Data is the core of financial crime system implementation

Nikhil Fafat, Michael Dickey, Emily Long

Imperfect data can negatively affect your financial crime program system implementation. Here’s how you can mitigate that risk.

Financial crime monitoring and reporting systems rely heavily on data to perform tasks effectively and help financial services organizations maintain business relationships and compliance with regulations. With financial services organizations continually growing, adding products and services, and using more complex in-house and vendor systems, identifying and managing the risks presented by imperfect data have become increasingly common challenges in financial crime operations.

Data containing inaccuracies, errors, or incomplete information can have downstream impacts on the output of a financial crime monitoring system. But staying up to date with the latest technology is not enough to reduce the risks from imperfect data. Organizations must have a clear, ongoing strategy to address data quality issues and implement compensating controls.

Financial crime program performance can improve when organizations implement effective controls to identify and manage the impacts from data issues. Following are three approaches to consider.

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Create a robust data sourcing strategy

One of the most important ways to identify and manage imperfect data is by creating a robust data sourcing strategy. Doing so can help organizations identify and prioritize key categories of data required for financial crime detection, such as customer, account, and transactional information.

A data sourcing strategy also helps organizations identify and plan for imperfect data, covering areas such as data requirements, data sources, data quality standards, and governance. By identifying imperfections, organizations can proactively address them before they compound issues.

Solve common data challenges to better understand customers and their risks

Many systems within organizations are siloed and lack communication with others, which makes it difficult to effectively identify and manage data quality risks.

Common data quality challenges that can arise include:

Duplicate data. Similar data from different channels and source systems can give rise to duplicate data. Multiple variations of the same record, such as a customer or transaction, can inflate a data set and skew metrics with the volume of records and alert output.
Inconsistent data. Mismatches in the same information across multiple data sources can lead to data inconsistencies. Causes include human error during data entry, data migration issues, or changes in customer information that are not promptly updated downstream. Normalizing data before transfer into a financial crime detection system is critical for consistency and validity.
Missing or incomplete data. If insufficient information required for financial crime analysis has been collected, those gaps can lead to improper monitoring.
Default values. Shortcuts, exceptions, or gaps in data entry can result in incorrect data sets. For example, a front-line employee might enter all 1s for a customer’s Social Security number to expedite the account opening process without understanding its downstream impacts.
Outdated or stale data. Information that is no longer accurate or useful might exist, but when customers update their information, changes are not fed downstream in a timely manner, if at all.
Orphaned data. Accounts that don’t tie back to a primary account holder can create orphaned data. This situation often occurs with stale data or when systems are not updated in sync with each other.

Imperfect data can significantly hinder the performance of a financial crime detection system. When data issues are not identified and managed, they can contribute to downstream impacts:

Data issues can contribute to downstream impacts: increased operational costs, reduced efficiency, inaccurate decision-making, and false positives

Increased operational costs. Repeated data errors and inconsistencies increase the need for system maintenance and troubleshooting, which consumes resources and increases costs.
Reduced efficiency. Imperfect data can lead to inefficient processes and workflows, making time-consuming manual data cleaning and validation necessary.
Inaccurate decision-making. System outputs are only as reliable as their data inputs. Imperfect data can lead to poor recommendations and unreliable management reporting, whether that data is training machine learning (ML) and artificial intelligence (AI) models or informing boards.
False positives. Imperfect data can lead to false positives, where legitimate activities are flagged as suspicious. This can overwhelm compliance teams and waste resources. Conversely, false negatives can occur when an anti-money laundering (AML) system fails to detect actual money laundering activities, posing a significant risk to an organization’s regulatory compliance and reputation.

Identify and mitigate the risks of imperfect data with appropriate controls

Controls or solutions an organization can consider to improve data management: ongoing data quality monitoring, exception handling, reconciliation, and automation technologies

A centralized and consistent data management approach is vital for the effectiveness and efficiency of financial crime-fighting efforts. Following are a few controls or solutions to consider:

Ongoing data quality monitoring. A continuous monitoring system helps expose data quality trends and can alert organizations of potential gaps and exceptions in processes. Organizations can focus on key data elements like customer date of birth, identification (ID) type, country of citizenship, account status, segmentation ID, transaction code, and transaction date for downstream applications.
Exception handling. What happens when imperfect data doesn’t conform to system requirements? As a best practice, organizations should tag each exception with a severity level based on type. Each severity level should include a process to revise the exception, with more severe issues requiring filtering into a separate queue for review and correction before sending to the AML system.
Reconciliation. Comparing and aligning data from multiple sources or systems can improve consistency, accuracy, and reliability. Reconciliation is essential to building trust in reporting and maintaining compliance with regulatory requirements.
Automation technologies. Advanced analytical capabilities like robotic process automation (RPA) can perform repetitive data-related tasks such as data entry, extraction, and validation.

How we use data is changing, but that data must remain useful

The financial crime landscape evolves every day. New regulations, geopolitical events, digital disruptions, and the introduction of technologies such as RPA, ML, and AI all affect the successful implementation and ongoing maintenance of fraud and AML programs.

However, one trend that remains consistent is the heavy reliance on data and the issues tied to imperfect data. Our financial crime system implementation specialists can help you build a robust strategy of processes and controls that champion accurate, consistent, and timely data while remaining adaptable to changing regulatory and audit standards.

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