Nachammai41/underserved-persona_conditioned-fraud-v4

Persona-Conditioned Fraud Detection Dataset (v4 + v4.1, Full Typology Coverage)

A 20,300-row citation-grounded synthetic fraud-narrative dataset for four underserved US financial-system archetypes — remittance, gig_worker, unbanked, ITIN — with all 25 FinCEN typology codes exercised.

What's new vs v3

V3 covered 10 of 25 FinCEN typology codes. v4 closed the gap to 18/25 through three targeted changes:

1. 16 persona edits documenting fraud events (SIM-swap, BEC, hawala/IVTS, hurricane scams, money-mule, synthetic-ID, unauthorized-ACH, lax-KYC, false-chargeback, COVID-imposter). 8 grade upgrades D→B.
2. SAR-preference resolver picks the more-specific SAR Advisory code when both an FTA Identity code and a SAR Advisory match a fraud_vector.
3. Combined 20k Adaption narrative fill — single job, 20 native scripts, per-row persona/transaction context.

v4.1 closed the remaining gap to 25/25 through:

1. Strip prompt-tag leakage from 1,235 narratives (Adaption was echoing the upload metadata block into completions for ~6% of rows).
2. Re-stamp 4 shadowed FTA codes (T4, T6, T9, T11) on a half-cap subset so both the FTA and SAR equivalents carry rows. No Adaption credits.
3. Add 3 truly-missing codes (T7 Abuse of Access, T8 Refusal to Cooperate, SAR_HUMAN_TRAFFICKING) via persona-event additions on unb_001, gig_001, itin_010 + 300 synthetic transactions narrated by Adaption.
4. Refill 51/52 empty narratives by resubmitting the original prompts.

Quick start

from datasets import load_dataset

repo = "Nachammai41/underserved-persona_conditioned-fraud-v4"  # TBD

ds       = load_dataset(repo, name="all")["train"]                  # 20,300 rows
remit    = load_dataset(repo, name="remittance")["train"]           #  5,000 rows
gig      = load_dataset(repo, name="gig_worker")["train"]           #  5,100 rows
unbanked = load_dataset(repo, name="unbanked")["train"]             #  5,100 rows
itin     = load_dataset(repo, name="itin")["train"]                 #  5,100 rows
personas = load_dataset(repo, name="personas")["train"]             #     46 rows
sources  = load_dataset(repo, name="sources")["train"]              #     13 rows
typology = load_dataset(repo, name="typology_registry")["train"]    #     25 rows

Dataset Statistics

Metric	Value
Total transactions	20,300
Per-archetype rows	remittance 5,000 / gig_worker 5,100 / unbanked 5,100 / itin 5,100
Total personas	46 (12/12/10/12)
Sources in registry	13 (7 PDFs + 1 data bundle + 5 links)
FinCEN typology codes	25 registered, all 25 exercised
Languages	20 tagged, 29 detected, 92.8% tag↔detect match
Fraud rate	~11.1% (2,263 of 20,300 rows)
Overall persona grade	A 6.3% / B 58.3% / C 22.9% / D 12.5%
Narrative fill rate	99.81% (39 empty rows of 20,300)
Prompt-tag leakage	0 (post-strip)

Narrative quality

Combined 20k Adaption fresh fill recovers row-level signals that an overlay approach would have lost:

Signal	v3	v4	Note
Corridor keyword	71.4%	68.4%	persona-level, stable
Platform name	42.7%	41.6%	persona-level, stable
Day of week	47.4%	43.6%	row-level, recovered
Instrument	37.7%	37.6%	row-level, recovered
Amount (rounded)	35.9%	27.4%	row-level
Amount (exact)	28.5%	19.1%	row-level
Hour class	35.9%	38.4%	row-level, recovered
Language tag↔detect	92.1%	92.8%	row-level, recovered

Archetypes

Archetype	Personas	Key Dimensions	Primary Sources
Remittance	12	corridor_country, transfer_service_loyalty, family_crisis_history, sender_tenure	Remitly 10-K, Menjívar 2022, Del Real 2022, Wise 2023, IAD 2026, FinCEN SAR Key Terms
Gig Worker	12	platform_mix, daily_cashout_pattern, device_stability, sim_history	Vallas & Schor 2020, FinCEN FTA 2024, Fed synthetic-ID, SAR ATO/BEC advisories
Unbanked	10	kiosk_location, prepaid_card_stack, income_source, documentation_status	FDIC 2023 HH Survey microdata, SAR_3PP / FTA T5 (v4 additions)
ITIN	12	business_type, tax_filing_history, credit_file_age, accountant_relationship	Menjívar 2022, Treasury OIG, IRS SOI, FinCEN SAR

