Advanced Analytics Engine

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.

Diagram

flowchart LR
    A[Investigation Data] --> B[Custom Metrics Engine]
    A2[Alert & Evidence Data] --> B
    B --> C[Statistical Analysis]
    B --> D[Pattern Recognition]
    C --> E[Trend Detection]
    D --> F[Anomaly Flags]
    E --> G[Dashboard Layer]
    F --> G
    G --> H[Line / Bar / Pie Charts]
    G --> I[Heatmaps & Tables]
    G --> J[Metric Cards]
    H --> K[GraphQL Subscription Feed]
    I --> K
    J --> K

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.

Last Reviewed: 2026-02-05 Last Updated: 2026-04-14

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