4 Anti-Financial Crime Use Cases for Entity Risk Detection Technology

How would you react if you found out you were banking Walter White, the drug kingpin from Breaking Bad?

There are actual Walter Whites hiding in your customer portfolio, exploiting gaps in your systems to launder money. To cover your bank for the financial crime risk associated with these customers, you need the ability to identify them. 

That’s where Entity Risk Detection comes into play. This technology helps banks detect risk using the following methods:

1. Data Consolidation: Entity Risk Detection brings data from multiple sources together (e.g., account ID information, watchlist data, and corporate data)
2. Entity Resolution: Entity Risk Detection merges all the information about the same customer into a single Golden Profile. 
3. Risk Factor Identification: When Entity Risk Detection creates the Golden Profile, new risk factors can emerge. 
4. Network Analytics: Entity Risk Detection identifies and illustrates connections and relationships between the Golden Profiles, uncovering unseen risk information. 

FIs can employ Entity Risk Detection in many anti-financial crime scenarios. In this article, we’ll explore four particular use cases:

1. Identifying Entity Risk at Onboarding 
2. Identifying Connection to Higher-Risk Entity
3. Identifying Shell Company Risk
4. Identifying Account Opening Fraud Risk

Use Case 1: Identifying Entity Risk at Onboarding 

The Entity Risk Scenario

A bank has previously exited a customer for having a risk score above the acceptable threshold. The customer had multiple SARs filed against them during a 12-month period. Later, the bank onboards a new customer.

The Risk Detection Process

1. During onboarding of the new customer, the bank’s Entity Risk Detection system observes that the new customer shares phone number and address attributes with the exited customer
2. The system alerts an investigator to the potential association 
3. The Entity Risk Detection system calculates the risk rating of the new customer. The number of attributes shared by the new customer and the exited customer factor into the rating.
4. The investigator recommends that the bank does not proceed with onboarding the new customer to prevent the risk from re-entering the organization.

Use Case 2: Identifying Connection to Higher-Risk Entity

The Entity Risk Scenario 

A bank has an established customer with a low-risk rating. The customer then opens a new account for their car wash business. 

The Risk Detection Process

1. During a periodic check of the individual customer, Entity Risk Detection observes that the customer shares phone, address, and email attributes with the owner of the business account. Entity Risk Detection opens a new case and alerts an investigator to the new association. 
2. The risk rating of the customer increases according to the risk level of the business. A car wash is a cash-intensive business, so it has a high-risk rating.
3. The investigator reviews the contextual information and takes a closer look at the associated customers
4. The investigator concurs with the increased risk rating of the individual and recommends adjustments to the bank's relationship with that individual

Use Case 3: Identifying Shell Company Risk 

The Entity Risk Scenario

A bank’s customer is a shareholder of a car wash business, which also has an account at the bank. The customer opens two new business accounts, one for a car valet service and another for a car repair shop. All three businesses are registered at the same address. 

The Risk Detection Process

1. During the onboarding of the two new companies, the bank’s Entity Risk Detection system observes that they share an address with another business account. The system opens a case alerting an investigator to the association. 
2. As the two businesses have medium risk ratings due to cash intensiveness, the shared location increases the risk ratings of the associated account. 
3. The investigator takes a closer look at the set of companies registered at the same address. They determine that the money laundering risk is higher than previously assessed. 
4. The investigator adjusts the monitoring of the three companies according to their risk rating.

Use Case 4: Identifying Account Opening Fraud Risk

The Entity Risk Scenario 

A bank has multiple accounts sharing one email address, IP address, and phone number. The bank onboards another customer using the same attributes.

The Risk Detection Process

1. During onboarding, Entity Risk Detection observes that the accounts have shared email address, IP address, and phone number attributes. Entity Risk Detection opens a case, alerting an investigator to the new association. 
2. The risk rating of the customers increases based on the number of accounts and shared attributes.
3. The investigator takes a close look at the set of companies with shared attributes. 
4. The investigator adjusts the monitoring of the customers according to their risk rating.

Hawk’s Entity Risk Detection Technology

Hawk’s Entity Risk Detection Technology helps FIs gain a more comprehensive view of the financial crime risk in their customer portfolio. By consolidating data, resolving entities, and analyzing networks, FIs can use Entity Risk Detection to surface unknown risk and act accordingly. Hawk’s Entity Risk Detection technology integrates with a full suite of financial crime risk management tools, including Transaction Monitoring, Customer Screening, Payment Screening, Customer Risk Rating, and Transaction Fraud Monitoring.

To learn more about Hawk’s Entity Risk Detection technology, request a demo.