Johnny Ayers’ Post

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CEO and Founder @ Socure | Ernst & Young Entrepreneur of the Year | Building the Most Accurate and Inclusive ID Verification Company in the World

Let’s geek out on name matching algorithms in the midst of a world full consent orders and burned out compliance teams.  An example to illustrate: There are lots of people named Jose Lopez or John Smith in the world, so when you are trying to automate names to watchlists, common names can yield a lot of complexity. Socure uses many novel approaches for name matching algorithms.  One example is the use of Siamese Neural Networks with Long Short-Term Memory (LSTM) layers. These models are designed to learn and compare sequential data, such as text or time series, and are particularly useful for tasks like name matching, sentiment analysis, or speech recognition, where the order of the input sequence matters. We use Siamese LSTM models because they are: 1️⃣ Well-suited for name matching from their ability to capture and learn the sequential nature of names. 2️⃣ Able to effectively capture long-term dependencies and contextual information within names, such as the order of the characters or the presence of specific patterns. 3️⃣ Enabled to learn robust representations for names and accurately compare them, even when dealing with variations, misspellings, or different name orders. This is one of thousands of examples of what innovation looks like in an industry that has not seen innovation in far too long.  Joshua Linn, Debra Geister, and Ganesh Kumar- we appreciate your leadership and dedication for always innovating.

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Aidan Herbert

Decentralized transactional ecosystem enabler

1w

Name matching == Identity theft's best friend. GDPR Article 5 snoozes while data brokers play fast and loose with your digital doppelgänger. ▶ Pro tip: Sprinkle your forms with zany pronouns. Confuse the algorithms, confound the traffickers of human identity. P.S. Data 'science' is about as scientific as astrology."

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Cori Shen

VP, Data & AI Leader: Innovate in FraudPrevention|DigitalIdentity|CustomerLoyalty |ConsumerInsights|Payments|eCommerce|Healthcare. Patents for Personalization|SyntheticIdentity|FirstPartyFraud|Refund/FriendlyFraud|GenAI

2w

Thanks for sharing! You are right, name matching is not just name matching or fuzzy match. Name matching for KYC should always take context and relations into consideration. Glad to see Socure use deep learning method to tackle the problem. Recently I explored two more techniques on the same topic: transformers with ensemble. Use "chargpt" to do heavy lifting context learning. Both are promising - just an FYI.

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