KYC Sanction Screening with Fuzzy Matching

<p><strong>🔎 KYC Sanction Screening with Fuzzy Matching</strong></p><p>This workflow demonstrates how to use <strong>approximate string matching</strong> to detect sanctioned individuals in a customer onboarding dataset. It simulates a typical <strong>KYC (Know Your Customer)</strong> compliance scenario where names may be <strong>misspelled, shortened, or altered</strong> to bypass screening.<br></p><p>🛠️ Steps in the Workflow</p><p><strong>Step 1: Loading Data 📂</strong></p><ul><li><p>Load two input tables:</p><ul><li><p>A <strong>reference list</strong> of sanctioned names (“Criminals”)</p></li><li><p>A <strong>comparison list</strong> of new customer names</p></li></ul></li><li><p>These serve as the <strong>basis for matching</strong>.</p></li></ul><p><strong>Step 2: Matching 🔗</strong></p><ul><li><p>Use the <strong>Approximate String Matcher</strong> (Levenshtein distance) to compare each customer name against the sanction list.</p></li><li><p>This step captures both <strong>exact matches</strong> (e.g., <em>Walter White</em>) and <strong>approximate matches</strong> (e.g., <em>Walther White</em>, <em>Jessy Pinkmann</em>).</p></li></ul><p><strong>Step 3: Labeling 🏷️</strong></p><ul><li><p>Apply a <strong>Rule Engine</strong> to classify results</p></li><li><p>Customers with names within <strong>4 edits</strong> of a sanctioned name are flagged for review.</p></li><li><p>Remaining customers are labeled <strong>Clear</strong>.<br><br></p></li></ul>