TAO TrueClaim™
Business
AD
Total Claims (filtered)
—
In period
Flagged (model)
—
RED band share
Reviewed
0
By adjusters
Confirmed fraud (reviews)
0
From reviewed only
Claim ID CREATED DATE Risk Score Risk Band Human Label Assigned to Status
Dashboard
Monitor fraud detection performance and operational health.
📅 Filter by Created Date:
General Overview
Triage & Operations
TAO Trees Model Performance
Confirmed by claim adjuster
Human-verified fraud
Fraud count
0
Automatic by model
Model-flagged (RED) fraud
RED-flagged count
0
Daily high-risk flags (model) vs confirmed fraud (adjuster)
Total claims reviewed
0
Reviewed red-flagged
0
Reviewed amber
0
Reviewed green
0
Claims Reviewed by Claim Adjuster
HITL agreement by model band (reviewed vs agreed)
Average Review Time by Adjuster
Model performance vs ground truth (from claim adjuster reviews)
Based on reviewed claims only. Binary: model RED = fraud, model GREEN/AMBER = legit.
Precision (Hit Rate)
—
TP / (TP + FP)
Recall (Sensitivity)
—
TP / (TP + FN)
True positives (TP)
0
HITL Fraud, model RED
False negatives (FN)
0
HITL Fraud, model not RED
Confusion matrix
Actual →Fraud / Legit
Fraud
Legit
Pred RED
0
TP
0
FP
Pred not RED
0
FN
0
TN
Unreviewed claims: model triage
Pending human review by model band. When there are none, we still show “0 pending reviews”.
Unreviewed by model band (count)
Counts by band
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📋 Claim Summary
🟢 GREEN — Low risk
🔍 Explainability Decision tree path · leaf peer analysis
🟢 GREEN — Low risk
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🌳 Decision Path — triggered rule path; each step shows the condition and this claim's feature values
💬 Ask about this decision
🕸️ Leaf Peer Radar — all features analyzed, each line = one claim reaching this leaf
Fraudulent claim
Non-fraudulent claim
✏️ Human-in-the-loop correction

Provide the correct label and a short justification. This will be used for model retraining.

🔗 Similar claims (same node)

Claims that followed the same path and reached the same leaf node.

Select Dataset
Dataset Overview

Select a dataset to view its information

Feature Schema

Click a feature row to open its distribution and binning logic.

Feature Name Type Missing Example Values Boundaries

Feature Distribution

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Current Fraud Rate
8.2%
Fraud Rate Trend
Model Performance Metrics
Data Drift (MVP)
Status: — Last run: —
Rows analyzed
—
Warnings
—
Critical
—
Distribution (reference vs current)
Top drifting features
Feature Layer Metric Value Missing (prod) Status
Run a drift check to see results.
Registered Models
Open Model Registry to load deployments.
Test Model with Custom Inputs

Same features as in the claims system. Fill the fields and click Score This Claim.

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Quick Test generates realistic sample values from the deployed model schema, scores the claim, and shows the leaf rule.

Recent Activity
Rule Catalogue (Deployed Models)
TAO outcome
Risk band
Bands reflect the selected deployment scoring mode.
Rule Catalogue Playground

Build your own fraud rule system from the models trained on your data: check the trainings and models you want to explore, pick the rules you like from their fraud rule library (and add your own), compose your own set, evaluate it on unseen holdout data, then deploy it as a live rule set that scores incoming claims.

1 Select trainings & models
2 Compose: pick rules + add your own
3 Evaluate Model Composed on test
4 Deploy for live claim scoring
1Select Trainings & Models Check one or more trainings, then check the models inside them whose rules you want in your library — expand a model for its metrics, tree and business tree
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2Fraud Rule Library

Every rule starts unselected. Check the rules you want, then click Add selected to Model Composed — rules are grouped below by the model that produced them (each model has its own color).

Check at least one model above to load its fraud rules.
2Build Your Own Rule Combine dataset features into a custom fraud rule and test it on the holdout set
No conditions yet — every condition is combined with AND.
3Model Composed
Check rules in the library above and click "Add selected to Model Composed", or use Auto-Generate.
4Deploy Rule Set Publish your selection as a named rule set — it appears in the Model Registry and becomes the active scorer: incoming claims are flagged by these rules, and claim review shows exactly which rule fired
Platform Documentation

Business and technical documentation for TrueClaim, API endpoints, integration guidance, and OpenAPI references.

