Solutions for Banking and Finance start with the ability to share data safely

Enable data to be shared forvaluable insights, without riskingprivacy or competitiveness

use cases

Our solutions and platform enable the banking and finance industry to use data more extensively, and for greater insight, while protecting individual privacy and competitiveness.

Systematize disclosure avoidance and strengthen regulatory compliance

Financial institutions can employ differential privacy to create aggregated reports for regulatory purposes like AML and KYC, offering essential insights without revealing identifiable information.

Enable data monetization

Use differential privacy to analyze large datasets securely without compromising individual privacy. DP adds security, deterring attackers by preventing personal data from being linked back to individuals.

Unlock new data collaboration opportunities with clean rooms

Analyze shared customer data securely without exposing individual details, facilitating safe collaboration on fraud detection algorithms, and co-developing personalized financial products with external partners.

Assure safe internal data sharing and data reuse

Differential privacy can be applied to credit score data and loan repayment histories to develop or refine credit risk models. Assess risk more accurately without compromising the privacy of individual customers' financial information.

Guarantee safe external data sharing

Securely share anonymized data with external parties for research, regulatory compliance, and fintech collaborations, ensuring individual privacy while supporting innovation and oversight.

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our process

A collaborative, calibrated process to assure utility while maintaining privacy

Proven with data-driven leaders in sensitive data-focused industries like banking and finance, our process and platform deliver on your specific goals.

Define the problem

Outline the problem, explaining the data release purpose, strategy, privacy considerations, error metrics, and a pseudocode algorithm draft.

Confirm the viability of using DP

Using default hyperparameters, see if it is possible to conduct a differentially- private data aggregation.

Decide on error metrics to optimize for

Develop internal error metrics to assess the utility of the differentially-private dataset, considering that while added noise is necessary for privacy, excessive noise can compromise data usefulness.

Experiment with a wide variety of hyperparameters

Before finalizing error metrics, conduct a grid search on hyperparameters such as output threshold, noise scale, and keyset to find the optimal set using the predefined error metrics.

Productionize the pipeline

Finalize the script with integrated error calculation and privacy loss logging, then automate its execution to run at regular intervals.

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Leading data-focused enterprises work with Tumult

How we use differential privacy

Derive insights and inform decisions in credit, finance, and product development from sensitive data, while preserving privacy.

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Raw personal data: too sensitive to use

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Protective transformation, with DP

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Safe to share summary data. DP offers valuable insights with guaranteed privacy.

Proven in production, Tumult’s solutions for banking and finance advance financial innovation.

Solutions for banking and finance

Why is differential privacy an ideal fit for banking and finance’s data use cases?

The sector’s unique need for balance between the need for stringent privacy measures and the potential benefits of data analytics and sharing is well matched by DP technology.
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Differential privacy offers the sector a powerful tool to leverage data for innovation, compliance, and improved customer service while upholding the highest standards of privacy protection. This approach not only mitigates risks associated with data sharing and analysis but also aligns with growing regulatory and consumer demands for stringent data privacy.

Videos

A short tour of Tumult Analytics

Curious to see the best-in-class differential privacy framework in action?

Recent articles

Evaluating the usability of differential privacy tools with data practitioners

Differential privacy (DP) has become the gold standard in privacy-preserving data analytics, but implementing it in realworld datasets and systems remains challenging.

Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy

Tumult Analytics is an open-source framework for releasing aggregate information from sensitive datasets with differential privacy (DP).

Using differential privacy to safely release earnings data on post-secondary grads

When the U.S. Census Bureau needed to release data on the median earnings of post-secondary graduates, one Tumult Lab founder helped the Census Bureau design a new differentially private algorithm.

Evaluating the usability of differential privacy tools with data practitioners

Differential privacy (DP) has become the gold standard in privacy-preserving data analytics, but implementing it in realworld datasets and systems remains challenging.

Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy

Tumult Analytics is an open-source framework for releasing aggregate information from sensitive datasets with differential privacy (DP).

Using differential privacy to safely release earnings data on post-secondary grads

When the U.S. Census Bureau needed to release data on the median earnings of post-secondary graduates, one Tumult Lab founder helped the Census Bureau design a new differentially private algorithm.

Unleash the power and value of your data.