Cense vs Scorechain (2026)
The decision is which direction your crypto problem faces, inward at a client you want to accept or outward at a book you police. Cense assembles admission evidence. Wallet, exchange, blockchain, fiat and client submitted records are reconciled into one case, with transfers matched, duplicates removed and balances validated, so a relationship manager, a compliance officer and a financial crime specialist can agree to onboard crypto wealth with the working shown. In crypto risk the product is usually refusal, and that purpose inverts it. Scorechain screens and monitors at scale: more than 100 blockchains, over 500 million addresses under management and 1,800 attributed service providers. A library of more than 300 named risk scenarios comes with sanctions screening against the United States, European and United Nations lists. It has sold since 2015 to more than 350 companies across 45 countries, with a Big Four joint offering in Germany. What joins them is a property this category rarely shows: both chose legibility over inference. Cense produces evidence rather than verdicts, and the conclusion is never drawn by the system at all. Scorechain publishes its analytics as configurable, inspectable scenarios on the stated argument that institutions should understand how risk is assessed. That stance keeps AI less central to the product and buys its customers the ability to explain a decision rather than relay it. Scorechain also names its regime precisely: MiCA with its dates, the Luxembourg supervisor, the money laundering directives by number and the German act by commencement date, which is the disclosure a supervised institution can actually map against.
- Your problem is a client you want, not a risk you fear. Source of wealth reconstruction a compliance officer can defend converts a decline into a documented yes, with Van Lanschot Kempen, a Dutch private banking group, already using it for exactly that.
- Three functions agree on one case. Relationship managers, compliance and financial crime specialists work the same case environment, and output is formatted to existing financial crime workflows rather than to a new process.
- Provenance matters to your diligence. The analytics foundations come from Glassnode, its established on chain analytics parent, and the seed round was co led by a banking infrastructure group's venture arm and a large Dutch bank's investment arm, strategic money that ran its own checks.
- You screen a book, not a client. Continuous monitoring across a hundred blockchains, half a billion addresses and a configurable scenario library serves exchanges, banks and payment firms with ongoing obligations rather than onboarding decisions.
- Your examiner wants the regime named. Rules, lists and reporting templates configure to MiCA, the money laundering directives and national law by name, on a private cloud stated to be hosted in the European Union.
- Explainability is your condition for automation. A compliance officer can see which of the named scenarios fired and tune thresholds to the institution's own appetite, rather than relaying a proprietary score it cannot defend.
This comparison is published by AI FinTech Index, an independent research platform that publishes independent ratings of AI vendors for financial services. Cense and Scorechain 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
| Cense | Scorechain | |
|---|---|---|
| Primary category | AML, KYC & Financial Crime | Not published |
| Founded | 2023 | 2015 |
| Headquarters | Baar, Switzerland | Belvaux, Luxembourg |
| Website | www.cense.com | www.scorechain.com |
Side by Side
Select any grade to read the note behind it.
| Axis | C Cense |
S Scorechain |
|---|---|---|
| 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
Cense
Cense assembles admission evidence for crypto wealth. It reconciles wallet, exchange, blockchain, fiat and client submitted records into one case, matching transfers, removing duplicates and validating balances, so a relationship manager, a compliance officer and a financial crime specialist can agree to onboard a client with the working shown. According to the AI FinTech Index, that purpose inverts a category where the product is usually refusal. The system produces evidence rather than verdicts, and the conclusion is never drawn by the machine at all. Nothing published states how long a reconstructed wealth history persists once an assessment concludes, including for a declined client. No process is described for correcting a wrong attribution inside a case file that is fuller than the person's own records.
Source: AI FinTech Index, 2026
Scorechain
Scorechain screens and monitors crypto exposure at scale: more than 100 blockchains, over 500 million addresses under management, 1,800 attributed service providers, and a library of more than 300 named risk scenarios with sanctions screening against the United States, European and United Nations lists. It has sold since 2015 to more than 350 companies across 45 countries, with a Big Four joint offering in Germany. According to the AI FinTech Index, its legibility choice is the substance. Analytics are published as configurable, inspectable scenarios on the stated argument that institutions should understand how risk is assessed. That keeps AI less central to the product and lets customers explain a decision rather than relay it. Its European regime is cited to dates, directive numbers and supervisor. No measured attribution accuracy is published and no route exists for a wrongly labeled wallet holder, and risk inherited through graph proximity attaches to addresses that did not choose their counterparties.
Source: AI FinTech Index, 2026
Common questions
How do Cense and Scorechain differ?
Opposite directions. Cense assembles admission evidence so a bank can accept crypto wealth with the working shown, in a category where the product is usually refusal. Scorechain screens and monitors a book at scale across more than 100 blockchains and 500 million addresses. 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.
What do the two share?
Both chose legibility over inference. Cense produces evidence rather than verdicts, and Scorechain publishes its analytics as configurable, inspectable scenarios so an institution can explain a decision rather than relay it. 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 names its regulatory regime?
Scorechain, precisely: MiCA with dates, the Luxembourg supervisor, the money laundering directives by number and the German act by commencement date. 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.
What does neither address?
The afterlife of a determination: how long a declined client's reconstructed wealth history persists at Cense, and how a wallet holder learns which scenario fired or gets a corrected label propagated at Scorechain. 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 AML, KYC & Financial Crime page.
The shared silence is the afterlife of a determination. Cense publishes nothing on how long a reconstructed wealth history persists once an assessment concludes, including for a declined client, and describes no process for correcting a wrong attribution inside a case file, though the file is fuller than the person's own records.
Scorechain describes no route for a wallet holder to learn which scenario fired or to have a corrected label propagate to institutions that already acted on the wrong one, and risk inherited through graph proximity attaches to addresses that did not choose their counterparties. Neither publishes a measured attribution accuracy, and Scorechain's generic claim of alignment with recognized security standards sits oddly beside a disclosure posture that names everything else precisely.