GraphJIraaf transforms tabular banking data into high-performance relationship maps. We specialize in identifying non-obvious risk patterns and hidden customer value that traditional RDBMS cannot detect.
Legacy banking systems are limited by rows and columns. Fraudsters and complex corporate entities operate in networks. Our graph-native approach allows your institution to query deep-link relationships in milliseconds.
Tailored consultancy from initial schema design to production-ready fraud engines.
Translation of traditional relational schemas into high-performance Labeled Property Graphs (LPG). Optimized for Neo4j and TigerGraph.
Learn more →Specialized patterns for detecting Credit Card Bust-out, Circular Transfers, and First-Party Fraud using Centrality algorithms.
Learn more →Resolve disparate customer data across retail and corporate silos into a single, unified knowledge graph.
Learn more →Assessing current data interconnectedness.
Designing the optimal graph schema.
Seamless ETL/ELT pipeline deployment.
Staff training and handover.