This presentation details a way for banks and financial institutions to regain their ability to work with data safely and efficiently. It shows how privacy-preserving synthetic data helps enterprises gain agility in data operations while complying with the financial industry's data constraints.

1. How financial
organizations can use
synthetic data to
overcome data inertia
Statice webinar

2. Visit our website to
access the recording
of this presentation

3. Benjamin Nolan
Head of Business Development at Statice
Working with Statice's partners to help them overcome their data
access and privacy challenges with privacy-preserving synthetic data.
Speakers
Sven Guhr
Associate Director at Sopra Steria
Working on the topics of data analytics & artificial intelligence for
many years, advising leading financials organizations.
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4. Synthetic data to
produce anonymized
data that will fuel
data-driven
use-cases
Applications for
privacy-preserving
synthetic data in the
financial industry
The challenges of
data-driven
innovation & the
competitive costs of
data inertia
Agenda
I. II
.
III.
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5. The challenges of data-driven innovation
& the competitive costs of data inertia
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6. Data fuels operational efficiency, personalized customer experiences and
product development.
Why is data so important?
“Customer intelligence will be the most important predictor
of revenue growth and profitability [...]”
Machine learning applications, and cloud infrastructures are gaining in
popularity. Both require greater data access and agility.

7. Data protection regulations in Europe
What prevents companies from accessing and using data?
“between 40 and 90 percent of banks’ workloads globally
could move to the cloud in a decade. [...] However, personally
sensitive data and trading data have not moved.”
Legacy systems with proprietary formats or siloed IT infrastructures
Data is too expensive to obtain, or its quality isn’t sufficient.

8. Data processing becomes a financial and business burden because it’s
resources intensive.
What is the cost to companies?
Teams lack the data agility required to innovate
Data is too expensive to obtain, or its quality isn’t sufficient.
“Companies arming their workers with data see a 32%
"significant increase" in product or service quality.”

9. So what’s the status quo?
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10. Problem? Personally Identifying Information
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11. The first step to protect data usually is to pseudonymize it
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12. But data still contains information not unique to you:
"quasi-identifiers"
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13. Sweeney, Latanya. Weaving Technology and Policy Together to Maintain Confidentiality. Journal of Law, Medicine
and Ethics, Vol. 25 1997, p. 98-110
Illustration: Massachusetts Governor leak
Medical data Voter list
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14. Fingerprint-like information
• On its own, a fingerprint seems cryptic
• Around 100 minutiae in a fingerprint
• Experts declare a fingerprint match if 12 minutiae match
• Precise identification is possible if fingerprints are indexed and
queryable
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15. Narayanan A, Shmatikov V. Robust de-anonymization of large sparse datasets. InSecurity and
Privacy, 2008. SP 2008. IEEE Symposium on 2008 May 18 (pp. 111-125). IEEE.
Illustration: Netflix Movie Preferences
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Ratings of only 4-5
films made it
possible to
successfully identify
a large number of
users.

16. Take-away: Pseudonymization is not enough
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● Pseudonymization is not sufficient
● Do not assume anything about the attack scenario
● Auxiliary dataset may contain Personally Identifiable
Information

17. 16
But that would leave us with few to no data elements

18. 17
“This regulation does not therefore concern
the processing of such anonymous
information, including for statistical or
research purposes.”
GDPR Recital 26
The best way to protect data, is to ensure strong safeguards
such as anonymization

19. A synthetic data generator to produce
anonymized data that will fuel
data-driven use-cases
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20. What are we talking about?
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21. What this might look like
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22. What’s different about privacy-preserving synthetic data?
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Data utility & data
privacy
Deep generative
models
Differential
privacy
+

23. Ability to use the data
What are the benefits of using it?
Greater data agility
Reduced corporate and financial risks.
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24. Applications for privacy-preserving
synthetic data in the financial industry
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25. Application: Next Stage of Anti Financial Crime
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26. Application: Cross-border ML
● Production financial data is difficult to access and can’t be shared
across borders.
● In multinational organizations, data teams and specific subject matter
experts are not always where the data is.
● Synthetic data provides quick access to relevant data for development
and testing, reducing staffing costs, and accelerating development
speed.
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27. Application: Personalized data-based services
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28. Application: Supporting Innovation with Sandboxing
“...we have been exploring the concept of a digital sandbox.
This will enhance some existing features of our innovation
services [...] providing innovative firms access to high
quality synthetic data sets to be able to test and develop
proofs of concept in a digital testing environment.”
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29. www.statice.ai
Visit our website for more
information on our product.