Traditional banking infrastructure struggles to keep up with the demands of speed, personalization, and security. We designed a core-banking architecture around three ideas: a transaction engine built for correctness under load, a data model that captures relationships rather than rows, and AI that turns that data into useful insight.
The system pairs TigerBeetle for high-throughput, strongly-consistent transactions with a Neo4j knowledge graph for relationship-aware analytics and fraud detection.
High-performance transactions with TigerBeetle
At the core sits TigerBeetle, an open-source distributed accounting database engineered for fast, fault-tolerant transaction processing. Unlike monolithic ledgers that buckle under scale, it is built to handle very high transaction volumes while guaranteeing strong consistency — essential where even a small inconsistency is unacceptable.
Its distributed design scales horizontally, absorbing increasing volume without sacrificing correctness, which makes it a strong foundation for institutions that need both high availability and ledger-grade accuracy.
Relationship-aware data with knowledge graphs
Rather than store isolated records, we modeled the domain as a knowledge graph in Neo4j. Representing customers, accounts, transactions, and behaviors as connected entities unlocks analysis that relational tables handle poorly:
- Fraud detection: surfacing unusual patterns and connections between accounts that indicate suspicious activity.
- Personalized service: a connected view of each customer enabling tailored products and recommendations.
- Advanced analytics: multi-hop queries across entities to reveal risk and behavior signals that are expensive or impossible in SQL.
Intelligent financial insights
On top of the ledger and graph, machine-learning models translate raw activity into real-time insight — financial-health analysis, personalized recommendations, and proactive guidance — improving the customer experience while sharpening the institution's view of risk and opportunity.
Streamlined KYC and compliance
Modern Know Your Customer (KYC) flows combine biometric verification with AI-assisted fraud detection to verify identity in real time. Automating verification reduces onboarding friction for customers while strengthening compliance with AML and CFT requirements.
The technology stack
- TigerBeetle — distributed, strongly-consistent transaction processing.
- Neo4j knowledge graph — relationship-aware analytics and fraud signals.
- AI/ML insight layer — personalized, real-time financial analysis.
- Cloud infrastructure — elastic scale, security, and availability.
Outcomes
Graph-based detection cut fraud incidents by roughly 40%, transaction processing got materially faster, and the platform gained a foundation for personalized, real-time banking experiences.
The result is a core-banking platform that is faster, more intelligent, and more secure — agile enough to meet the demands of a digital-first world and extensible enough to grow with the institution.