Aizle · Synthetic Data
Aizle Case Studies
See how organisations use Aizle's synthetic data to innovate safely — from regulatory sandboxes and government smart data schemes to global bank AI hackathons and fintech product development.
Case Study: UK Financial Conduct Authority
The Challenge: How does the Financial Regulator or Central Bank bring innovators together, so that they can safely build new products and services, explore and demonstrate AI safety, and collaborate on critical social challenges?
Aizle is the leading provider of synthetic data in the FCA's Digital Sandbox, where regulated firms innovate every day to develop new and exciting financial service propositions. In the first year, Aizle's data powered more than 50% of all FCA sandbox usage, helping 60+ companies — from start-ups to global banks — transform how they approach data innovation.
Use Cases
- APP Fraud – Machine Learning and AI detection
- AI testing and regulatory exploration
- KYC/On-boarding
- Consumer Vulnerability
- Cross-sector smart data
- Small Business Lending
- Mortgage process improvement
- Secure Data Sharing and Privacy Enhancing Technologies
- Combining Telephone and Banking data for real-time Fincrime intervention
These ground-breaking datasets contain 2 years of cross-sector data for up to 30,000 people and 5,000 businesses, across different banks, energy providers, insurance companies and multiple other sectors. It also correlates data on SMS and Call activity, so that cross-sector Financial Crime use cases can be explored. Full Personal data on KYC checks, Employment, Education, PII, and Vulnerabilities is included for consumer protection and education use cases.
Case Study: UK Department for Business and Trade
The Challenge: How does Government design Smart Data Frameworks with confidence that the economic, societal, and individual impact is understood, and implemented in the best way?
Aizle data powers the Department for Business and Trade Smart Data Challenge, allowing users to step into the data future of a full "Smart Data" scheme — supporting UK Government analysis of policy, implementation and economic impact of a Smart Data Economy. This world-leading synthetic data encompasses thousands of people and businesses, with billions of datapoints.
Sector Coverage
- Open Banking and Finance – accounts, transactions, loans, mortgage, cards
- Open Insurance
- Open Retail (Supermarket)
- Open Energy
- Open Property
- Digital Identity and Personal Data
All data is linked with full integrity across all data tables to support all use cases in the challenge, so that the economic and society benefits of Smart Data can be established — safely and with confidence. Future coverage includes: Open Pensions, Open Investments, Open Tax, Open Transport.
Case Study: Global Bank AI Hackathon
The Challenge: A Global Bank wanted to run a collaborative multi-team event to explore use cases, produce prototypes, and compare and contrast the usage of various AI toolsets. But when working with sensitive data, how can this be shared among many users, and used in AI tools that might not yet be cleared or permitted for use in these circumstances?
Aizle worked with the bank to produce a range of datasets that were completely safe for AI processing, meaning all uses of the data were compliant with internal and external policy and could be shared freely between teams. This allowed rapid prototyping and demonstration of how AI could integrate with existing products and showcase how it could be used in future innovations.
Data Features
- Full, unredacted data on businesses, people, and their families
- Alternative data structures: data presented as both internally-formatted and Open Banking compliant formats
- Multi-bank views, enabling greater understanding of customer context
- Restricted data such as financial crime attempts and outcomes, enabling innovation where it would ordinarily be heavily controlled
- Commercial and Personal Banking coverage
The bank is acknowledged as one of the UK leaders in AI usage and has moved rapidly to integrate it in many parts of its operations and customer-facing touchpoints. The Hackathon illustrated to senior and executive colleagues the true potential AI had to transform the business, and to drive better business and customer outcomes.
Case Study: infact systems
The Challenge: A start-up real-time credit reference agency needed to develop, optimise and update an identity matching algorithm — to demonstrate the value of their product to customers and investors. But how can you do this when you don't have the variety, volume or veracity in your data to truly understand coverage and blindspots?
Aizle worked with infact systems to develop from the ground up multiple datasets of 100k customers, simulating source data gathered by many different data providers. Infact used this to develop methods to reliably match customers across these sources, compensating for many issues engendered by mixed and confused data lineage and legacy systems.
Test Coverage
- Multiple datasets of 100k customers with 10 data mutations each, with a canon 'ground truth' to measure against
- Data compression: truncation, abbreviation and concatenation
- Data entry: transposition, 'fat finger' mis-entry
- Data age: maiden names, old addresses
- Data variance: alternate spellings, special and non-roman characters, nicknames
Infact successfully managed to build and validate their industry-leading matching service on Aizle data, helping them to establish credibility with customers and investors. As a result of our partnership, infact have raised £4Mn in VC/PE funding, and now partner with customers such as Curve, Zopa, and Fair For You.
Ready to innovate with synthetic data?
Talk to us about how Aizle can unlock safe, compliant data innovation for your organisation.