How to Turn Compliance into a Competitive Advantage

The Power of Machine Learning for Better Compliance Outcomes

New Federal Plans for Artificial Intelligence Must Be Paired With Digital Identity Implementation

Top Bank Rooted Out Money Mules & Synthetics from Accounts

Uncovering Hidden Threats: Why Auditing Your Portfolio for Money Mules is Critical

Expanding Coverage and Improving Precision with the Latest Advances in Deceased and Address Data

Socure Solution Insights: Expanding Coverage and Improving Precision with the Latest Advances in Deceased and Address Data

<p>In today&#8217;s complex regulatory landscape, organizations face the daunting challenge of ensuring compliance with evolving regulations and increased scrutiny on operations from regulators. One crucial aspect organizations must get right? Accurately verifying the customer in question, and ensuring they are not included on any sanctions enforcement lists.</p>
<p>FinCEN revealed some shocking statistics at the 2023 FedID conference. In their most recent analysis of suspicious activity reports (SARs), they found that of 3.2 million total SARs that were filed, approximately 1.6 million — or 42% — were actually deficiencies in identity verification.</p>
<p>Reliance on basic name matching techniques for CIP/KYC and sanctions compliance is a key driver of these high false positive rates and leads to significant problems. Investigators waste time chasing irrelevant alerts while organizations face legal and reputational risks from potential enforcement actions.</p>
<p>Traditional legacy approaches leverage algorithms such as Levenshtein distance or Jaro-Winkler that are error-prone, and insufficient to handle the complexity and scale of modern compliance requirements.</p>
<p>This is where machine learning algorithms excel at solving real problems for compliance professionals.</p>
<h2>Using AI to Boost Name Matching Performance</h2>
<p>Machine learning algorithms excel at handling vast amounts of data and identifying intricate patterns that may go unnoticed by traditional methods. By training models on extensive datasets, machine learning algorithms can learn from historical data and adapt their matching techniques based on the underlying patterns. As a result, the accuracy of name matching improves significantly, reducing false positives and false negatives. This increased accuracy reduces the risk of compliance breaches and helps organizations maintain a robust and reliable compliance framework.</p>
<h3>Enhanced Compliance Efficiency and Scalability</h3>
<p>Machine learning algorithms automate the name matching process, decreasing the need for error-prone and time consuming manual intervention. With the ability to process vast amounts of data at scale, these algorithms provide substantial time and cost savings for compliance teams. Furthermore, machine learning algorithms can handle increasing volumes of data as organizations grow, ensuring scalability without compromising accuracy or efficiency.</p>
<h3>Adaptability to Dynamic Regulations</h3>
<p>Regulations and compliance requirements are not static; they evolve over time. Machine learning algorithms offer the advantage of adaptability, allowing compliance products to stay up-to-date with changing regulations. By regularly retraining algorithms on updated datasets, compliance products can continuously learn and adjust their matching techniques, ensuring compliance with the latest regulatory guidelines. This adaptability eliminates the need for manual updates, streamlining the compliance process and reducing the risk of non-compliance.</p>
<h3>Intelligent Risk Assessment</h3>
<p>Incorporating additional data points and risk indicators helps compliance teams go beyond basic name matching by considering contextual information, such as addresses, transaction history, and relationship networks to build a comprehensive risk profile for each entity, and distinguish between individuals with similar names. Leveraging this additional intelligence can mean making a more complete risk assessment. Organizations can prioritize their compliance efforts, focusing on the most high-risk entities and minimizing the false positives that often arise from traditional broad-matching methods to ensure no one slips through without detection.</p>
<h3>Continuous Improvement</h3>
<p>Through analysis, feedback of known good and bad outcomes and incorporating new datasets, machine learning algorithms can refine their matching techniques, further enhancing accuracy and reducing false positives. Continuous improvement allows compliance teams to stay ahead of emerging compliance risks and challenges, ensuring long-term effectiveness and adaptability.</p>
<h2>Socure’s Approach</h2>
<p>Socure uses a novel approach for name matching algorithms using <a href="https://machinelearningmastery.com/gentle-introduction-long-short-term-memory-networks-experts/">Siamese Neural Networks with Long Short-Term Memory (LSTM) layers</a>. 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.</p>
<p>Socure uses Siamese LSTM models because they are well-suited for name matching from their ability to capture and learn the sequential nature of names. These models can effectively capture long-term dependencies and contextual information within names, such as the order of the characters or the presence of specific patterns. This enables the models to learn robust representations for names and accurately compare them, even when dealing with variations, misspellings, or different name orders.</p>
<h2>Socure’s Models vs. Traditional Legacy Approaches</h2>
<p>Socure’s proprietary models outperform long-held industry standard techniques and perform precise matching, even in the presence of variations, misspellings, or different name orders. The result is unparalleled accuracy and reliability in compliance, achieving near 98% AUC, compared to Damerau-Levenshtein at 91.5% and Jaro-Winkler at 92.6%.</p>
<p><img loading="lazy" decoding="async" class="alignnone wp-image-9258 " title="Socure’s Models vs. Traditional Legacy Approaches" src="https://www.socure.com/wp-content/uploads/2023/11/unnamed-1-300x224.png" alt="Socure’s Models vs. Traditional Legacy Approaches" width="474" height="354" srcset="https://media.socure.com/app/uploads/2023/11/unnamed-1-300x224.png 300w, https://media.socure.com/app/uploads/2023/11/unnamed-1.png 512w" sizes="auto, (max-width: 474px) 100vw, 474px" /></p>
