In recent years, regulators have tightened their requirements for tackling money laundering and terrorist financing. Fraudsters often use offshore accounts and complex corporate structures to circumvent the law and mask their criminal activities. But to outpace bad actors, organizations need to better understand the network surrounding suspicious business activity.
Identification of Ultimate Beneficiary Ownership (UBO) is a crucial element. Being able to identify the UBO drives compliance to regulatory requirements, and better risk assessment of the business in question.
Defining a single, holistic, and comprehensive view of clients with complex structures can be a challenge.
To tackle this problem, you need to have a comprehensive view of each of the businesses constituting the legal hierarchy. This can be achieved by leveraging innovative AI and Machine Learning (ML) technologies powered by entity resolution and network generation.
- Entity Resolution allows you to bring information on businesses together into one single data point and generates a likelihood to determine which identities are a match and which are not.
- Network generation connects business entities based on their ownership structure (among other factors). This also allows you to discover hidden links which can be challenging when looking at data in isolation.
There are many techniques that can help organizations explore and extract useful insights from complex legal hierarchies. These include better risk assessment of businesses that are unusually complex compared to competitors.
The power of representing data via networks lies in the fact that even differing networks can have similar structural properties, which is why using graph analytical approaches yield big business value. To start, you just need the right context.
Click here to access the full blog and learn more about:
- The Challenge Legal Hierarchies Pose for Businesses
- Defining a Single View of Complex, Corporate Structures
- Applying Network Analytics to Extract Insights
- Cycle Detection in Practice
- How Link Imputation Can Reveal Further Risk
- Representing Data Via Networks Starts with Context