Enhanced Due Diligence with iceDQ for FinCEN

Figure 1

Customer credit ratings are crucial for assessing commercial and money laundering risks associated with both commercial and individual loans. To address these concerns, the Federal Financial Institutions Examination Council (FFIEC) and the Financial Crimes Enforcement Network (FinCEN) have provided guidelines for Anti-Money Laundering (AML) and Enhanced Due Diligence (EDD).

EDD necessitates the validation of customer data attributes before Enterprise Credit Risk Rating (ECRR) is calculated, which serves as a key indicator for risk and compliance officers in making loan approval decisions.

Streamlining the EDD Process: Recognizing the limitations of manual processes, which are costly, error-prone, and often fall short of regulatory requirements, the bank adopted iceDQ’s automated Data Reliability Platform in 2024.

Benefits of iceDQ: iceDQ enabled automated daily checks on 12,000 accounts data. iceDQ not only helped the bank meet regulatory requirements but also improved productivity by reducing the time and cost associated with manual data validation.

EDD is a crucial component of a sound risk management framework for financial institutions. It involves a comprehensive assessment of various ECRR attributes to determine the creditworthiness of a customer.

Figure 2

iceDQ evaluated a range of ECRR attributes such as:

Below figure 3 highlights the data flow of the entire due diligence process.

Figure 3

Figure 4

A. Delimited Files vs DB2
B. Transformation validation and reconciliation for DB2 vs DB2
C. Downstream reconciliation DB2 vs Hadoop Data Lake
D. Continuous monitoring of customer activity through the ECRR System
E. Business validation of customer additional information

Refer to figure 5 below, which shows an iceDQ reconciliation rule connecting the staging area (DB2) as the source and the Party (DB2).

Figure 5

– In section A, the staging area DB2 database is selected as source.
– In section B, the Party DB2 is selected as target.
– The column, key_acct is chosen as the diffjoin type.
– In section C, the result type is set to “A-B” and “B-A”. This checks if all the records in both source and target are exact replicas of each other.

The above section covers reconciliation testing in iceDQ. Now, let’s explore how iceDQ validates transformation logic using a user-defined function (UDF) and incorporates all the rules into a regression suite for continuous data monitoring on predefined schedules. Refer to figures 6 and 7 below for details.

Figure 6

Figure 6 above shows a custom UDF created in iceDQ to check transformations:

Figure 7

Figure 7 above shows a regression suite in iceDQ:

By leveraging iceDQ’s capabilities, the global financial services firm successfully automated and enhanced its Enhanced Due Diligence process. This implementation not only improved data accuracy and efficiency but also strengthened the firm’s risk assessment and regulatory compliance posture. The success of this project demonstrates the critical role of automated data testing in modern financial operations, particularly in high-stakes processes like customer due diligence and credit risk assessment.

iceDQ empowers organizations to ensure data trust and reliability throughout the data life cycle.

Our comprehensive platform combines data testing, data monitoring, and data observability into a single solution, enabling data engineers to proactively manage data quality and eliminate data issues before they impact business decisions.

Leading companies across industries, including prominent players in banking, insurance, and healthcare, rely on iceDQ to continuously test, monitor, and observe their data-driven systems. This ensures trustworthy data that fuels informed decision-making and drives business success.

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