AI & Data Solutions for Modern Financial Institutions
We help banks, fintechs, insurance companies, and financial services firms detect fraud faster, automate compliance, and modernise core systems — enabling speed and trust in equal measure.
Why Financial Services Leaders Struggle
The operational and strategic barriers holding financial services organisations back.
Sophisticated Fraud
Fraud patterns evolve faster than rule-based detection can keep up — resulting in financial losses, regulatory fines, reputational damage.
Regulatory Burden
AML, KYC, GDPR, PSD2, Basel III regulations require enormous reporting effort, diverting skilled staff from value-generating work.
Legacy System Constraints
Monolithic core systems limit product speed, prevent open banking readiness, create integration complexity with fintech partners.
Where We Create Value
Specific use cases delivering measurable results in financial services.
Real-Time Fraud Detection
Adaptive ML models learn new fraud patterns continuously, significantly outperforming rule-based systems with 99%+ accuracy.
Regulatory Compliance Automation
Automate AML, KYC, GDPR, PSD2 data collection, transformation, validation, and reporting — reducing analyst hours and errors.
Core Banking Modernization
Cloud-native microservices enable open banking API readiness, faster product delivery, and seamless fintech integration.
Tailored Solutions for Financial Services
What Clients Achieve
How We Work
Fraud Detection Assessment
Analyse existing fraud rules, false positive rates, transaction data quality; identify highest-value detection gaps.
ML Model Development
Build and train adaptive fraud detection models on historical transaction and fraud data; validate against held-out test set.
Regulatory Data Mapping
Map source systems to regulatory reporting requirements (AML, KYC, GDPR, Basel III); design ETL pipelines with validation rules.
Core System Modernization
Decompose monolithic core systems into microservices; migrate to cloud-native architecture with API gateway and CI/CD.
Compliance & Governance
Deploy fraud detection and regulatory reporting systems; establish model monitoring, audit logging, and regulatory sign-off.
Mid-Sized Bank: 99.2% Fraud Detection, 60% Cost Reduction
A regional bank was losing $3M annually to fraud and spending 400 analyst hours monthly on manual AML reporting. We deployed AI fraud detection to catch evolving patterns rule engines missed, and built regulatory reporting automation for GDPR, AML, KYC. Within 4 months: fraud detection improved from 85% to 99.2%, false positives dropped 70%, and compliance reporting effort fell 60%.
Common Questions
Industry-specific insights for financial services leaders.
Our AI fraud detection models consistently achieve 99%+ accuracy on client production data — compared to 85–90% for well-maintained rule-based systems. More importantly, AI dramatically reduces false positive rates (legitimate transactions incorrectly flagged as fraud), which is a major cost driver in financial services. Rule-based systems typically generate 50–200× more false positives than well-tuned ML models. AI also detects novel fraud patterns that rule engines miss entirely, since rules can only catch fraud that has been seen before.
Our regulatory data platforms are designed for extensibility across frameworks. We have built reporting automation for AML/CTF (FinCEN, FATF), KYC/CDD, GDPR (Article 30 records of processing, breach notification), PSD2 (transaction monitoring, strong customer authentication), Basel III/IV (capital adequacy, LCR, NSFR), IFRS 9 (expected credit loss provisioning), and various jurisdiction-specific reporting requirements. The platform is built on a flexible data model that allows new reporting requirements to be added as frameworks evolve.
We use the Strangler Fig migration pattern — building cloud-native services in parallel with the legacy core, redirecting individual business capabilities one at a time as they are validated in production. This approach has zero forced downtime and allows rollback at any granular step. For core banking specifically, we typically start with peripheral services (notifications, document management, reporting) before migrating core transactional capabilities, giving teams time to build confidence in the new architecture before migrating the highest-risk components.
A focused fraud detection model deployment typically takes 8–12 weeks from data assessment to production. A regulatory reporting automation platform for 2–3 frameworks runs 10–16 weeks. Full core banking modernisation is an 8–18 month programme depending on system complexity, number of products, and regulatory dependencies. We provide a bounded estimate after a two-week discovery sprint — so you have full scope clarity before committing to the full programme.
Financial data requires the highest security standards, which we deliver without exception. We implement PCI-DSS compliance for payment card data, SOC 2 Type II controls for operational security, end-to-end encryption for all data in transit and at rest, tokenisation for sensitive financial identifiers, and immutable audit logs for all data access events. All work is performed within your regulated cloud environment (on-premises, or on AWS/Azure/GCP regions matching your data residency requirements). Team members undergo background checks and sign comprehensive NDAs before any data access.
Ready to Transform Financial Services?
Book a consultation to explore how AI, data, and technology can unlock growth.