Currently, most banks don’t have internal data masking and anonymization capabilities, as data monetization and sharing is not a widely practiced approach. There are different levels of data masking. (Anonymized data removes any personal identifiers; synthetic data injects additional noise and randomness.)
Goals and Objectives
The goal of data masking is to anonymize personal and transactional data; so data aggregates can be used to develop behavioral patterns, preferences, segments, and location patterns. Data masking is an essential step for data monetization without the customer consent.
Data analytics, data mart, data lake
Use Case Summary
Data masking is an essential prerequisite for data monetization and data sharing without customer consent. Data is anonymized and aggregated so that it can be used for statistical analysis while preventing identification of the data subjects.