We’re launching the Yanez Multimodal Inorganic Identities Dataset (MIID) Subnet, a test network designed to generate high-quality inorganic identities for AI research, fraud prevention, and compliance testing.

This subnet is built to power Yanez Compliance, an AI-powered platform for finding weaknesses in financial crime prevention systems. We are enhancing the ability of financial institutions to test, validate, and improve their identity verification and fraud detection systems.

Why Inorganic Identities?

Identity verification and fraud detection require accurate, diverse, and privacy-safe data. However, real-world datasets are limited, biased, and carry privacy risks. Inorganic identities solve this by providing realistic but artificial profiles to power:

Anti-Fraud & Financial Crime Prevention – Financial institutions and compliance teams can test detection models with privacy-safe identity datasets.
Identity Matching & Sanctions Screening – AI-driven compliance tools can improve accuracy across diverse name structures and transliterations.
AI & Machine Learning Training – Researchers can enhance identity recognition models without exposing real identities.
Privacy-Preserving Compliance & Security – Organizations can validate identity-related AI models without handling sensitive data.

The Yanez subnet is an integral part of Yanez Compliance, a business actively serving clients in financial crime prevention.

What is Yanez Compliance?

Yanez Compliance is an AI-powered platform that helps financial institutions detect and correct vulnerabilities in their financial crime prevention systems. A key challenge in financial crime prevention is ensuring that compliance tools work accurately across diverse identities, name structures, and transliterations. False positives and false negatives in name matching can lead to costly inefficiencies, regulatory risks, and missed threats.

This is where the Yanez subnet comes in.

How the Yanez Subnet Strengthens Financial Crime Prevention

Benchmark and fine-tune name-matching algorithms with diverse identity datasets.
Improve sanctions screening accuracy across different transliterations and spelling variations.
Test fraud detection models with privacy-preserving identity datasets.
Reduce bias in AI-driven compliance tools by introducing global name diversity.

A Real Business Use Case in the Bittensor Ecosystem

The Yanez subnet solves a defined market problem with existing demand.

Real industry adoption – Our technology is already in use by financial institutions.
Clear business application – The subnet provides direct value to compliance teams.
Market-driven innovation – We are bringing decentralized AI to an enterprise-level challenge.

This subnet is an opportunity for Bittensor miners and AI researchers to shape the future of decentralized AI in financial security and compliance.

Why Join?

The Yanez subnet is building the largest decentralized inorganic identity dataset, and you can be part of it.

Advance AI-driven fraud prevention
Strengthen compliance tools for sanctions screening & KYC
Support privacy-preserving identity research
Build a decentralized identity dataset that powers real financial security applications.
Bring Bittensor closer to enterprise adoption by demonstrating real-world AI use cases.

Strengthening Bittensor Subnets: My Experience with Yanez MIID and Bitsec.ai