Entity resolution is the process of stitching together multiple records relating to the same individual or business in order to get a more concise view of risk on a given person or company. For entity resolution to work, unique entity records are standardized and displayed together in a single cluster instead of multiple, independent records.
Without proper consolidation, risk assessments become cumbersome, with analysts executing time-intensive processes to review red flags for onboarding or investigations. Banks say that as much as 85 percent of activities related to financial crime compliance and anti-money laundering remain administrative or non-analytical in nature. This includes the manual collection of data from some systems to import into others.
How Entity Resolution Increases Team Output
Whatever the risk appetite, entity resolution uses multiple data points to group records together. This results in two advantages:
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Analysts see fewer results when performing searches, and in many cases just one for the entity they searched, thanks to the AI-driven resolution of key identifiers.
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Risk events related to an entity are also resolved so that the same information from multiple sources is grouped together, which simplifies reviews.
How entity data is compacted for consumption is extremely important. An organization’s risk appetite will inform fine tuning, whether the goal is to return focused or broad results. Organizations with higher risk appetites may under-resolve data to include 100% exact matches only as part of due diligence processes. On the opposite end of the spectrum, highly scrutinized organizations often lean towards over-linking, including fuzzy matches when resolving entities.
Benefits of Entity Resolution
Here are a few ways that entity resolution can make compliance work more efficiently:
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Reduce Duplicate Records: Duplicate records for the same entity can proliferate within databases and systems, especially those that pull information from multiple external sources. This can lead to confusion about which record is the accurate one.
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Avoid Inaccurate Analytics: Without entity resolution, data analytics and reporting may be compromised. Analysis based on incomplete or inaccurate entity data can lead to flawed insights and poor decision-making.
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Manage Regulatory Compliance Risks: Failing to resolve entities accurately can lead to non-compliance and regulatory fines. Accurate customer identification is essential for Know Your Customer (KYC) and Anti-Money Laundering (AML) regulations.
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Stronger Fraud Detection and Prevention: Incomplete or inaccurate entity data can hinder fraud detection efforts. Fraudsters can exploit gaps in entity identification to engage in identity theft or financial fraud.
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Address Mergers and Acquisitions Challenges: When organizations undergo mergers or acquisitions, the lack of entity resolution can complicate the process of integrating customer, supplier, and partner data.
Automating data consolidation processes with the use of technology frees up risk teams to focus on analysis and escalation tasks that actually require human intervention.
Identifiers Used in Entity Resolution
There are many ways to resolve entity data. Each technology you consider may have a different mix of identifiers contributing to the resolution algorithm. A strong digital system will often combine multiple identifiers to ensure accuracy.
Some identifiers that can be used for entity resolution include:
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Government Identifiers: For individuals, this might include Social Security numbers or government-issued ID numbers. For companies, it could be registration numbers or tax identification numbers. Exact matching on these identifiers is a strong basis for linking and resolving entities.
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Name Variations: Names of individuals and organizations can vary significantly due to misspellings, abbreviations, or variations in formatting. Algorithms for fuzzy or phonetic matching are used to link records with similar but not identical names.
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Address Information: Addresses, including street addresses, postal codes and city names, can be used to link records, especially in cases where names may vary. Geospatial data may also be used for this purpose.
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Contact Information: Includes using email addresses, phone numbers and other contact information to establish connections between multiple entity records.
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Date of Birth/Incorporation: For individuals and companies, dates of birth and incorporation can be used to establish links, especially when other information is incomplete or inconsistent.
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Social Network and Relationship Data: Information about relationships between entities, such as family connections or business affiliations, can be used to create links between related records.
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Transaction Data: Data related to transactions, such as dates, amounts and transaction types, can be used to identify connections between entities involved in financial activities.
The Challenges of Unresolved Customer Risk Data
Below are three main issues commonly encountered by compliance teams that don't leverage entity resolution to streamline operations.
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Fragmented Data Streams
Multiple records for the same entity from various sources, each with their own format and structure, can make it practically impossible for banks and mid-market corporations to accurately assess customer-associated risks at scale. We’ve seen organizations grow compliance and risk teams of 5 to 50 and more, and still face the same issues with data management.
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Increased Manual Efforts
Consolidating multiple risk sources can equal a lot of noise in search results. The time saved from searching disparate sources is often reallocated to combing through comprehensive results.
Take for example a single search relating to a current customer; you’ll see unique records for sanctions, peps, adverse news and more from multiple databases. Your team may be looking through different records pointing to the exact same entity, or even the exact same risk, which can be cumbersome in reviews.
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Gaps in Internal Data
Challenging scenarios can arise when internal data is incomplete or “dirty”. For example, let’s say a client returns multiple Social Security numbers, what do you do? Imagine these types of fringe cases piling up, at scale.
Without resolving external risk data to provide deeper insights, gaps in internal data add to the manual workload of risk and compliance teams. Even with automated processes, he volume of alerts generated can lead to backlogs, while a lack of comprehensive data coverage can result in gaps in surveillance.
Final Thoughts on Entity Resolution
In a landscape where even the largest enterprises with extensive compliance resources are often penalized for violating regulatory requirements, no organization can afford to look the other way when it comes to taking a proactive and comprehensive approach to risk management.
Regulatory bodies and other stakeholders demand that organizations have necessary processes to ide ntify financial crimes in real time amidst frequently expanded and updated regulations, and that requires additional efficiencies.
Ensuring strong entity resolution in your current and future risk software is one way to stay ahead of regulatory requirements without overwhelming your team.
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