AML Analytics vs Quantexa Entity Resolution

AML Analytics has no write-up on Specifiction yet; its figures are above.

Quantexa Entity Resolution links ambiguous records across people, organizations and places to build fuller profiles. Its machine-learning models match data incrementally, using available information and, where useful, context from third-party data. Machine learning and natural-language processing prepare structured and unstructured data through cleansing, parsing, transliteration and synonym handling, with models tuned for each country. Low-code, schema-less ingestion automates data mapping and handles complex nested data. The product supports batch matching and real-time resolution at request time, including household and organization matching. Quantexa says it can scale to tens of billions of records and reports 99% matching accuracy. It can be deployed on premises or in a preferred cloud, and connects with analytics environments, data lakes and warehouses through APIs and libraries. Identified uses include financial crime, anti-money laundering, customer intelligence and fraud. Fine-grained security can tailor responses to use cases and access rights. The web, API and self-hosted product is paid, with pricing on request.