Entity Resolution

What is entity resolution?

Entity resolution is the process of identifying and linking multiple references to the same entity, such as a person, organization, or company, across different data sources and systems. In the context of financial crime, this involves disambiguating and consolidating data to create a single, accurate, and comprehensive view of an entity, enabling effective risk management, compliance, and decision-making.

Why entity resolution is important

Entity resolution is crucial in financial crime compliance, as it helps identify and mitigate risks associated with money laundering, terrorist financing, and other illicit activities. Entity resolution enables organizations to:

Enjoy accurate data analysis – ensuring that data is accurate and free from duplication is essential for reliable analysis.

Identify high-risk customers and counterparties – accurately highlighting customers and associated potential risks keeps the bank compliant and ensures that the organization is less likely to suffer financially were something to go wrong.

Prevent fraudulent activities – matching entities accurately means payment fraud and other fraudulent activities are less probable.

Comply with regulatory requirements (e.g., Know Your Customer (KYC), Anti-Money Laundering (AML)) – organizations can help meet regulatory requirements by maintaining consistent and accurate records.

Improve customer due diligence and ongoing monitoring – by improving the bank’s processes, the organization can remain vigilant to potential pitfalls and mitigate the risks associated with money laundering and terrorist financing.

How entity resolution works

Entity resolution typically involve the following steps:

Data collection: Gathering data from various sources (databases, documents, etc.)

Data cleaning: Removing inconsistencies and inaccuracies.

Feature extraction: Identifying key attributes (e.g., names, addresses, transaction IDs) that will be used for matching.

Entity matching: Comparing and linking records using algorithms to determine if they refer to the same entity.

Entity consolidation: Consolidating matched entries into a single, coherent record.

Entity verification: Validating the accuracy of the resolved entity information.

Primary use cases of entity resolution

Entity resolution is commonly used in the following cases:

Customer Due Diligence (CDD): Ensuring accurate customer records by linking various pieces of customer information.

Fraud Detection: Identifying suspicious activities by linking related transactions and entities.

Sanctions Screening: Matching customers and transactions against sanction lists to ensure compliance.

Anti-Money Laundering (AML): Consolidating data to create accurate risk profiles and detect illicit activities.

Using entity resolution effectively also has a knock-on positive effect for financial organizations when it comes to customer risk management and compliance and regulatory reporting.

What methods and models are used in entity resolution?

Several methods and models are employed in entity resolution. These include:

Rule-Based Matching: Using predefined rules and criteria for matching entities.

Probabilistic Matching: Utilizing statistical techniques to estimate the likelihood that different records refer to the same entity.

Machine Learning: Applying algorithms that learn from data to improve matching accuracy over time.

Hybrid Models: Combining multiple techniques to enhance the reliability and precision of the resolution process.

Benefits of entity resolution

There are many benefits of entity resolution. These include:

Improved data quality: Ensures that data is accurate, consistent, and comprehensive leading to better decision making.

Enhanced compliance: Facilitates meeting regulatory requirements by maintaining clean and precise records.

Operational efficiency and accuracy: Entity identification improves leading to a reduction in time and resources spent on manual data matching and cleaning.

Enhanced KYC and CDD: Better KYC and CDD processes allow for improved identification and mitigation of fraudulent activities.

Improved risk management: Provides a more accurate risk assessment by consolidating fragmented data and reducing false positives.

Cost and productivity savings: With better data quality, there are significant cost savings as teams will be more productive by focusing on genuine risk.

Challenges in entity resolution

Entity resolution is not without its difficulties. Challenges include:

Data quality issues: Inconsistent, incomplete, or inaccurate data can hinder the entity resolution process.

Scalability: Resolving entities in massive datasets can be computationally intensive, especially when bringing together data on a global scale.

Data volume and complexity: Large global datasets often come with variations in data formats, languages, and naming conventions, which can complicate matching processes.

Entity ambiguity: Ambiguous data makes for a difficult resolution process. This can be exacerbated by homophones (words that look the same but have differing meanings across languages).

Privacy Concerns: Handling sensitive data while ensuring privacy and compliance with regulations.

Precision and recall: Balancing precision (an ability to correctly identify matches) with recall (the ability to find all true matches) requires constant tweaking to find the best model for an organization. Essential for developing an effective entity resolution model, and an ideal approach would aim to maximize both metrics.

Precision	Recall	Description	Example
High	High	The model is very accurate in identifying true entities (precision) and can find most of the actual entities (recall). This is ideal but often difficult to achieve. It means the model correctly identifies almost all relevant entities with very few false positives or false negatives.	The model finds almost all duplicate records in a customer database, and nearly all of its matches are correct.
High	Low	The model is very accurate in identifying true entities (high precision), but it misses a lot of actual entities (low recall). This means it correctly identifies the entities it does detect, but it fails to find many of them, leading to many missed matches.	The model may identify customer duplicates only when the names and addresses exactly match, missing duplicates with slight variations.
Low	High	The model identifies most of the actual entities (high recall), but it also includes a lot of incorrect entities (low precision). This means it doesn’t miss many true entities, but it also makes many mistakes, resulting in many false positives.	The model might consider any similar sounding name as a duplicate, thereby catching almost all duplicates but also matching non-duplicates.
Low	Low	The model is not very accurate in identifying true entities (low precision) and misses a lot of actual entities as well (low recall). This is the least desirable outcome, as it means the model performs poorly, generating many false positives and false negatives.	The model might be inconsistent and inaccurate in its matching criteria, leading to an ineffective entity resolution process.

Best practice in entity resolution

Effective entity resolution is only as good as the team and technology behind it. As such, best practice in entity resolution includes:

High-quality data collection: Prior to integration, ensure data is high-quality, accurate, and complete. Reliability of the data being used is paramount.

Regular updates and refinement: Continuously update entity resolution models and algorithms to handle new data and emerging risks.

Use of advanced technologies: Employ state-of-the-art machine learning and AI techniques to improve matching accuracy.

Comprehensive Training: Equip compliance and risk management teams with the necessary skills and knowledge to excel.

Audit and Validation: Regularly audit and validate the resolution process to ensure accuracy and compliance.

Entity resolution is a vital process for financial institutions aiming to combat financial crime effectively. By using best practice and advanced methods, organizations can ensure accurate data, regulatory compliance, and robust risk management.

Introducing entity resolution from SymphonyAI

SymphonyAI offers an entity resolution tool to make everything easier. Perfect for AML, payment fraud, KYC/CDD, and sanctions screening, entity resolution allows financial institutions to transform their risk and compliance ecosystem. 

Available out of the box as part of SymphonyAI’s financial crime prevention software for AML transaction monitoring, screening, and CDD processes, entity resolution allows businesses to resolve disconnected data. Not only does it allow for more effective investigations, but it also helps identify duplicate records and expose hidden risk. 

Reconciling data hidden in siloed systems for a unified view of customers, entity resolution allows for the identification of shared contact details, easily allowing investigators to recognize how different entities relate to one another, or how they may even be the same person. 

Visualize customer relationships and follow the money to uncover concealed relationships and expose criminal networks, improving a financial institution’s ability to remain compliant. 

Want to know more? Visit the entity resolution page. 