Nachammai41/underserved-persona_conditioned-fraud-v3

Persona-Conditioned Fraud Detection Dataset (v3, Citation-Grounded)

What's new in v3 vs v2

v2 generated personas from design assumptions. v3 grounds every load-bearing persona field in a real-world source (FinCEN advisories, FDIC microdata, Urban Institute reports, Menjívar et al. TPS survey, Del Real Venezuelan migration interviews, Remitly 10-K, Wise / Inter-American-Dialogue industry reports, Treasury OIG fraud alerts, IRS SOI filer statistics). Every fraud vector is mapped to a FinCEN typology code.

The generation machinery (TabDDPM-style persona-conditioned sampling with joint platform→hour→amount for gig workers, five v2 tightening rules, Adaption Labs narrative fill) is unchanged from v2 R4. Only the conditioning input — the persona profiles — is upgraded.

Three new universal columns on every transaction

Field	Type	Description
persona_source_ids	list[string]	Citation IDs for real-world sources that grounded this record's persona
fraud_vector_typology_ref	string (nullable)	FinCEN SAR Advisory / FTA Identity typology code for the row's fraud_vector; null on legit rows
behavioral_evidence_grade	string A/B/C/D	Evidence quality: A=ethnographic field interview; B=regulatory typology; C=industry research; D=synthetic design assumption

Quick start

from datasets import load_dataset

# Full 20,000-row combined dataset
ds = load_dataset("Nachammai41/underserved-persona_conditioned-fraud-v3", name="all")["train"]

# One archetype
remit = load_dataset("Nachammai41/underserved-persona_conditioned-fraud-v3", name="remittance")["train"]

# Citation registry — look up a persona_source_id
sources = load_dataset("Nachammai41/underserved-persona_conditioned-fraud-v3", name="sources")["train"]

# FinCEN typology lookup — resolve fraud_vector_typology_ref
typology = load_dataset("Nachammai41/underserved-persona_conditioned-fraud-v3", name="typology_registry")["train"]

Dataset Statistics

Metric	Value
Total transactions	20,000 (5,000 per archetype)
Total personas	46 (12 remittance, 12 gig_worker, 10 unbanked, 12 ITIN)
Fraud rate	~10% per archetype
Sources in registry	13 (7 PDFs + 1 data bundle + 5 links)
FinCEN typology codes	25 (14 FTA Identity 2024 + 11 SAR Advisory Key Terms)
Languages represented in narratives	17+ (en, es, vi, ht, hi, mr, ta, te, yo, fr, am, tw, ru, zh, tl, ko, ar)
Overall grade distribution	A 6.3% / B 50.0% / C 22.9% / D 20.8%

Archetypes

Archetype	Personas	Key Dimensions	Primary Source
Remittance	12	corridor_country, transfer_service_loyalty, family_crisis_history, sender_tenure	Remitly 10-K, Menjívar 2022, Del Real 2022, Wise 2023, IAD 2026
Gig Worker	12	platform_mix, daily_cashout_pattern, device_stability, sim_history	Vallas & Schor 2020, FinCEN FTA 2024, Fed synthetic-ID
Unbanked	10	kiosk_location, prepaid_card_stack, income_source, documentation_status	FDIC 2023 HH Survey microdata (empirical distributions)
ITIN	12	business_type, tax_filing_history, credit_file_age, accountant_relationship	Menjívar 2022 (former-TPS lapse), Treasury OIG, IRS SOI, FinCEN SAR

Available Configs

Transaction data:

• all — 20,000 rows across 4 archetypes
• remittance / gig_worker / unbanked / itin — 5,000 rows each

Reference / attribution:

• personas — 46 persona profiles with evidence grade, source IDs, per-field grounding
• sources — 13-entry citation registry
• typology_registry — 25 FinCEN typology codes with applies_to_fraud_vectors mapping

Schema (per transaction row)

Field	Type	Description
data_uuid	string	Unique identifier
persona_id	string	Source persona (e.g., rem_004, gig_001) — join to personas
archetype	string	remittance, gig_worker, unbanked, itin
dataset_version	string	"v3"
transaction_amount_usd	float	USD amount
fee_amount_usd	float	USD fee
sender_age	int	Persona-derived age with jitter
hour_of_day	int	Persona-window constrained hour
day_of_week	int / string	Day index / name
days_since_last_txn	int	Cadence-derived interval
account_age_days	int	Tenure-derived account age
txn_count_30d	int	Cadence-derived monthly count
instrument	string	Payment method / platform
language	string	From persona's language_mix
fraud_vector	string	Fraud type or instrument label
narrative_text	string	Adaption-generated first-person narrative
is_fraud	int	0 = legit, 1 = fraud
device_type	string	Persona's device
device_stability	float	Device churn proxy
record_timestamp	string	ISO timestamp
persona_source_ids	list[string]	v3: citation IDs that grounded this record's persona
fraud_vector_typology_ref	string (nullable)	v3: FinCEN typology code for the fraud_vector
behavioral_evidence_grade	string A/B/C/D	v3: evidence quality grade

Grade Distribution

Archetype	A (ethnographic)	B (regulatory)	C (industry)	D (design)
Remittance	16.7%	25.0%	41.7%	16.7%
Gig Worker	0%	25.0%	41.7%	33.3%
Unbanked	0%	100%	0%	0%
ITIN	8.3%	50.0%	8.3%	33.3%

Generation Pipeline

13 curated sources (PDFs + links)
    └── Structured extraction (persona_dimensions per source)
        └── 46 grounded persona profiles with per-field attribution
            └── TabDDPM v3 generator (joint platform+hour sampling,
                five v2 tightening rules, per-persona fraud-vector weighting,
                FinCEN typology resolution)
                └── 20,000-row transaction dataset (22 columns)
                    └── Adaption Labs narrative fill (persona-anchored prompts)
                        └── Final dataset with narrative_text (25 columns)

Citation

@dataset{palaniappan2026underserved,
  author    = {Palaniappan, Nachammai},
  title     = {Underserved Financial Fraud Dataset},
  year      = {2026},
  publisher = {HuggingFace},
  note      = {Created with Adaptive Data by Adaption.
               Uncharted Data Challenge, Adaption Labs.},
  url       = {https://huggingface.co/datasets/nachammai779/underserved-financial-fraud}
}

License

Released under CC-BY-4.0 for research and educational purposes. Persona names are fictional; biographical details are composed from published aggregate source evidence. Any resemblance to real individuals is coincidental.

Credits

• Adaption Labs — narrative fill via Enhanced Completion recipe
• FinCEN — Financial Trend Analysis 2024 (Identity), SAR Advisory Key Terms
• FDIC — 2023 National Survey of Unbanked and Underbanked Households
• Menjívar, Agadjanian & Oh — "The Contradictions of Liminal Legality" (Soc Probl 2022)
• Del Real — "Seemingly inclusive liminal legality" (J Ethn Migr Stud 2022)
• Vallas & Schor — "What Do Platforms Do?" (Annu Rev Sociol 2020)
• Remitly, Wise, Inter-American Dialogue, Oxfam America, IRS SOI, Treasury OIG, Federal Reserve (FedPayments Improvement) — industry & regulatory sources
• Tab-DDPM — Gaussian multinomial diffusion for tabular data (Kotelnikov et al.)