MarTech Interview with Ché Wijesinghe, CEO at Cape Privacy

Ché Wijesinghe, CEO at Cape Privacy talks about some of the key data science and data privacy challenges facing B2B teams in tech while diving into the start-up story of Cape Privacy in this chat:


Tell us a little about yourself Ché, and tell us about Cape Privacy and how it’s solving the core problems in data science and privacy for business users?

Thanks for the opportunity. I’m the CEO of Cape Privacy, a company that was founded in 2018 as an encrypted learning platform to help companies leverage external data for data science and machine learning, while protecting privacy by design.

Previously, I served as Chief Revenue Officer at Datalogue and was responsible for worldwide field operations at OmniSci. I also ran sales globally for Cisco Systems’ Data and Analytics Group.

At Cape Privacy, we believe that encrypted learning has the potential to help solve some of the world’s hardest data problems.  Most notably, such as generating Alpha for the Financial Services industry by enabling cross-department/organization external data access.  Cape Privacy’s unique formula of advanced cryptography and machine learning enables collaboration on data without ever decrypting it.  Furthermore, Cape guarantees trust and security through privacy by design. On the spectrum of privacy-enhancing technologies, encrypted learning has several advantages including order of magnitude performance and security benefits: it’s significantly more secure than anonymization, it doesn’t affect utility compared to differential privacy, it’s more flexible than federated learning and much faster than homomorphic encryption. 

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What are some of the biggest data science and data privacy challenges you see financial institutions struggle with today?

The challenge Cape is addressing is that competitors in any industry are highly protective of their data, which can make collaboration impossible.  Financial services is certainly no exception. What if financial services companies could work together in a secure and trusted way to derive valuable business insights, increase revenue and drive competitive advantage? This is where our encrypted learning comes in.Consider potential data providers like major retailers and credit card companies. These organizations are limited in their ability to monetize their data, given concerns around proprietary and confidential data being exposed.

Additionally, financial institutions such as hedge funds and investment management firms would pay a premium to subscribe to external provider data to optimize and enrich their machine learning models.  Even a percentage point increase in accuracy of their models can represent millions if not billions of dollars in business value.  Analytical innovators know that organizations that share their own data with customers, vendors, government agencies and even competitors report increased influence in their business ecosystem.

Cape’s technology is the broker between data providers and subscribers, enabling them to securely collaborate on encrypted data because they know it’s secure and trusted.

The Covid-19 pandemic has forced banks and financial institutions to embrace digital transformation. When it comes to bigger institutions choosing a fintech partner to support these initiatives, what are some top thoughts that come to mind?

With the digitization of financial services only accelerated by the global pandemic, the need for reliable fintech partners is at an all-time high.  Financial institutions large and small are realizing that internal innovation and R&D efforts will only take them so far; today’s financial leaders look to fintech vendors for the latest in technology advances.  In choosing such a partner, one of the top things they should be thinking about is how well the partner can help keep confidential  data secure —otherwise, all their efforts in machine learning and AI will be constrained.

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Can you talk about some interesting innovations you’ve come across in the global market that are enabling better customer services for financial institutions? 

Yes there are several newer trends that I think have become important for financial institutions to embrace:

  • Advances in encryption to allow for secure processing of data in insecure environments (like the cloud).
  • (Near) real-time inference and machine learning.
  • Advances in deep learning, allowing for improved modeling, especially for complex problems like forecasting and market evaluation.

As global tech trends change and the market shifts due to business environments (and also Covid-19), what are your comments on the state of fintech especially in 2021 and beyond?

Fintech partners will be more vital than ever before as digital transformation in financial services becomes a top priority.  Financial institutions are realizing that they need external partners to unlock dark data in order to make it actionable—and will be looking for the ones who can also keep it secure. 

Overall, we hope to see more collaboration across financial services for industry advancement thanks to the increased trust that solutions like ours help build. 

We’d love to hear about some of Cape Privacy’s upcoming plans / innovations for users?

We recently brought on David Besemer as VP of Engineering, a former executive at OmniSci and Cisco, who will be responsible for all aspects of engineering operations, procedures, and strategy. Adding to the deep expertise in cryptography, computer science and engineering of our founders Gavin Uhma, Ben DeCoste and Morten Dahl, David is a great addition to our team as we continue to innovate.

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Cape PrivacyCape Privacy offers an enterprise SaaS platform for encrypted learning combining advanced machine learning and cryptography

Ché Wijesinghe is the CEO of Cape Privacy, an Encrypted Learning SaaS company enabling collaborative data science for secure machine learning.  He also serves as an advisor to boldstart ventures, an investment firm for innovative founders reinventing the enterprise.  Ché has an extensive background in building high-impact teams that generate sizable revenue growth and business value.  Previously, he was Chief Revenue Officer at Datalogue and served as a Senior Vice President at OmniSci.  Ché also led sales globally for Cisco Systems’ Data and Analytics Group.

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