Data Minimization is a cornerstone within Data Risk & Privacy (DRP) and focuses on carefully limiting the amount of personal and business-sensitive information that is collected, processed, and stored. By collecting only the data that is directly necessary for a specific and justified purpose, the overall data volume is significantly reduced. This decreases the risk of unnecessary or outdated data falling into the wrong hands and prevents unnecessary exposure in the event of a breach. In the context of fraud management, Data Minimization contributes to reducing the attack surface and shortening exposure lines, ensuring that malicious actors cannot exploit excess or irrelevant data to cause financial or reputational damage.
Financial Mismanagement
In combating financial mismanagement, the focus is on limiting the collection and storage of financial data to what is strictly necessary for audit and reporting purposes. Only transaction data with indispensable metadata—such as transaction ID, date, and amount—are retained, while detailed invoice copies or extensive customer profiles are anonymized or deleted after processing is complete. Periodic reviews of data flows ensure that outdated financial records are cleaned up in accordance with retention policies. As a result, only the essential dataset remains active, reducing the risk of manipulation or unintended access to large financial archives.
Fraud
For fraud management, data minimization is applied to customer and account information, with only the essential attributes—such as customer number, maximum transaction limit, and authenticator status—actively stored. The collection of unnecessary personal data, such as IP history or full behavioral logs, is reduced to situations where it directly contributed to a fraud detection model. Once a customer relationship is terminated or a transaction period is completed, remaining copies of supporting data are anonymized. By limiting the dataset to core attributes, fraud risk exposure remains contained, while crucial insights for anomaly detection are preserved.
Bribery
In the fight against bribery, Data Minimization focuses on cleaning up contract and supplier data after due diligence activities are completed. Only essential verification materials—such as business registration numbers, approval dates, and compliance statements—are retained in the supplier domain. Detailed due diligence reports, confidential correspondence, and negotiation documents are encrypted and stored under strict retention policies after approval, or they are restructured or deleted. By no longer actively retaining unnecessary documentation, the number of data points available for manipulation is reduced, further minimizing the complications of secret price-fixing or invoice fraud.
Money Laundering
To combat money laundering activities, customer identifications and transaction profiles are reduced to aggregated risk scores and tokens that only allow for necessary validations. Detailed Personally Identifiable Information (PII) fields such as full address and date of birth are anonymized once the identity is confirmed. Transaction logs are compressed to key fields such as date, amount, and risk classification, after which underlying fields are periodically purified. This approach limits the amount of data that could be used to hide advanced structuring or layering techniques, while the effectiveness of monitoring systems for suspicious transactions remains unaffected.
Corruption
Within the fight against corruption, data minimization is applied to internal policy and decision-making documents. Only policy rules, approval status, and responsible officers are retained in active workflows. Detailed minutes, email correspondence, and internal memos with background information are encrypted and archived or anonymized after decision-making. As a result, only a concise register of decision-making steps remains available for audits, while any unnecessary details that could serve as a basis for corrupt reinterpretation or manipulation are no longer accessible. This minimizes the risk of covert policy adjustments.
Violations of International Sanctions
In sanction compliance, data minimization is applied to transaction and partner data so that only necessary attributes—such as entity ID, country information, and risk status—are actively managed. Detailed communication and document logs about previous transactions with sanctioned regions are anonymized or deleted after review. Any email attachments and conversation logs that are not directly relevant to compliance audits are purified according to retention policies. By limiting the dataset to core data for sanctions screening, focus areas remain sharp, while potentially sensitive information that could be misused to mask sanction routes is no longer present.
