Explainable Fuzzy Matching - Typo Error Statistics

<p><strong>Explainable Fuzzy Matching on Payee Data</strong></p><p>This workflow demonstrates how to use <strong>Approximate String Matching</strong> to reconcile noisy, user-entered payee names with a clean reference list of canonical entities. Beyond generating similarity scores, the workflow provides <strong>explainable error statistics</strong> to highlight where and how mismatches occur.</p><p>🔹 Steps in the Workflow</p><ol><li><p><strong>📂 Load Data</strong></p><ul><li><p>Reference Data: clean list of canonical payee names.</p></li><li><p>Payee Data with Typos: noisy, real-world names entered by users.</p></li></ul></li><li><p><strong>🔍 Approximate String Matching (Levenshtein)</strong></p><ul><li><p>Matches each entered payee name against the reference list.</p></li><li><p>Produces a <strong>Match Sequence</strong> (e.g., oooo=ooo=ox=+) that explains differences character by character:</p><ul><li><p>o → match</p></li><li><p>= → substitution (wrong character)</p></li><li><p>+ → insertion (extra character)</p></li><li><p>x → deletion (missing character)</p></li></ul></li></ul></li><li><p><strong>🧮 Error Type Analysis</strong></p><ul><li><p>Counts substitutions, insertions, deletions, and matches.</p></li><li><p>Calculates error ratios, edit distance, and match accuracy.</p></li><li><p>Provides <strong>explainable quality metrics</strong> for each match.</p></li></ul></li><li><p><strong>📊 Aggregation &amp; Statistics</strong></p><ul><li><p>Groups results by reference payee.</p></li><li><p>Computes the <strong>average error profile per entity</strong> (e.g., “Deutsche Bank AG entries often miss characters”).</p></li><li><p>Rounds and formats values for readability.</p></li></ul></li><li><p><strong>📈 Interactive Dashboard</strong></p><ul><li><p>Table of canonical payees with their <strong>average match accuracy</strong>.</p></li><li><p>Bar chart showing the <strong>distribution of error types</strong> (substitution, insertion, deletion).</p></li><li><p>Clear insights into where manual review may be needed and which vendors/customers are most error-prone.</p></li></ul></li></ol><p>🔹 Business Value</p><ul><li><p><strong>Data Quality Monitoring</strong> → Understand how user-entered names deviate from reference data.</p></li><li><p><strong>Explainable Matching</strong> → Not just similarity scores, but insights into <em>why</em> mismatches occur.</p></li><li><p><strong>Operational Efficiency</strong> → Identify entities requiring frequent manual corrections.</p></li><li><p><strong>Compliance Support</strong> → Improve accuracy for KYC, AML, and financial reconciliation tasks.</p></li></ul>