Case Studies
We work with some of the UK's most recognised organisations,from global banks and insurers to government departments, helping them harness data and AI to solve their hardest problems.
Trusted By
GenAI-Powered Quality Assurance in Financial Crime Customer Due Diligence
bigspark's team of Software and ML Engineers and Data Scientists partnered with a Tier 1 UK bank to automate their Quality Assurance (QA) processes with a goal to streamline the Quality Assurance process in Financial Crime Customer Due Diligence (CDD) using AI, without compromising on quality or control, replacing the current labour intensive manual, risk-prone process.
We delivered a GenAI-powered solution that reduced review effort by 55%, elevated the consistency of risk assessments, and laid the foundation for smarter, more scalable compliance operations.
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Automation
55% reduction in manual effort through process automation
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Quality
Better articulation of risk due to automation
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Efficiency
Reduction of human error/oversight due to robust quality controls
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Compliance
Improved regulatory compliance
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Reusability
Scope to reuse solution and pattern
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Model Accuracy and Prediction
80% improved accuracy following human review by Financial Crime
55%
Reduction in review effort
80%
Improved model accuracy
Tier 1
UK bank client
Ideation to Production for Pricing at NatWest
For over 2 years we have worked with NatWest in their pricing teams to consistently develop their use of data in their pricing methodology from Credit Cards to Mortgages.
Discovery
- Strategy, plan and intention
- Business goal & use case
- Customer types
- KPI improvement desired
Early 2024
Strategised and planned POCs with senior stakeholders to prove business case. Discussed how bigspark's experience could provide useful input. First engineer teams on the ground beginning POCs for Credit and Debit cards.
Prove Value to Procure
- Execute plan and intention
- Culture
- Business goal & use case
- Select first customer types
- KPI improvement confirmed
Mid-to-Late 2024
KPIs selected as time for price to get from backbook to customer. POC completed and business case proven. Begin delivering on credit and debit card teams using their internal front and backend system.
Deploy to Production
- Stay on track with original strategy
- Remain focused on business goal and use case
- KPIs agreed and measured
- Project management effectiveness
- Deployment and operational risk management
Early 2025
Phased deployment to first customer types. Business case presented to wider organisation, expanding delivery to Loans, Deposits and Overdrafts. SMEs lead and deliver on teams across organisation.
Monitoring Performance
- Risk appetite
- Customer types
- KPI improvement desired
- Incident management history
- Governance effectiveness
- Disaster recovery
Ongoing
bigspark team members embedded in over 5 pricing teams across frontend, backend, ML and data science. Continued improvements, releases and project management.
2+
Years of partnership
5+
Embedded pricing teams
Cards → Mortgages
Scope of delivery
Anomaly Detection Labs
How we leveraged ML and AI to address high value hypotheses at a large EU financial institution within 4 weeks
We used available infrastructure and technology to build a Machine Learning lab to design and develop machine learning and AI solutions, delivering results in a rapid and agile way which does not disrupt business as usual.
Minimal Disruption Model: Using current resources
Infrastructure Anomaly Detection
Can ML assist in identifying the source of system failure?
Data Source: User Logs — 8 types of logs e.g. PowerShell
Proxy Logs Anomaly Detection
Can ML assist in identifying high-risk anomalous user behavior?
Data Source: User Proxy Logs — Client/Server Bytes, Categories, Headers, Media Types
POCs Driven by
- Previous and current technology investments
- Business and infrastructure data acquisition
- Targets and commitments
- Moonshots: business outcomes with very fast time-to-value
Iterative Delivery Cycle
4 weeks
From ideation to results
2
High-value POCs delivered
EU Tier 1
Financial institution
Revolutionising UHF RFID Inventory Management through Computer Vision Integration
bigspark were engaged by an Asset Management Company to develop UHF (Ultra High Frequency) RFID (Radio Frequency Identification) asset management and tracking solutions.
bigspark built a centralised platform designed to efficiently manage all assets, meticulously tracking the location, condition, and usage of each asset, offering invaluable insights into their utilisation patterns. Integrating computer vision for visual verification, inventory monitoring, employee behaviour and movement patterns helped enhance the efficiency and effectiveness of the inventory management solution.
Solution Architecture
Enterprise Data Lake
Data Processing Layer
Key Outcomes
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Enhanced accuracy in inventory count and tracking by combining RFID data with visual verification provided by computer vision
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Real-time visibility by leveraging computer vision technologies
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Data-driven insights facilitating faster decision making
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Streamlined replenishment enabled by inventory notifications
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Operational efficiency and reduced maintenance overhead
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Future-proofing for evolving industry standards
Transforming Complaint Handling with AI
Organisations want faster, more consistent, and lower effort complaints handling. Many teams are held back by manual investigation, time consuming drafting, and variation in decision quality.
Our phased model introduces AI driven support where it adds the most value. It starts by simplifying written submissions, it then reduces the effort needed to gather information, and it finally strengthens decision making while keeping humans in control. This provides a clear path that delivers early benefits and builds confidence as capability grows.
AI does
- Extract key issues from incoming complaints
- Produce a clear summary of each issue
- Remove irrelevant text, acronyms, and noise
- Generate consistent, templated response letters
AI does
- Access multiple internal platforms and data sources
- Compile a full investigation pack for each issue
- Gather transaction histories, account notes, service incidents, and supporting records
- Flag cases that need human review if data is missing or complex
AI + Human
- AI evaluates investigation findings for each issue
- Recommends redress or outcome based on established rules
- Human reviews recommendations and approves or adjusts final decision
- Records decisions for audit, compliance, and continuous improvement
The Problem
1 — Time-Consuming Complaint Handling
Handlers spent 35 minutes crafting each final response letter, reducing overall efficiency.
2 — Low Employee Job Satisfaction
Handlers felt burdened by repetitive tasks, limiting focus on complex, value-adding activities.
3 — Inconsistent Accuracy and Communication
Errors led to increased review times, compliance risks, and reduced productivity.
The Solution
Implemented a GenAI-powered Final Response Letter generation tool.
AI automation allowed complaint handlers to concentrate on higher-value tasks.
Fine-tuned GenAI model pre-trained with brand tone, response templates and complaints data.
The Benefits Realised
- 85% reduction in letter generation and review cycle
- 30 minutes saved per case
- Increased efficiency and productivity
- 40% increase in employee engagement & productivity
- Reduction of manual efforts and elimination of tedious tasks
- Consistent, brand-aligned content
- Human error eliminated
- Improved overall quality & customer satisfaction
The GenAI Edge
- Tailored AI generated responses with Empathy Scores for letters and Hallucination Checks
- Continuous Learning using Model Monitoring and Logging Letter Edits and User feedback
- Re-usable, scalable, flexible solution requiring minimal change for adoption for future applications
85%
Reduction in letter review cycle
40%
Increase in employee engagement
30 mins
Saved per case
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.
Who we work with
NatWest
Banking
HM Revenue & Customs
Government
Barclays
Banking
Esure
Insurance
CAPCO
Financial Consulting
Department for Business and Trade
Government
Financial Conduct Authority
Financial Regulation
Fintech Scotland
Fintech
TISA
Financial Services
infact systems
Fintech
"bigspark is one of the UK's fastest growing startups, trusted at the heart of the most well-known brands in the country."
— The Times, Top 100 Fastest Growing Private Companies 2024
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