Nachammai41/itin-fraud-narratives

This dataset is a remastered version prepared using Adaption's Adaptive Data platform.

itinfraudnarratives

This dataset contains prompt templates designed to generate first-person narratives about financial fraud targeting ITIN holders. Each entry specifies variables such as fraud vector, financial instrument, transaction amount, and language to guide the creation of synthetic victim stories. The samples focus on scenarios involving identity theft, tax fraud, and synthetic identity crimes within immigrant communities. All provided completions in the sample are null, indicating this is a prompt-only collection for data generation tasks.

Dataset size

There are 3,317 data points in this dataset. This is an instruction tuning dataset.

Quality of Remastered Dataset

The final quality is A, with a relative quality improvement of 88.0%.

Domain

• —Writing-editing-communication (92%)
• —Legal (8%)

Language

• —English (100%)

Tone

• —Anecdotal (100%)

Evaluation Results

• —Quality Gains: <img src="https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/5e77a1cd-d5dc-43f1-831e-dba8fa5ccfd6.png" alt="QualityGains" style="max-width: 50%; display: block; margin-left: auto; margin-right: auto;" />

• —Grade Improvement: <img src="https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/e929a7f0-5f3a-4bc0-a82d-a1c006fed992.png" alt="Grade" style="max-width: 50%; display: block; margin-left: auto; margin-right: auto;" />

• —Percentile Chart: <img src="https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/9c118d30-632b-4669-9193-504fcf39d3bf.png" alt="Percentile Chart" style="max-width: 50%; display: block; margin-left: auto; margin-right: auto;" />

Underserved Financial Fraud Dataset

Synthetic fraud detection data for underrepresented_communities

Created with Adaptive Data by Adaption | CC BY 4.0 | 5 languages

What This Is

A synthetic financial fraud dataset covering four underserved community archetypes — populations that rely on remittance transfers, gig economy payouts, prepaid cards, and ITIN-based transactions. These communities are disproportionately targeted by fraud, yet no open-source fraud dataset has ever modeled their financial behavior.

This dataset fills that gap.

The Four Archetypes

Languages

en English &nbsp;|&nbsp; es Spanish &nbsp;|&nbsp; hi Hinglish &nbsp;|&nbsp; ht Haitian Creole &nbsp;|&nbsp; yo Yoruba &nbsp;|&nbsp; vi Vietnamese &nbsp;|&nbsp; ta Tamil &nbsp;|&nbsp; ta-en Tamil-English

What Makes It Different

No existing fraud dataset covers this population. PaySim simulates generic mobile money. Sparkov models middle-class credit cards. IEEE-CIS captures e-commerce. None remittance kiosks, gig payouts, or ITIN-linked accounts.

Generated with diffusion, not rules. Tabular data generated using Tab-DDPM (denoising diffusion for tabular data) — learns joint correlations across behavioral features, not just independent column sampling. Trained on A100 GPU via Google Colab Pro.

Multilingual narrative text. Every fraud transaction has a narrative_text field — the scam message or fraud description in the community's language. Generated by Adaptive Data by Adaption. Quality score improved from E (5.0) to A (9.2–9.4).

Reasoning traces. 390 chain-of-thought fraud analysis examples — step-by-step investigator reasoning grounded in community-specific fraud signals. No existing fraud dataset includes this. Built for fine-tuning financial language models (FinBERT, Gemma).

How It Was Built

1. Scrape      1,040 real fraud narratives from CFPB, BBB Scam Tracker,
               and Reddit archive (Pullpush.io)
 
2. Profile     Behavioral distributions per archetype derived from
               scraped narratives — amounts, channels, corridors,
               fraud vectors, language mix
 
3. Generate    Tab-DDPM trains on 5,000 seed rows per archetype,
               learns joint feature correlations, generates 5,000
               synthetic transactions per archetype
 
4. Narrate     Adaptive Data by Adaption fills narrative_text in
               8 languages per transaction's fraud context
 
5. Trace       390 reasoning traces generated — chain-of-thought
               fraud analysis for fine-tuning use

Schema (Key Fields)

Intended Use

• —Training fraud detection models on underserved community transaction patterns
• —Benchmarking existing models (IEEE-CIS trained) against this population
• —Fine-tuning financial language models on multilingual fraud narratives
• —Research into AI fairness and financial inclusion
• —NLP research on under-resourced financial language

What This Is Not

This is a fully synthetic dataset. No real transaction data. No PII. Behavioral distributions are informed by public fraud narratives and World Bank remittance corridor data — not empirically measured transaction logs. Like all synthetic fraud datasets (PaySim, Sparkov, Cifer-AF), ground truth validation against real data is not possible due to privacy constraints.

Origin

This dataset was created as part of the Uncharted Data Challenge by Adaption Labs (April 2026). It extends the Fraud Detection Framework — an Agentic RAG pipeline with a custom Financial SLM built on the IEEE-CIS dataset (AUC-ROC 0.9486). The underserved dataset enables direct benchmarking: how does a model trained on mainstream data perform on populations it has never seen?

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}
}

Credits

• —Adaptive Data by Adaption — Narrative generation and dataset enrichment
• —Tab-DDPM (Kotelnikov et al., 2022) — Tabular diffusion model
• —CFPB — Consumer Financial Protection Bureau public complaint database
• —BBB Scam Tracker — Better Business Bureau public scam reports
• —Pullpush.io — Reddit archive API

License: CC BY 4.0 — Free to use with attribution