README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - text-generation
language:
  - en
tags:
  - synthetic
  - B2B
  - finance
  - privacy-safe
size_categories:
  - n<1K
pretty_name: Aml_Transaction_Anomalies

Aml_Transaction_Anomalies (Synthetic B2B Dataset Preview)

Add me on Discord: xomohappy for access support, delivery questions, or product questions about this premade commercial dataset.

This is a premium, privacy-compliant, industry-safe synthetic dataset simulating Anti-Money Laundering (AML) suspicious logs for B2B applications.

About this Dataset

This dataset is generated programmatically using large language models combined with a strict data curation and validation layer.

• Privacy-safe: Contains synthetic names, phone numbers, and company identifiers.
• Structured and verified: Schema-validated, formatted, and deduplicated to reduce model overfitting risk.
• Target AI use case: FinTech compliance teams and fraud detection developers.
• Category: Finance

Schema Definition

Each record contains the following fields:

• transaction_id (str): Pre-assigned Transaction ID (e.g. TXN-AML-XXXX)
• account_holder (str): Pre-assigned Account Holder Name
• account_type (str): Personal Savings, Corporate Checking, or Trust Account
• transaction_pattern (str): Description of transaction amounts, frequencies, and locations
• compliance_flag (str): Suspicious Activity Report (SAR) Flag, Enhanced Due Diligence (EDD), or Cleared
• investigator_rationale (str): Detailed analysis of layering, structuring, or velocity indicators

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Get the Commercial Version

Need a larger dataset for production fine-tuning? The matching private repo is HaseebDev/aml_transaction_anomalies-commercial and is available under a commercial license.

• Payment: Lemon Squeezy hosted checkout.
• Delivery: Lemon Squeezy checkout with secure commercial delivery after purchase
• Typical commercial package: 10,000+ records, schema documentation, and quality audit report.

Open the commercial access page

Quality Audit

Dataset Quality Report

Executive Summary

Metric	Value
Status	PASS
Quality Score	94/100
Records Audited	100
Niche	Anti-Money Laundering (AML) suspicious logs
Category	Finance

Quality Gates

Dimension	Result
Schema Conformance	100.0% (100/100)
Duplicate Rows	0
Duplicate Identity Values	0
Non-Synthetic Phone Risk	0
Email Address Count	0
Max Fuzzy Similarity	0.404
Average Words Per Descriptive Field	143.76
Vocabulary Diversity	0.872
Repeated Phrase Count	803
Domain Keyword Coverage	0.533

Buyer Assurance

This dataset was checked with deterministic local tooling before publication. The audit verifies schema consistency, uniqueness, text depth, duplicate risk, repeated wording, domain terminology coverage, and obvious PII leakage patterns.

Findings

• MINOR: 803 repeated four-word phrase pattern(s) detected.