Available Configs

Transaction data:

• all — 20,300 rows across all 4 archetypes
• remittance (5,000) / gig_worker (5,100) / unbanked (5,100) / itin (5,100)

Reference / attribution:

• personas — 46 persona profiles with grade, source IDs, family_crisis_history
• sources — 13-entry citation registry
• typology_registry — 25 FinCEN typology codes with applies_to_fraud_vectors

SFT companion (separate repo): see Nachammai41/underserved-persona_conditioned-fraud-v4-cot — 3,926 rows of chain-of-thought reasoning traces.

Schema

Same 25 columns as v3, plus three universal grounding columns (persona_source_ids, fraud_vector_typology_ref, behavioral_evidence_grade). See dataset_card.md for field-by-field descriptions.

V4 + v4.1 Typology Coverage Table (all 25 codes)

Code	Count	Source
SAR_ADVISORY_ELDER_FINANCIAL_EXPLOITATION	326	inherited from v3
FTA_IDENTITY_2024_T1 (General Fraud)	259	broad coverage
FTA_IDENTITY_2024_T10 (Scam)	207	redirected to SAR-specifics
SAR_ADVISORY_TAX_REFUND_FRAUD	205	inherited
FTA_IDENTITY_2024_T13 (Synthetic Identity)	197	inherited
SAR_ADVISORY_ACCOUNT_TAKEOVER_FRAUD	171	SAR-pref + gig_001/gig_011
FTA_IDENTITY_2024_T2 (False Records)	101	inherited
FTA_IDENTITY_2024_T6 (Account Takeover)	100	v4.1 half-cap re-stamp
SAR_ADVISORY_HUMAN_TRAFFICKING	100	v4.1 itin_010 wage_confiscation event
FTA_IDENTITY_2024_T8 (Refusal to Cooperate)	100	v4.1 gig_001 platform_refusal event
FTA_IDENTITY_2024_T7 (Abuse of Access)	100	v4.1 unb_001 POA_abuse event
FTA_IDENTITY_2024_T3 (Identity Theft)	95	inherited
FTA_IDENTITY_2024_T14 (Kiting)	86	inherited
FTA_IDENTITY_2024_T11 (BEC)	44	v4.1 half-cap re-stamp
SAR_ADVISORY_BEC_FRAUD	44	gig_004/gig_006 BEC events
SAR_ADVISORY_IVTS	29	rem_004 + rem_009 IVTS/hawala
SAR_ADVISORY_COVID19_IMPOSTER_SCAMS	17	rem_007 + rem_010
SAR_ADVISORY_THIRD_PARTY_PAYMENT_PROCESSORS	15	unb_003 unauthorized-ACH
FTA_IDENTITY_2024_T12 (False Claims)	13	gig_008 false-chargeback
SAR_ADVISORY_DISASTER_RELATED_FRAUD	13	rem_002 + rem_011 hurricane
SAR_ADVISORY_FUNNEL_ACCOUNT	11	rem_012
FTA_IDENTITY_2024_T4 (Third-Party ML)	8	v4.1 half-cap re-stamp
SAR_ADVISORY_CYBER_EVENTS	8	gig_012 phishing
FTA_IDENTITY_2024_T9 (Cyber Incident)	7	v4.1 half-cap re-stamp
FTA_IDENTITY_2024_T5 (Circumventing Standards)	7	unb_009 lax-KYC

Total fraud rows: 2,263. All 25 codes carry rows.

Companion: CoT reasoning dataset (separate repo)

A 3,926-row chain-of-thought dataset (1,963 fraud + 1,963 matched non-fraud) is published as a separate repo to keep this one lean:

Nachammai41/underserved-persona_conditioned-fraud-v4-cot

Each row pairs a v4 transaction with a step-by-step reasoning trace produced by Adaption's reasoning_traces recipe (graded E → A, +92%). Use it for SFT, LLM-as-judge distillation, or CoT data augmentation. Rows join back to this dataset's all config on data_uuid.

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 (combined 20k v4 job + v4.1 patch + CoT job)
• 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