Investigation Analytics and Insights

Overview#

A financial intelligence supervisor reviewing the week's caseload notices something odd: resolution times have climbed 18% over the past month, but no single analyst's numbers look alarming. Without aggregated analytics, that trend stays invisible until it becomes a compliance issue. The Analytics and Insights module surfaces exactly that kind of signal, turning raw case data into operational intelligence that leadership can act on before problems compound.

Designed for compliance leadership, financial intelligence units, and fraud investigation management teams, the module combines real-time dashboards, machine learning models, and behavioural analytics to move investigations from reactive triage to proactive management.

Key Features#

Real-Time Intelligence Dashboards: Surfaces critical investigation insights through interactive visualisations, enabling analysts to monitor case progress, risk distributions, and team performance at a glance.
Predictive Analytics: Machine learning models forecast investigation outcomes, identify high-risk cases early, and recommend resource allocation based on historical patterns and current case characteristics.
Heatmap Visualisations: Temporal pattern analysis reveals entity activity patterns, transaction concentration periods, and behavioural anomalies through intuitive visual representations.
Connection Strength Analysis: Quantifies relationship risk levels between entities using graph analysis, helping investigators prioritize the most significant connections in complex networks.
False Positive Reduction: ML models learn from analyst decisions to improve alert accuracy over time, reducing the volume of low-value alerts and allowing teams to focus on genuine threats.
Source Distribution Analytics: Automatically identifies data gaps in evidence collection, ensuring investigation thoroughness and highlighting areas requiring additional research.
Investigation KPI Tracking: Monitors key performance indicators including case resolution times, evidence collection rates, quality scores, and team productivity metrics.
Trend Analysis and Pattern Detection: Identifies emerging investigation patterns, seasonal trends, and systemic risks across the investigation portfolio to support strategic decision-making.
Customisable Report Generation: Produces automated analytics reports for management review, regulatory submissions, and operational planning with configurable metrics and time periods.

Use Cases#

Portfolio Risk Management: Compliance leadership uses analytics dashboards to monitor the overall risk profile of active investigations, identify resource bottlenecks, and make data-driven allocation decisions.
Alert Optimisation: Machine learning models analyse historical analyst decisions to refine alert scoring, reducing false positives while maintaining detection sensitivity for genuine threats.
Operational Efficiency Tracking: Management teams monitor investigation throughput, average resolution times, and quality scores to identify process improvements and training opportunities.
Behavioural Pattern Detection: Temporal analytics reveal coordinated activity patterns, unusual transaction timing, and behavioural anomalies that indicate potential financial crime.
Regulatory Reporting: Automated generation of compliance metrics, investigation statistics, and trend analyses for regulatory examination preparation and ongoing reporting obligations.
Strategic Planning: Historical analytics and predictive models inform hiring decisions, technology investments, and process improvements based on projected caseload trends.

Integration#

The Analytics and Insights module integrates with the investigation platform's case management, entity resolution, and transaction monitoring systems to aggregate data from multiple sources. Dashboards support role-based access controls, ensuring analysts, supervisors, and executives see appropriate metrics. Export capabilities enable integration with enterprise business intelligence tools and regulatory reporting systems.

Open Standards#

GraphQL (June 2018 specification): All analytics queries, mutations, and dashboard data are served through a GraphQL API, enabling typed, composable data fetching for investigation metrics, predictive model results, and KPI aggregations.
ISO 8601 / RFC 3339: All timestamps throughout the analytics and KPI modules are expressed as UTC ISO 8601 strings; the platform validates and parses them strictly, rejecting malformed values before they reach the database.
OAuth 2.0 (RFC 6749) / Bearer Token (RFC 6750): Access to every analytics endpoint is gated on scoped bearer tokens (e.g. kpi:read), with token claims verified against the platform's RS256 JWKS endpoint before data is returned.
JSON Web Token (RFC 7519): RS256-signed JWTs carry the identity and permission scopes used to authorise analytics requests and to enforce per-organisation data isolation across all KPI and dashboard routes.
OpenAPI 3.x: The REST KPI routes are described by an auto-generated OpenAPI 3.x schema (served at /openapi.json), providing a machine-readable contract for client integration and regulatory tooling.
AMPDS (Advanced Medical Priority Dispatch System): KPI SLA targets are structured around the five AMPDS acuity bands (Echo, Delta, Charlie, Bravo, Alpha), with per-band response-time and coverage thresholds aligned to the Irish HSE National Ambulance Service standard.
WebAssembly (W3C): Client-side Python analytics scripts are executed in-browser via Pyodide, a WebAssembly-compiled CPython runtime; execution sessions and code hashes are persisted and auditable.
JSON (RFC 8259): All analytics payloads, LLM analysis results, custom metric definitions, and report exports are serialised as JSON, ensuring interoperability with enterprise business intelligence and regulatory reporting systems.

Last Reviewed: 2026-02-23 Last Updated: 2026-04-14