<p>This increase in accuracy represents insurance against compliance risk and enforcement action.</p>
<p>Below are some examples of pairs of names where the traditional methods struggle but the LSTM model can resolve to match or unmatch. Damerau-Levenshtein and Jaro-Winkler scores range from 0 to 1 whereas LSTM Score ranges from 0 to 100, where a score above 70 is considered as a strong match and a score of 85 is a very strong match.</p>
<table dir="ltr" style="height: 264px;" border="1" width="892" cellspacing="0" cellpadding="0">
<colgroup>
<col width="100" />
<col width="100" />
<col width="100" />
<col width="100" />
<col width="100" />
<col width="100" /></colgroup>
<tbody>
<tr>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Input Name&quot;}"><strong>Input Name</strong></td>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Matched Name&quot;}"><strong>Matched Name</strong></td>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Jaro Winkler&quot;}"><strong>Jaro Winkler</strong></td>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Damerau-Levenshtein&quot;}"><strong>Damerau-Levenshtein</strong></td>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Socure&quot;}"><strong>Socure</strong></td>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Target&quot;}"><strong>Target</strong></td>
</tr>
<tr>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Mark Wright&quot;}">Mark Wright</td>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Mary Wright&quot;}">Mary Wright</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:0.958}">0.958</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:0.909}">0.909</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:15.1}">15.1</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:0}">0</td>
</tr>
<tr>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Dustin Kiley&quot;}">Dustin Kiley</td>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Austin Riley&quot;}">Austin Riley</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:0.889}">0.889</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:0.833}">0.833</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:11.2}">11.2</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:0}">0</td>
</tr>
<tr>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Jimmy Jayes&quot;}">Jimmy Jayes</td>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Timmy Mayes&quot;}">Timmy Mayes</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:0.879}">0.879</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:0.818}">0.818</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:8.7}">8.7</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:0}">0</td>
</tr>
<tr>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Robert Jenkins&quot;}">Robert Jenkins</td>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Bobbie Jenkins&quot;}">Bobbie Jenkins</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:0.799}">0.799</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:0.588}">0.588</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:88.5}">88.5</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:1}">1</td>
</tr>
<tr>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Elizabeth Williams-Cady&quot;}">Elizabeth Williams-Cady</td>
<td data-sheets-value="{&quot;1&quot;:2,&quot;2&quot;:&quot;Lizzy Cady&quot;}">Lizzy Cady</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:0.45}">0.45</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:0.35}">0.35</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:78.6}">78.6</td>
<td data-sheets-value="{&quot;1&quot;:3,&quot;3&quot;:1}">1</td>
</tr>
</tbody>
</table>
<p>&nbsp;</p>
<p>In addition to matching nicknames and phonetic matches accurately, the model can distinguish between male and female names and completely different individuals, even if there is a single letter variation in the name components. Further, the model can easily match and distinguish matches that include cultural variations, historical names, or rare name patterns.</p>
<p>Unconventional or rare names can pose challenges for distancing techniques or word embeddings as they may fail to capture the semantic meaning and context behind such names, leading to inaccurate matches.</p>
<p>Traditional approaches typically focus solely on character-level differences and not capture the semantic meaning or unique characteristics of the name, resulting in a false positive.</p>
<p>Socure&#8217;s models excel at comprehending the patterns and contextual cues associated with rare names, ensuring precise matching and reducing false positives or false negatives.</p>
<h3>Industry-First Entity Matching</h3>
<p>Our approach doesn’t rely solely on name matching. Instead, we take it a step further by incorporating additional attributes such as date of birth, address, city, state, country and national ID. By considering multiple attributes, Socure’s models generate an entity score that dynamically matches complete entity profiles.</p>
<p>This comprehensive risk assessment helps ensure that businesses are equipped to make informed decisions through accurate and context-aware matching. This streamlined process significantly decreases the time required to review matches, improving operational efficiency for compliance teams.</p>
<h3>Moving Forward</h3>
<p>Machine learning algorithms provide a powerful solution for entity matching in compliance, offering improved accuracy, enhanced efficiency, and adaptability to dynamic regulations. By leveraging the capabilities of these algorithms, organizations can streamline their compliance processes, minimize risks, and stay in compliance with ever-changing regulatory requirements.</p>
<p>As technology advances and data volumes grow, integrating machine learning algorithms into compliance solutions is essential for organizations seeking to achieve robust and efficient compliance frameworks.</p>
<p>Socure is redefining industry best practices to achieve optimal results, minimizing false positives and false negatives to achieve the highest standard of compliance possible.</p>
<p><em><strong>Learn more about our industry-leading <a href="https://www.socure.com/solutions/compliance">compliance solutions here</a>.</strong></em></p>


Unmasking Sanctions Evasion: Trends, Challenges, and Solutions

The Power of Machine Learning for Better Compliance Outcomes

Related Posts

Matt Johnson