Predictive Analytics and Machine Learning

Overview#

A law enforcement command unit wants to know where to pre-position resources on Friday night, not just react to where incidents happened last Friday. A financial crime risk team needs to flag suspicious transactions before settlement, not review them after the fact. A public health authority tracking disease vectors needs to see where an outbreak is heading, not just where it has been. Argus Predictive Analytics exists for exactly these scenarios: turning historical investigation data, real-time signals, and contextual factors into actionable predictions that give operators a window ahead of the present moment.

The analytics engine is purpose-built for law enforcement, financial crime, and public safety workflows. It combines explainable model outputs with continuous feedback loops, so predictions improve over time and operators can always understand why a score came out the way it did.

Open Standards#

• GraphQL (June 2018 specification): all predictive analytics queries, mutations, and subscriptions are exposed through a typed GraphQL API, enabling interoperable access to forecasts, risk scores, and model outputs from any compliant client.
• GeoJSON (RFC 7946): atmospheric dispersion results and spatial risk zones are serialised as GeoJSON Polygon features, and weather-station responses are requested with the application/geo+json media type for standards-compliant geospatial exchange.
• OASIS Common Alerting Protocol (CAP 1.2): the weather-alert subsystem ingests CAP-format alerts from NOAA NWS and Environment Canada, normalising severity, certainty, and urgency fields per the CAP enumeration scheme.
• ISO 55000 / BSI PAS 55: the predictive maintenance scheduler explicitly aligns scheduled work orders and maintenance KPIs with the ISO 55000 asset-management family and its British precursor PAS 55.
• IEEE C57.104: transformer dissolved-gas analysis (DGA) and dielectric-strength maintenance procedures within the predictive maintenance scheduler are defined per this IEEE standard for interpretation of gases in oil-immersed transformers.
• Pasquill-Gifford Atmospheric Stability Classification: the Gaussian-plume dispersion model uses the internationally recognised Pasquill-Gifford stability-class scheme (classes A to F) to determine lateral and vertical dispersion coefficients for hazmat and CBRN threat prediction.
• ARIMA / SARIMAX (Box-Jenkins methodology): demand-forecasting and temporal-prediction services implement the AutoRegressive Integrated Moving Average family of statistical time-series models, an internationally standardised methodology for sequential data forecasting.

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

Key Features#

• Deep Learning and Neural Networks: Multi-layer architectures for complex pattern recognition across text, images, audio, behavioural data, and temporal sequences
• Ensemble Prediction Models: Combined algorithms delivering production-grade predictions with confidence scoring and feature attribution for transparent decision-making
• Continuous Learning: Models that improve accuracy through feedback loops, adapting to emerging threat landscapes and evolving patterns without manual retraining
• Real-Time Scoring: Sub-second prediction delivery for operational deployment in live investigation, fraud detection, and threat assessment workflows
• Explainable AI: Transparent model decisions with confidence scoring, feature importance rankings, and human-readable explanations supporting legal and compliance requirements
• Temporal Pattern Recognition: Time-series forecasting for threat prediction, crime pattern analysis, and resource demand planning using historical and real-time data
• Graph-Based Entity Resolution: ML-powered deduplication and identity resolution across fragmented data sources for accurate entity profiles
• Adaptive Risk Scoring: Dynamic risk models that adjust to emerging threat landscapes, new data sources, and evolving criminal methodologies

Use Cases#

• Crime Prediction and Prevention: Forecast crime hot spots, predict high-risk time periods, and recommend proactive patrol deployment based on historical patterns, environmental factors, and real-time signals
• Financial Fraud Detection: Identify fraudulent transactions, detect money laundering patterns, and score risk levels in real-time across banking, insurance, and procurement operations
• Threat Assessment: Evaluate threat levels for persons, organisations, and locations by analysing behavioural patterns, communication networks, and intelligence indicators
• Resource Optimisation: Predict staffing needs, call volumes, and equipment requirements to support agency resource allocation and budget planning decisions
• Recidivism and Risk Prediction: Assess reoffence risk for supervision and sentencing support using evidence-based models with transparent scoring methodologies

Integration#

The platform integrates with investigation management, alert systems, CAD/RMS, financial analysis tools, and OSINT intelligence modules within Argus. Models consume data from multiple sources and deliver predictions through dashboards, API endpoints, and automated alert workflows. All predictions are scoped to the organisation and subject to RBAC access controls.