Babel Street Match is an AI-driven solution designed for accurate name screening and the reduction of false positives. Its patented two-pass approach uses advanced fuzzy name matching algorithms to quickly identify potential matches and then re-evaluate them using pre-trained AI. This ensures high-quality matching in various languages and scripts, accounting for linguistic, cultural, and contextual factors. The software offers real-time responses and is infinitely scalable, handling massive datasets efficiently. It supports cross-lingual matching for names of people, organizations, addresses, and dates, providing a match score for each comparison. Users can customize match parameters and benefit from explainable AI, understanding how confidence scores are derived. Babel Street Match integrates seamlessly into existing workflows, automating processes and reducing the need for manual review, thus increasing productivity and reducing costs. Its flexible licensing and deployment options include on-premises and cloud solutions, even supporting air-gapped systems. The software is available on AWS Marketplace with plugins for Amazon OpenSearch and Elasticsearch.

Features

Accurate Name Screening

• Ensures high-quality matching in native languages and reduces the total cost of ownership for name matching software.
  • Uses AI to intelligently adapt to novel names.
  • Reduces false positives, increasing productivity by automating workflows.
  • Provides real-time responses, even over massive datasets.

Cross-lingual Matching

• Works across different scripts and languages, considering linguistic, cultural, and contextual factors without compromising performance.
  • Matches names of people and organizations, as well as dates and addresses in 24+ languages.

Explainable AI and Consistent Match Scores

• Allows users to easily configure match parameters and understand how Match arrives at match confidence scores.

Flexible Integration

• Offers flexible integration options—on-premises and cloud—including air-gapped systems.