
AWS Entity Resolution introduces advanced fuzzy matching using Levenshtein, Cosine, and Soundex algorithms to help organizations resolve consumer records across fragmented and inconsistent datasets with improved accuracy.


<div>
<p>
AWS Entity Resolution introduces advanced fuzzy matching capabilities using sophisticated algorithms to help organizations resolve consumer records across fragmented datasets.
</p>
<ul>
<li>Uses Levenshtein Distance, Cosine Similarity, and Soundex algorithms for matching</li>
<li>Enables tolerance for variations and typos in records</li>
<li>Bridges gap between rule-based and machine learning matching techniques</li>
<li>Allows setting similarity and distance thresholds for record matching</li>
<li>Applicable across industries like advertising, retail, and financial services</li>
<li>Helps match and link customer records across multiple applications</li>
<li>Easy to implement without specialized entity resolution expertise</li>
</ul>
<p>
The feature enables more accurate record matching, improving customer verification, fraud detection, and marketing personalization.
</p>
</div>


Related articles