Idea
Model delivering high-accuracy person name matching for complex multilingual identity data at scale.
Research Paper
Core Innovation
This paper introduces SGER, a curriculum-guided two-phase fine-tuning approach that first trains LLMs to parse name structure and semantics, then optimizes for binary entity matching. This method significantly improves accuracy over few-shot prompting and single-stage fine-tuning, especially in linguistically complex and noisy datasets.
Why It Matters
Accurate entity resolution is critical for identity verification and compliance in diverse linguistic contexts, reducing errors from inconsistent naming and transliteration. This solution improves operational efficiency and user experience for platforms handling large-scale, noisy identity data. It scales to real-world multilingual systems, enabling reliable customer unification and regulatory compliance.
Market Size (TAM)
$2–10B TAM for identity resolution and KYC compliance solutions; $500M–$1B SAM from financial services, digital platforms, and government agencies. Driven by regulatory compliance and digital identity verification adoption.
Potential Customers & Pain Points
- Financial institutions – Struggle with KYC compliance due to inconsistent identity data
- Large digital platforms – Need to unify user identities across heterogeneous records
- Government agencies – Require accurate citizen data matching in multilingual environments
- Identity verification providers – Face challenges with noisy and culturally diverse name data.
Business Model
SaaS platform offering API access for entity resolution services with tiered pricing based on volume and customization; enterprise licensing for large-scale deployments.
Competitive Landscape
- IBM InfoSphere
- Senzing
- Microsoft Azure Cognitive Services
- Google Cloud Data Fusion
Implementation Challenges
- Integration complexity with existing legacy systems
- Handling extreme linguistic and cultural name variations beyond training data
- Data privacy and compliance constraints in identity data processing
Validation Strategy
- Pilot deployments with financial institutions for KYC workflows
- Benchmarking against industry-standard datasets and competitor solutions
- Scaling tests on multilingual datasets from diverse regions
- User feedback and accuracy monitoring in production environments
Research Paper Overview
Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts
Summary
This paper presents Structure-Guided Entity Resolution (SGER), a two-phase fine-tuning framework for large language models to improve person name matching in linguistically diverse and noisy datasets. Evaluated on Indian identity data, SGER achieves 99.02% accuracy and outperforms GPT-4o few-shot and single-stage baselines. The system is deployed at Dream11, serving over 250 million users, demonstrating scalable, high-precision entity resolution in multilingual real-world environments.