Data creation and collection is a vital aspect of doing business in financial services. Every transaction and customer interaction creates a data trail that, in aggregate, can reveal valuable trends and insights. When analyzed with the right models, financial data can be used to create decision intelligence and generate predictions that can be used to improve trading performance, reduce risk, create and improve products, detect fraud, and give the user a competitive edge.
But there is friction in the gears. Data security and privacy regulations, not to mention the imperative to keep intellectual property secret, require that financial services organizations take steps to protect sensitive data. A litany of federal, state, and international regulations and standards like the Gramm-Leach-Bliley Act (GLBA), the Fair Credit Reporting Act (FCRA), Europe's General Data Protection Regulation (GDPR), Massachusetts 201 CMR 17, New York 23 NYCRR 500, the Payment Card Industry Digital Security Standard (PCI-DSS), and many more stand in the way of putting valuable data to use.
That is because the steps required to protect sensitive data create conflict with the use of that data for generating valuable business intelligence. Encryption is the cornerstone of any data protection and compliance program, but encrypted data is difficult to work with for training machine learning algorithms, running sophisticated data models, or creating predictive intelligence.
Traditionally, data would have to be decrypted and formatted in such a way that key elements associated with personally identifiable information (PII) would be omitted in order to protect the privacy of individuals. Every row of PII scrubbed from the record denied the organization running their models the kind of rich, detailed information that would produce better results. Models might be supplemented with synthetic data, federated data, or run using intended to improve outcomes, but each alternative method was plagued by drawbacks such as data bias, high cost, unreliability, and the risk of running afoul of authorities due to mishandling or a data breach.
Today, the stakes are even higher. Data cloud operators like Snowflake make a compelling case for moving data onto third-party platforms that not only reduce the cost of maintaining an on-premises data warehouse, but that provide integral tools like data science and applications, data lakes, artificial intelligence, and other means of putting data to use. And yet, even with the tremendous capabilities made possible with such innovative services, the fundamental problem remains: protecting the most sensitive data requires encryption, and machine learning data models can't use encrypted data.
Cape Privacy enables businesses to run predictive machine learning models on encrypted data stored in third-party cloud platforms like Snowflake. Data owners never have to worry about exposing their data to make it useful for training machine learning algorithms or generating decision intelligence. Data that was previously inaccessible for AI/ML because it was encrypted can now be used to run powerful predictions without decryption.
In order for artificial intelligence applications like machine learning and decision intelligence to function at their maximum potential, they need data; lots of rich, detailed, and often sensitive data. Using Cape Privacy to gain access to new sources of protected data without having to decrypt it means that financial services firms can obtain a more precise understanding of market trends, their customers, and product performance at a level of detail that was previously not possible. Better data produces better decisions, and better decisions can unlock millions of dollars in previously unrealized business value.
Cape's platform uses secure multiparty computation (MPC) to operationalize the data securely, avoiding a single point of failure and enabling computations on data that was previously inaccessible. Instead, encrypted data can now be transferred to a third-party data cloud, along with whatever models they choose, and run predictions using the encrypted data. Because the data is never not encrypted, privacy and security compliance are assured.
With access to new, rich sources of data, predictive modeling in financial services can deliver better decision intelligence for more precise credit and market risk calculations, better personalization of customer services, more accurate financial fraud detection, and even easier Know Your Customer (KYC) regulatory compliance. One Cape Privacy customer was able to access and analyze 17 years of previously protected third-party credit card transaction data. By improving volume trading performance by only a fraction of a percent, the firm realized tens-of-millions of dollars in new revenue.
You've always known you could improve your business outcomes if only you could put your most sensitive data to work. Now you can. Now with Cape Privacy, it's a new era for data cloud users who want to produce better decision intelligence and generate more powerful predictions. For more information about Cape Privacy, contact us to start a conversation with one of our experts.