Use Pseudonymisation-Enabled Legitimate Interest processing to:
Key Business Considerations
In financial services and retail banking, client data is processed and used in a number of different ways. Analytics, AI and ML tools help to determine the credit of clients, test models for trading, analyse the potential market impact of trades, and interact with customers through chatbots, as well as producing reports for regulatory compliance. Furthermore, data is used to help prevent and detect fraud and financial crimes by looking for unusual behaviour.
Analytics, AI and ML provide financial services professionals with information to improve:
Key Legal Considerations
An important aspect of the GDPR is the requirement that consent must be specific and unambiguous to serve as a valid legal basis. In order for consent to serve as a lawful basis for processing personal data, it must be “freely given, specific, informed and unambiguous.” These requirements for specific and unambiguous consent are impossible to satisfy when advanced analytics are not capable of being described with the necessary specificity and unambiguity at the time of data collection, due to their complexity.
This means that consent cannot easily be relied on in the context of financial services and retail banking, for this kind of processing. Instead, you need to rely on Legitimate Interests processing as a complement to valid and lawful consent, to support advanced analytics. Legitimate Interests processing provides benefits for data controllers wanting to lawfully use data for secondary processing and repurposing, such as fraud detection in the financial industry.
However, for Legitimate Interests processing to satisfy legal requirements, you must show that you are using “appropriate safeguards” to adequately reduce the risk of data misuse, such as GDPR-compliant Pseudonymisation.
Anonos Pseudonymisation Technology
The problem is that until now, no data protection technologies were capable of supporting Legitimate Interests processing, by reconciling the conflict between data protection and utility when processing the personal data of customers to maximise lawful data value.
For example, conventional data protection technologies that support anonymisation, encryption, static token allocation, and differential privacy:
Click here to learn what other global companies have proven – that it is possible to retain up to 100% of the accuracy of analytical value when processing datasets protected using patented Anonos Variant Twins®.
Anonos state-of-the-art Pseudonymisation technology is superior to other data protection techniques because it helps enable lawful repurposing, distributed secondary processing and data sharing while delivering data utility equivalent to processing unprotected cleartext versions of personal data.