solsticestudioai/dark-pool-pack

Aestrea Dark Pool Financial Anomaly Pack (Sample)

A synthetic financial-anomaly and fraud-detection dataset for AML / transaction-monitoring model training, false-positive reduction research, and fintech anomaly-detection pipelines. Each row is a complete financial activity sequence — a dormant warm-up period followed by a sudden activity burst — labeled as account takeover, market-manipulation spoofing, or legitimate-but-high-risk (false positive) behavior, with telemetry, identity context, detection logic, and financial exposure.

Built by SolsticeAI as a free sample of a larger commercial pack. 100% synthetic. No real transactions, accounts, addresses, order books, or customer data.

What is included

File	Rows	Format	Purpose
dark_pool_sample.parquet	10,000	Parquet	Columnar, typed, best for analytics
dark_pool_sample.jsonl	10,000	JSON Lines	Streaming / LLM training friendly

This sample: 10,000 financial activity sequences, stratified 3,333 per fraud class.
Fraud classes (3): Account_Takeover_Sequence, Market_Manipulation_Spoofing, Legitimate_High_Risk_Activity (false positive)
Label distribution: ~6,666 fraudulent / ~3,334 benign (by design — one of three scenarios is a benign high-risk lookalike)
Severity tiers: low, medium, high, critical — scenario-weighted so spoofing skews mid, ATO skews high, false-positives skew low
Assets covered: BTC, ETH, USDC, USDT, SOL, USD, EUR, GBP, JPY
Geographic coverage: 20 country codes across high-risk and low-risk jurisdictions

Record structure

Each record is one financial activity sequence with 7 top-level fields:

Field	Type	Contents
schema_version	string	Pack schema version (1.0.0-dark-pool-sample)
event	struct	id, trace_id, timestamp, severity, label, label_confidence
identity_context	struct	account_id, baseline_risk_score, session_entropy, account_age_days, kyc_tier
telemetry_stream	list	Ordered financial events: timestamp, event_name (e.g., LARGE_WITHDRAWAL, LIMIT_ORDER_CANCELLED, LOGIN_NEW_COUNTRY), asset, transaction_amount_usd, geo_country, latency_ms, burst_indicator
vulnerability	struct	fraud_class, scenario_description, exposure_vector
detection	struct	anomaly_type, baseline_deviation, detection_logic (SQL-like), anomaly_score, confidence_band
impact	struct	financial_exposure_usd, customer_funds_at_risk_usd, recoverable_pct
simulation	struct	synthetic, engine, chaos_profile, ground_truth_classification

See SCHEMA.md for the full nested field breakdown.

Why this dataset is useful

Most public financial-fraud datasets are either flat tabular snapshots (IEEE-CIS, credit-card fraud) or narrow single-event labels. Real AML / transaction-monitoring models need something these don't provide: sequences (warm-up → burst), ambiguous labels (legitimate-but-high-risk that looks like fraud), and detection logic grounded in behavioral patterns rather than just feature engineering.

• Full pre-event dormant and check-in telemetry — not just the burst
• burst_indicator flags at each step so models can learn temporal dynamics explicitly
• A dedicated false-positive class (Legitimate_High_Risk_Activity) with benign ground truth but fraud-adjacent behavior — the exact class that drives real-world FP rates
• Per-record detection_logic in SQL-like form shows how the scenario would be detected in production
• Per-record anomaly_score and label_confidence so models can train on calibration targets, not just hard labels
• Cross-asset (crypto + fiat), cross-geography coverage

Typical use cases

• Fraud-detection model training
• AML / transaction-monitoring pipelines
• False-positive-reduction research (reducing customer-experience pain)
• Risk-scoring systems
• Account-takeover detection
• Spoofing / market-manipulation classifiers
• Temporal-burst anomaly detection
• LLM fine-tuning on fraud-investigation narratives
• Benchmarking anomaly scoring against ambiguous ground truth

Quick start

import pandas as pd
import pyarrow.parquet as pq

df = pq.read_table("dark_pool_sample.parquet").to_pandas()

# Label distribution (mix of fraudulent and benign)
print(df["event"].apply(lambda e: e["label"]).value_counts())

# Average financial exposure by fraud class
df["cls"] = df["vulnerability"].apply(lambda v: v["fraud_class"])
df["exposure"] = df["impact"].apply(lambda i: i["financial_exposure_usd"])
print(df.groupby("cls")["exposure"].mean().round(0))

# Burst density by scenario (bursts per sequence)
df["burst_count"] = df["telemetry_stream"].apply(
    lambda s: sum(1 for step in s if step["burst_indicator"])
)
print(df.groupby("cls")["burst_count"].mean().round(2))

# False-positive rate signal — proportion of fraudulent-label vs benign per severity
df["severity"] = df["event"].apply(lambda e: e["severity"])
df["label"] = df["event"].apply(lambda e: e["label"])
print(pd.crosstab(df["severity"], df["label"], normalize="index").round(3))

Streaming form:

import json

with open("dark_pool_sample.jsonl") as f:
    for line in f:
        sequence = json.loads(line)
        # one financial activity sequence per line

Responsible use

This dataset is intended for defensive / monitoring use cases: fraud-detection model training, AML research, false-positive reduction, and academic benchmarks. It contains synthesized transaction amounts, synthetic account IDs, and fictional scenario narratives — it does not contain real customer data, real transactions, real wallet addresses, or any PII. Models trained on this data will learn fraud-pattern shape and temporal dynamics; downstream deployment for live transaction monitoring requires calibration against institution-specific real-world data under appropriate compliance review.

License

Released under CC BY 4.0. Use freely for research, fraud-tool prototyping, education, and commercial development with attribution.

Get the full pack

This Hugging Face repo is a 10K-sequence sample. The production pack scales to 5M+ sequences with expanded scenario coverage (layering, smurfing, wash trading, sanctions-evasion routing, trade-based ML, bust-out fraud), additional asset classes (equities, FX, derivatives, NFTs), multi-entity transaction graphs, richer ambiguous-label distribution tuning, parquet + JSONL + SAR-narrative-aligned delivery, and buyer-specific variants.

Self-serve (Stripe checkout):

• Sample Scale tier — $5,000 — ~25K records, one subject, 72-hour delivery.

Full pack + enterprise scope:

• www.solsticestudio.ai/datasets — per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants.

Procurement catalog:

• SolsticeAI Data Storefront — available via Datarade / Monda.

Citation

@dataset{solstice_dark_pool_pack_2026,
  title        = {Aestrea Dark Pool Financial Anomaly Pack (Sample)},
  author       = {SolsticeAI},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/solsticestudioai/dark-pool-pack}
}