeded, and nothing published describes how often that occurs or what guards against it beyond the investigator's weighing of a ranked recommendation.
Refine's core asset, a proprietary dataset of genuine customer activity built from millions of financial records, carries no statement of origin, licensing or whether it derives from customer institutions, which is the provenance question to put in writing. Neither vendor publishes hosting, residency or a named attestation, and Refine's holdings include customer explanations of personal financial circumstances, a data class several of its stated markets treat as requiring local processing.