Investigation Timeline Tracking

Overview#

The order in which events happened is often the investigation. A fraudulent wire transfer means little by itself. Place it in sequence with a phishing email three days earlier, a credential compromise on the same morning, and a funds withdrawal within ninety minutes of the transfer, and a clear attack sequence emerges. The Timeline Tracking module reconstructs those sequences automatically, ingesting events from dozens of data sources, normalising time zones, and applying temporal correlation algorithms to reveal patterns that only become visible when the data is arranged chronologically.

The module serves financial fraud investigators reconstructing attack sequences, AML analysts identifying layering patterns in transaction timing, law enforcement units validating or challenging alibis, and counter-terrorism analysts mapping coordination between events across geographies and timeframes.

Key Features#

Multi-Source Event Capture: Events are ingested from dozens of distinct source types including financial systems, communications platforms, location services, digital forensics tools, and physical evidence systems, with automatic time zone normalisation and duplicate detection.
Temporal Correlation Engine: Multiple analysis algorithms including sequential pattern mining, temporal clustering, causality detection, synchronisation analysis, and frequency pattern recognition identify relationships between seemingly unrelated events.
Gap Detection and Analysis: Automated identification of unexplained time periods between related events highlights missing information requiring investigation, with configurable gap thresholds based on investigation context.
Pattern Recognition: Recurring temporal sequences indicating coordinated activity, systematic behaviour, or operational patterns are automatically detected and highlighted, with configurable time windows from seconds to weeks.
Event Confidence Scoring: Each event receives reliability ratings based on source type, validation status, and corroboration from other sources, enabling investigators to assess timeline accuracy and identify potential fabrications.
Velocity Analysis: Physically impossible scenarios such as location changes faster than travel time or document creation speeds exceeding human capability are automatically flagged as anomalies.
Interactive Timeline Visualisation: Visual timeline views with zoom, pan, filtering, and layering enable investigators to explore event sequences across multiple entities and time scales simultaneously.
Cross-Investigation Correlation: Timeline events are compared across multiple investigations to identify coordinated activities, shared participants, or common temporal patterns spanning separate cases.
Anomaly Detection: Timing deviations from established patterns or expected sequences are highlighted, identifying unusual behaviour changes that may indicate criminal activity transitions or operational shifts.

Use Cases#

Financial Fraud Timeline Reconstruction: Investigators reconstruct wire fraud attack sequences from phishing emails through unauthorized access, fraudulent transfers, and detection events to establish precise incident timelines for prosecution.
Money Laundering Pattern Analysis: Temporal correlation reveals layering patterns in transaction sequences, identifying recurring timing signatures in fund movements across accounts and jurisdictions.
Alibi Validation: Cross-referenced timelines from location data, communications records, and transaction logs confirm or refute witness statements and subject claims with timestamped evidence.
Coordinated Activity Detection: Synchronisation analysis identifies events occurring simultaneously across different entities or locations, revealing conspiracy and coordinated criminal networks.
Evidence Corroboration: Timestamp cross-referencing across independent data sources strengthens or weakens evidence reliability, exposing inconsistencies and potential document fabrication.
Investigation Briefing: Visual timeline exports provide clear chronological narratives for case briefings, regulatory filings, and court presentations, communicating complex event sequences effectively.

Integration#

The Investigation Timeline Tracking module integrates with the platform's case management, evidence management, entity resolution, and graph analysis systems. Events captured during investigations automatically appear on case timelines, and timeline analysis findings feed into entity profiles and relationship graphs. The module connects to external data sources through pre-built connectors for financial systems, communications platforms, location services, and digital forensics tools, with timeline visualisations embedded in investigation interfaces and reports.

Open Standards#

ISO 8601 / RFC 3339 (UTC timestamps): All timeline events are stored and exchanged as UTC-normalised ISO 8601 datetimes; the ingestion pipeline parses multi-format source timestamps and normalises them to UTC before correlation, satisfying the cross-jurisdiction temporal alignment requirement.
GraphQL (June 2018 specification): The entire timeline API surface is exposed via a Strawberry GraphQL schema, enabling structured queries, mutations, and real-time subscriptions for events, tracks, layers, bookmarks, and state across investigation interfaces.
W3C Verifiable Credentials Data Model v2.0: Evidence items surfaced on the timeline carry Ed25519-signed W3C VC DM v2.0 credentials issued for provenance and chain-of-custody transfers, enabling court-admissible verification of event source integrity.
OASIS STIX 2.1: Threat indicators and intelligence reports ingested from STIX 2.1 bundles carry valid_from and created_at temporal fields that are mapped into investigation timelines, linking adversary-activity observations to the chronological event sequence.
Plaso / log2timeline (open-source forensics standard): A dedicated integration ingests Plaso-generated super-timelines from digital forensics sources, surfacing file-system, registry, browser, and log artefacts as typed timeline events within the investigation view.
W3C SCXML (State Chart XML): The investigation lifecycle state machine governing which states (e.g. DRAFT, ACTIVE, CLOSED) generate audit-bound timeline events is implemented in conformance with W3C SCXML via XState v5, ensuring deterministic, auditable state transitions.
OAuth 2.0 / JWT (RFC 6749 / RFC 7519): Timeline access control, including shared-timeline permissions and investigation-scoped sharing, is enforced via JWT bearer tokens issued under OAuth 2.0 flows, ensuring only authorised principals can read or modify timeline data.

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