In 2020, identity fraud losses exceeded $56 billion in the United States alone. This number includes $13 billion for traditional identity fraud, such as data breaches, and $43 billion for other types of identity fraud scams. 

Financial services companies have been reluctant to collaborate for multiple reasons; including competitive pressures, concerns about antitrust exposure and additional concerns about data privacy.  However, $56 billion is too large a number to ignore. A key reason identity fraud happens is any one financial institution has just a limited profile of its customers. The typical consumer has multiple accounts with multiple institutions.  If financial institutions could collaborate and gain a holistic picture of their customers, they could develop more effective algorithms to combat identity fraud.  This holds true for other illegal activity, such as money laundering schemes. In our recent Privacy Enables the Adoption of Open Banking blog, we discuss reasons banks and financial institutions are still reluctant to share data with competitors. 

Solutions that enable and facilitate collaboration haven’t been up to the task. Legal agreements institutions attempt to put in place are complex, take a long time to negotiate and rely on the goodwill of the parties involved. Some technology solutions, such as homomorphic encryption, do enable data sharing while remaining in compliance with data privacy standards, but severely degrade the performance of financial institutions’ networks. Others, such as secure enclaves provide an incomplete solution.

TripleBlind’s solution addresses these issues and allows financial services competitors and partners alike to share data without needing to trust the recipient because the most sensitive information within each data set remains private. TripleBlind’s API-driven virtual exchange creates an environment where encrypted data can safely be shared and used by institutions without ever exposing them to the risks that come with handling raw data, ultimately reducing fraud, intentional or not, and ensuring higher levels of compliance.

One example of how TripleBlind’s solution could prevent credit card fraud would be for Bank 1, Bank 2 and Bank 3, to share encrypted data with a credit card fraud detection company using TripleBlind’s private AI infrastructure. If a customer has accounts with the three banks, it would be most beneficial for the fraud detection company to access spending habits from all three sources and then share data among them to ensure the customer’s finances are secure.

However, while Bank 1 wants to give the fraud detection company information regarding the consumer’s spending habits, Bank 1 is reluctant to share that data with Banks 2 or 3. TripleBlind’s technology would only give Bank 2 and 3 the essential information necessary to determine if the customer’s account has been compromised; and vice versa for data from the other two banks.

Additionally, the data can only be used for its agreed-upon purpose. So if Bank 1, Bank 2 and Bank 3 agree to share data for fraud detection, they cannot access it for additional operations, such as marketing activities. 

Sharing data with TripleBlind allows competitors to collaborate for mutual benefit without giving up the proprietary data – everybody wins.

TripleBlind has already partnered with leaders in the healthcare and financial services industries to tackle their data sharing needs with ensured safety, including Mayo Clinic, BC Platforms and Snowflake. If you are interested in exploring how your company can increase your data sharing capabilities, please contact us for a free demo HERE.

TripleBlind is currently the only solution that effectively de-identifies genomic data. Its groundbreaking approach to data sharing involves de-identification via one-way encryption that allows for safe and compliant data sharing among healthcare institutions. The solution meets the legal definition of de-identification, and TripleBlind never hosts any data that is being shared.

TripleBlind unlocks the ability for healthcare organizations to share PHI, health records, genomic and other data, enabling data to be usable at its highest resolution without incurring an accuracy penalty. TripleBlind de-identifies data by splitting each record, randomly, byte-by-byte, automatically de-identifying it without anonymizing it. Because the random splits cannot be used to identify an individual, the data sharing remains compliant with privacy standards, like HIPAA and GDPR.

Blind de-identification via one way encryption provides many advantages over the five methods for data anonymization most frequently utilized today, the utmost being that blind de-identification does not alter the fidelity of the data. Apart from often being slow, expensive, and unclear as to if full sets of data are actually fully de-identified and secure, other methods of de-identification remain inferior to TripleBlind’s mode of blind de-identification.

• K-anonymization alters the fidelity of the data through two means: suppression (data masking); certain values of the attributes are replaced by an asterisk. All or some values of a column may be replaced by an asterisk; or generalization; individual values of attributes are replaced with a broader category, e.g., the value 19 might be replaced with <20,
• Pseudonymization replaces private identifiers with fake identifiers or pseudonyms,
• Data swapping (shuffling or permutation) rearranges the dataset attribute values so they do not correspond with the original records,
• Data perturbation modifies the original data set by rounding numbers and adding random noise, also known as differential privacy,
• Synthetic data is often used in place of altering the original dataset or using it as is and risking privacy, but even the best synthetic data is still a replica of specific properties of the original data.

One way encryption creates a clear path from data collection to data usage that is significantly faster, cheaper, seamless and compliant. 

TripleBlind has already partnered with leaders in the healthcare and finance industries to tackle their data sharing needs with ensured safety, including Mayo Clinic, BC Platforms and Snowflake. 

We have upcoming webinars that go into depth about our services so follow us on LinkedIn and Twitter for updates. If you have questions or would like a free hands-on demo, reach out to us at contact@tripleblind.com.