Safeguarding Customer and Financial Data in Analytics and Machine Learning

Digital Transformation and the opportunities to use data in Analytics and Machine Learning are growing exponentially, but so too are the business and financial risks in Data Privacy. The increasing number of privacy incidents and data breaches are destroying brands and customer trust, and we will discuss how business prioritization can be benefit from a finance-based data risk assessment (FinDRA). More than 60 countries have introduced privacy laws and by 2023, 65% of the world’s population will have its personal information covered under modern privacy regulations. We will discuss use cases in financial services that are finding a balance between new technology impact, regulatory compliance, and commercial business opportunity. Several privacy-preserving and privacy-enhanced techniques can provide practical security for data in use and data sharing, but none universally cover all use cases. We will discuss what tools can we use mitigate business risks caused by security threats, data residency and privacy issues. We will discuss how technologies like pseudonymization, anonymization, tokenization, encryption, masking and privacy preservation in analytics and business intelligence are used in Analytics and Machine Learning. Organizations are increasingly concerned about data security in processing personal information in external environments, such as the cloud; and information sharing. Data is spreading across hybrid IT infrastructure on-premises and multi-cloud services and we will discuss how to enforce consistent and holistic data security and privacy policies. Increasing numbers of data security, privacy and identity access management products are in use, but they do not integrate, do not share common policies, and we will discuss use cases in financial services of different techniques to protect and manage data security and privacy.