Overview#
A financial intelligence unit tracking money flows through a dozen shell companies needs more than a report template. They need an engine that computes statistical significance, flags deviations from baseline, and generates a narrative an analyst can brief from. The Advanced Analytics Engine provides exactly that: custom metric definition, real-time calculation, multi-dimensional dashboard orchestration, and historical trend analysis, all operating against the platform's PostgreSQL primary data store with full multi-tenant isolation.
Unlike basic reporting tools, this engine provides deep analytical capabilities that include statistical analysis, pattern recognition, and predictive modelling, directly within investigative workflows and without the cost of a separate BI platform.
Key Features#
Custom Metrics Engine#
Build metrics tailored to your organisation's specific analytical needs. Supported types include COUNT, SUM, AVERAGE, MIN/MAX, PERCENTAGE, and CUSTOM formulas combining multiple data sources. Aggregation periods range from hourly operational monitoring through yearly performance reviews.
Real-Time Dashboard Architecture#
Create interactive, collaborative dashboards with drag-and-drop layout, role-based access, public transparency portals, and live data refresh via GraphQL subscriptions. Widget types include line charts, bar charts, pie charts, tables, metric cards, and heatmaps, each with drill-down capabilities. Multi-tenant isolation ensures dashboards respect organisation boundaries throughout.
Historical Trend Analysis#
Track metric evolution over time with unlimited history retention, point-in-time analysis, automatic trend detection, seasonality recognition, anomaly detection, and year-over-year comparisons.
Statistical Analysis#
Built-in descriptive statistics cover mean, median, mode, standard deviation, percentile rankings, and quartile distributions. Inferential analytics include confidence intervals, trend significance testing, correlation coefficients, and growth rate projections.
Pattern Recognition#
Automatic identification of temporal patterns such as peak activity detection and seasonal variation, behavioural patterns covering investigator productivity and case complexity, and anomaly identification for statistical outliers, unexpected deviations, and rare event detection.
Predictive Capabilities#
Case volume prediction, resource planning, budget projections, and risk scoring. Export metric data for external machine learning model training and import prediction results back as custom metrics.
Use Cases#
- Law Enforcement: Clearance rate optimisation, resource allocation analysis, evidence backlog management, disclosure compliance tracking, crime pattern analysis, and community transparency dashboards.
- Financial Crime Analytics: AML alert efficiency metrics, transaction monitoring volumes, risk score distributions, fraud investigation velocity, and regulatory compliance reporting.
- Corporate Security: Incident response metrics covering MTTD and MTTR, vulnerability management, supply chain risk analysis, behavioural analytics, and executive dashboards.
- Government and Intelligence: Threat actor tracking, intelligence collection metrics, analytical throughput, and cross-agency collaboration analysis.
Integration#
Full API access for enterprise integrations covers programmatic dashboard management, widget configuration, metric calculation, and data export. Cross-domain intelligence connects investigations, alerts, evidence management, court filings, threat intelligence, blockchain analytics, maritime and aviation tracking, and command centre operations. Real-time streaming is delivered through GraphQL subscriptions, with Kafka Streams handling high-volume event ingestion from connected data sources.
Open Standards#
- GraphQL (June 2018 specification): all metric queries, dashboard mutations, and live data updates are served through a GraphQL API, with real-time delivery implemented via GraphQL Subscriptions over the server-push channel.
- WebSocket (RFC 6455): the underlying transport layer for GraphQL subscriptions and for server-side broadcast of analytic events to connected dashboard clients.
- JSON (RFC 8259): the canonical interchange format for metric configurations, widget layouts, filter expressions, event payloads, and all API request and response bodies.
- ISO 8601 / RFC 3339: all timestamps across metric calculations, trend history, period boundaries, and event records are serialised as UTC-normalised ISO 8601 strings.
- OAuth 2.0 Bearer Token (RFC 6750) / JWT (RFC 7519): every analytics endpoint enforces JWT Bearer authentication, with tenant isolation derived from the organisation identifier claim in the token.
- OpenAPI 3.0 (OAS 3.0.3): the platform publishes a machine-readable OpenAPI 3.0 description of all REST endpoints, including programmatic dashboard and metric management surfaces.
- SQL (ISO/IEC 9075): the calculation engine and repository layer execute parameterised SQL against a PostgreSQL primary store, using standard SQL aggregation, window functions, and common table expressions for statistical computations.
Last Reviewed: 2026-02-05 Last Updated: 2026-04-14