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FINRA Securities Arbitration

Pattern intelligence from the full public award corpus.
What the corpus reveals about cases shaped like yours.

The corpus

What the corpus reveals

The public FINRA award corpus spans decades and tens of thousands of decided cases. Each case is encoded as a structural fingerprint: the claims asserted, the conditions that appeared, the panel's findings, the outcome.

This is not keyword search. What you get back is pattern context: the structural shape of cases like yours, what conditions have been present when panels found for claimant, what conditions have shifted outcomes the other way. You see frequency, co-occurrence, and the direction of influence. Not a summary or opinion.

Coverage

Claim types in the corpus

Claim typeCases in corpus
Suitability: unsuitable investment strategy847
Failure to supervise741
Misrepresentation / omission688
Churning: excessive trading612
Breach of fiduciary duty503
Breach of contract445
Unauthorized trading389
Negligence372
Fraudulent transactions284
Unjust enrichment198
Forgery156
Conversion134
Additional claim types indexedN/A

Sample dataIllustrative counts; not live corpus statistics. Accurate figures will be published at general availability.

Sample output

What a pattern brief contains

Sample dataIllustrative example: numbers are representative, not live corpus statistics.

Claim type: Suitability: Unsuitable investment strategyCases with panel decision on merits: 631

Co-occurrence

Appears alongsideFrequency
Churning / excessive trading62% of suitability cases
Misrepresentation / omission44%
Failure to supervise38%
Breach of fiduciary duty31%

When suitability and churning appear together, the combined claim succeeds at a higher rate than either claim alone. The pattern is consistent across case vintages.

Outcome conditions

Panel finds for claimant in 41% of suitability cases overall.

Conditions associated with higher claimant success

  • Account concentration above 40% in one security or sector: 67% claimant success rate (n=214)
  • No written acknowledgment of client risk tolerance on file: 59% claimant success rate (n=187)
  • Multiple unsuitable recommendations documented across account history: 63% claimant success rate (n=156)

Conditions associated with lower claimant success

  • Suitability letter signed by client and on file: 19% claimant success rate (n=143)
  • Client requested the specific securities in writing: 24% claimant success rate (n=98)
  • Client classified as sophisticated or accredited investor: 22% claimant success rate (n=76)

What the pattern does not tell you. This is structural frequency from the public corpus. It is not a prediction and does not account for case-specific evidence, arbitrator composition, or facts not captured in the award text. The panel weighs the evidence you present. The corpus tells you what evidence has mattered in cases with this shape.

How it works

Bring a case.
Get pattern context back.

Describe the case: claim types, the facts that matter, what you are trying to establish.

Sediment returns pattern context for your claim shape. What the corpus knows about cases structured like yours. What conditions have shifted outcomes in those cases. The closest prior decisions by structural fingerprint.

You use that context to sharpen theory, identify what conditions the panel is likely to weigh, and see what the opposing pattern looks like.

Pattern context is delivered as a structured brief in the same format as the sample above, for each claim type in your case.

01

Describe your case

Claim types, key facts, what you need to establish

02

Corpus match

Structural fingerprints matched across decided cases

03

Pattern brief delivered

Co-occurrence, outcome conditions, direction of influence

Early access

Advisory Partner Program: FINRA

Four seats. Claimant-side practitioners only.

Pattern context for every FINRA claim type in the corpus
Private session layer for active cases: your case facts stay separate from the public corpus
Direct feedback channel: your practice shapes what the corpus learns next
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