Financial Services

The Financial Services industry is well renowned for its innovation and status as a first mover. At the start of the internet era, HTTPS was initially heavily adopted by the financial services industry for payments and several other use-cases. It enabled multiple new revenue opportunities and over the years HTTPS adoption increased. Today the technology is the industry standard.

We stand at a similar crossroads today, where the financial service industry is in dire need of new technologies to move the sector forward and create new opportunities, especially within the analytics space.  

Over the past few years, financial services firms have assembled large numbers of data scientists to transform their business using the data they own internally as well as by accessing third party datasets. However, most of the data is very sensitive. More often than not they are unable to access or utilize this data due to regulatory and privacy requirements, like GDPR and CCPA. This leaves them struggling to inject data insights into their business lines, be it a credit card approval at a bank, quantitative trading strategies at a hedge fund, or building unified customer profiles within insurance.

Similar to how HTTPS ushered in a new era, advanced new technologies like secure multi-party computation, differential privacy, and AI, can solve these challenges for the financial sector. CryptoNumerics’ Privacy Automation and Data Sharing solutions provide a critical end-to-end software component that balances compliance needs with business goals

How CryptoNumerics Supports Financial Services Organizations

 

How to collaborate and share data with third parties to build and train modelsCurrent approaches to use data for secondary purposes are either in a grey area or clearly do not respect user privacy. Using state of the cryptographic techniques such as Private Set Intersection and Secure MultiParty Computation CryptoNumerics allows linking and analytics on internally siloed data, as well as third-party data. These techniques allow data collaboration in a legal and ethical way, without requiring the need to share or move data in any form or manner.
How to deliver high-value data utility for Data Science and AnalyticsCurrent approaches to de-identify data such as masking, tokenization, and aggregation, can leave datasets unprotected, or without analytical value. Using AI and the most advanced anonymization techniques, such as optimal k-Anonymity and Differential Privacy, CryptoNumerics privacy-protects your dataset and maintains their analytical value.

 

 

Use Case: Credit Card Data as a New Revenue Stream

CryptoNumerics helped a credit card aggregator open up a new revenue stream, by using its data for secondary purposes with its business partners: retailers, banks and advertising companies. 

Use Case: Building a Privacy-Protected Data Lake

A large bank wanted to broaden access to its data lake without compromising data privacy, preserving the data’s analytical value, and at reasonable infrastructure costs.

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