Data is the core of financial crime system implementation

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• Outdated or stale data.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:

• 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.