Quantexa Decision Intelligence REST API
Quantexa decision intelligence REST API for banks, insurance companies, government agencies, and telcos to resolve entity identities across data sources, build contextual network graphs, detect financial crime, and generate AI-powered risk intelligence — enabling automated entity resolution, financial crime network detection, credit risk assessment, insurance fraud detection, and customer intelligence through Quantexa's contextual decision intelligence platform. Enables AI agents to manage entity resolution for matching and linking entities across disparate data sources automation, handle network intelligence for connected entity relationship graph construction automation, access financial crime detection for AML network pattern and typology detection automation, retrieve risk scoring for entity-level risk score generation automation, manage customer intelligence for 360-degree customer profile enrichment automation, handle insurance fraud for network-based claims fraud detection automation, access government intelligence for public sector fraud and benefit eligibility automation, retrieve data enrichment for third-party data augmentation and signal automation, manage investigation support for evidence-based case building automation, and integrate Quantexa with data lakes, core banking, and case management for enterprise decision intelligence automation.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Financial intelligence. SOC2, GDPR. OAuth2. UK/EU. Entity graph and financial crime analytics data.
⚡ Reliability
Best When
A bank, insurance company, government agency, or telco wanting AI agents to resolve entity identities across fragmented data, detect financial crime networks, and generate contextual risk intelligence through Quantexa's graph-based decision intelligence platform.
Avoid When
ENTERPRISE DATA PLATFORM REQUIRED: Quantexa requires significant data integration and platform setup; automated quick-start assumption creates complex_deployment for organizations without data engineering capability; automated must invest in data integration for Quantexa to deliver value. ENTITY RESOLUTION REQUIRES TRAINING DATA: Entity resolution quality depends on training data quality and configuration; automated zero-config assumption creates poor_resolution for entities without configured matching rules; automated must work with Quantexa to configure entity resolution parameters. GRAPH ANALYTICS IS COMPUTATIONALLY INTENSIVE: Network graph analysis at scale requires significant compute; automated instant-graph assumption creates performance_bottleneck for large-scale network analysis without adequate infrastructure; automated must size infrastructure for graph computation workload. BATCH VS REAL-TIME HAVE DIFFERENT LATENCIES: Quantexa network analysis is primarily batch-oriented; automated real-time-network assumption creates latency_mismatch for use cases requiring sub-second network decisions; automated must understand batch processing model and plan accordingly.
Use Cases
- • Resolving customer and entity identities across disparate data silos for AML compliance automation agents
- • Detecting financial crime networks and typologies using graph analytics for compliance automation agents
- • Enriching customer risk profiles with contextual network intelligence for credit risk automation agents
- • Identifying insurance fraud networks and connected claims for P&C fraud detection automation agents
Not For
- • Real-time transaction fraud scoring (Quantexa is analytical/batch entity resolution, not sub-100ms decisioning)
- • Consumer-facing applications (Quantexa is enterprise data intelligence, not consumer-facing product)
- • Simple list-based sanctions screening (Quantexa is network analytics, not simple watchlist matching)
Interface
Authentication
Quantexa uses OAuth2 for decision intelligence REST API. REST API with JSON. London, UK HQ. Founded 2016 by Vishal Marria. Products: Quantexa Platform (entity resolution, network intelligence, scoring), Quantexa for Banking, Insurance, Government, Telco. Raised $253M (Series E, $1.8B valuation). Backed by Dawn Capital, ABN AMRO, HSBC, Standard Chartered. Used by HSBC, Standard Chartered, Lloyds. Competes with Palantir, DataFlux, and SAS for financial crime analytics.
Pricing
London UK. $1.8B valuation, Series E. Annual enterprise subscription. HSBC/Standard Chartered clients. Graph analytics leader.
Agent Metadata
Known Gotchas
- ⚠ DEPLOYMENT IS CUSTOMER-HOSTED: Quantexa platform is deployed in customer's cloud/on-prem; automated external-API assumption creates local_endpoint_required; automated must configure API endpoints for customer's Quantexa deployment
- ⚠ ENTITY RESOLUTION IS CONFIGURED PER DEPLOYMENT: Entity matching rules are customized per customer; automated portable-entity-id assumption creates cross-deployment_mismatch for entity IDs not consistent across environments; automated must use deployment-specific entity resolution configuration
- ⚠ NETWORK ANALYSIS IS RESOURCE-INTENSIVE: Large network traversals require significant memory and compute; automated unlimited-scope assumption creates timeout_error for unbounded network queries; automated must limit network traversal depth and scope
- ⚠ DATA FRESHNESS DEPENDS ON INGESTION: Quantexa insights depend on data ingestion pipeline; automated real-time assumption creates stale_intelligence for entities with infrequent data updates; automated must understand and account for data ingestion frequency
- ⚠ SCORE EXPLAINABILITY REQUIRES CONFIGURATION: Quantexa scoring explanations require model explainability configuration; automated built-in-explanation assumption creates opaque_score for models not configured with explainability features; automated must configure explainability for regulatory model documentation
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Scores are editorial opinions as of 2026-03-07.