From raw transaction data to explainable, auditable decisions — purpose-built for organizations where financial integrity, operational accountability, and regulatory compliance demand more than a black box.
3
ML Model Tiers
3
Explainability Levels
3+
Cloud Providers
✓
No-Code Config
End-to-end coverage from detection to decision — each pillar purpose-built for the demands of operational risk management.
Machine learning models that score every transaction against learned behavioral baselines — with configurable thresholds, post-processing rule overrides, and a granular feature contribution trail for every flagged record.
Multi-model ensemble forecasting across any time-series dimension — revenue cycles, claim frequencies, freight volumes — with probabilistic confidence intervals and trend explainability at the series and portfolio level.
Investigation workflows built for analyst accountability: review flagged transactions, record decisions, escalate cases, and maintain an immutable record of every human judgment — with full AI-context at the point of review.
Each studio is a self-contained module. Together they form a complete operational risk intelligence pipeline — from raw data to final decision.
Ingest, validate, cleanse, and engineer features from any transaction schema. Configurable field mappings, cleansing rules, and derived feature expressions — no code required.
Train tiered ML models, manage the model registry, and configure thresholds. From local ensemble models to cloud-hosted endpoints on AWS SageMaker, Azure AI Foundry, and GCP Vertex AI.
Explore anomaly scores, forecast curves, and transaction flow visualizations. AI explainability at three levels — individual transaction, case summary, and portfolio — powered by GPT-4o mini.
Structured investigation workflows with analyst review, decision labeling, and full case accountability. Every action timestamped and queryable.
Explainability, human oversight, and auditability aren't features you add later — they're woven into every layer of the platform.
Every detection is explainable. At the transaction level: which features drove the score. At the case level: what the investigation pattern means. At the portfolio level: what today's results say about systemic risk. Powered by GPT-4o mini, grounded strictly in your data.
Analysts remain in control. Every flagged transaction can be reviewed, labeled, and escalated through a structured workflow. Model outputs inform human judgment — they don't replace it. Analyst decisions feed back into the training and tuning cycle.
Every AI explanation is stored with the full prompt used to generate it. Every analyst decision is timestamped and attributed. Every model version is versioned and retrievable. This is the foundation for regulatory defensibility.
Domain-specific feature engineering expressions, cleansing rules, anomaly thresholds, and field mappings are all configurable through the admin interface — without code changes or redeployment. Different clients, different rules, zero engineering overhead.
A tiered, provider-agnostic architecture that scales from local models to managed cloud endpoints — without rewriting your detection logic.
Local · Deterministic
IsolationForest · AutoRegression
Local · Ensemble
Outliers 6-detector · Darts 3-model
Cloud · Managed
SageMaker · Azure AI · GCP Vertex
The provider abstraction layer means training dispatch is cloud-agnostic. Swap providers without touching detection logic.
AWS SageMaker
Train & host RCF, DeepAR, and custom models via managed endpoints
Azure AI Foundry
Leverage Azure ML pipelines and registered model endpoints
GCP Vertex AI
Deploy to Vertex AI endpoints with AutoML and custom training
MLflow-compatible model registry. Not married to any provider — add or swap endpoints via the provider registry.
Schema-agnostic ingestion with configurable field mappings per org and domain. Cleansing and feature engineering pipelines execute from DB-stored rule definitions — no code deployment required for new clients.
Visualize transaction volumes and fund flows between domains, accounts, and channels as an interactive Sankey diagram. Instantly surfaces concentration risk, unusual routing, and domain-level volume anomalies that tabular data hides.
Every capability in TransIQ is exposed through a documented REST API — built to OpenAPI 3.1 specification. Integrate detection, forecasting, case management, and configuration into your own workflows, BI tools, and orchestration platforms without touching the UI.
OpenAPI 3.1 Specification
Interactive Swagger UI and ReDoc documentation with request/response schemas and examples for every endpoint.
Dual Authentication
JWT Bearer tokens for SaaS users. X-API-Key header for API-only programmatic access. Both support org-scoped access control.
Role-Based Access Control
Admin, Analyst, and Viewer roles enforced at the API layer — not just the UI. API keys carry scoped permissions.
Fully Multi-Tenant
Every endpoint is scoped to an org slug. One platform, many clients — with strict data isolation at the API and database layers.
# Authenticate with API key
curl -H "X-API-Key: tiq_live_••••••••"
-H "Content-Type: application/json"
https://api.transiq.app/orgs/acme-bank/
results/anomalies
# Run batch inference
POST /orgs/{slug}/results/batch-infer
# Generate AI explanation
POST /orgs/{slug}/explain/anomaly/{id}
# Manage org members & API keys
POST /orgs/{slug}/members
POST /orgs/{slug}/api-keys
# Full OpenAPI docs at:
GET /api/docs → Swagger UI
GET /api/redoc → ReDoc
The same intelligence platform, configured for the specific domains, field schemas, and risk patterns of your sector.