User Access Management
User Role Joined Status
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Billing & Usage

Contract quota and monthly usage for this tenant (claims and training). Tenant admins only — not a customer-facing billing page.

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System Configuration
Used when showing financial amounts in Claim Review.
Using backend threshold settings.

Technical Console

Operate the ML lifecycle: datasets → standardization → training → evaluation → deployment.
System
Healthy
API + workers OK
Active Deployment
prod-motor-01
Last Dataset
run_0192
Artifacts Store

minIO

Quick Actions

All Business Sessions

View every workspace at a glance. Switch session context to scope datasets, training, testing, and deployments.
Sessions
Each session should contain datasets with the same raw feature schema.
Name Session ID Domain Status Created Active Actions
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Datasets & Versions

Upload a raw CSV, then define each feature's nature in the YAML config. The transformer is fitted on training data and applied at inference time.
Step 1 — Upload Raw CSV
Upload the raw data file. Column headers will be auto-detected so you can assign feature types in the next step.
Leave empty to auto-split the train file at training time (ratio/mode in Training tab). If provided, the feature transformer and standardization pipeline are fit only on the train CSV and applied unchanged to this test CSV — metrics are reported on this uploaded holdout.
Dataset Registry
dataset_id name row_count feature_count holdout created_at actions

Standardization

Create Preprocessing Version
Fit on train only; test rows never influence imputation, scaling, or encoding.
Column types are auto-detected on the latest transformed CSV. Configure raw feature types under Datasets & Versions → Configure.
Uncheck columns to exclude from the preprocessing pipeline.
Select a dataset above to load transformed columns.
All Datasets & Preprocessing Counts
dataset_id name preprocessing_versions_count latest preprocessing_id latest status latest created_at
ds_0f21 motor_claims_saudi_v1 2 pp_120a COMPLETED 2026-01-18
ds_77a8 motor_claims_es_v2 1 pp_88b2 COMPLETED 2026-01-06
Preprocessing Registry
preprocessing_id dataset_id strategy status output_feature_count created_at
pp_120a ds_0f21 robust COMPLETED 312 2026-01-18
pp_0c77 ds_0f21 baseline COMPLETED 280 2026-01-10

Training Runs

Launch a Training Run
Training Configuration
Loading default hyperparameters…
Run Status & Progress
Status
—
Select run
Progress
—
jobs done / total
Current stage
—
run stage
best_model_id
—
Runs by Dataset
training_run_id run_name status best_model_id created_at actions
All Runs
run_id run_name status progress best_model_id best_f1 started actions
No runs yet. Queue a training run to see results here.

Testing & Evaluation

Single-Model Test
Select a training run first
⚠️ Upload raw, untransformed data
Upload the CSV in the same format as your original training dataset — with the same column names and raw values. The system will automatically re-apply the fitted preprocessing pipeline (imputation, encoding, scaling) before running evaluation. Do not pre-scale or pre-encode the file.
Multi-Model Comparison
Select a training run first
⚠️ Upload raw, untransformed data
Each model carries its own fitted preprocessing pipeline. Upload the raw CSV (same format as training). The system re-applies each model’s pipeline independently before scoring.

Deployments

LLM Activation
Configure global OpenAI settings and per-deployment claim narrative generation. Existing claims are never re-evaluated.
Global LLM settings
—
Per-deployment activation
New claims need both global settings (key + narrative enabled) and per-deployment activation. Re-run inference on new claims after enabling.
Promote & Deploy
Select a training run first
Rollback
Deployment History
event_id deployment_name event_type env model_ids preprocessing_id reason

Inference

▶ Run Inference
📦 Raw Features
🔀 Transformed Features
🔍 Explainability
✏️ Corrections (Relabel)
Create Inference
Trueclaim Raw Feature
transaction_idclaim_idstagereceived_atactions
Click Refresh or run an inference first.
Trueclaim Transformed Feature
transaction_idclaim_idstagefeature_contract_hash created_atactions
Click Refresh or run an inference first.
Trueclaim Explainability
transaction_idclaim_idpredpred_probaleaf_no deployment_idmodel_idpreprocessing_idcreated_atactions
Click Refresh or run an inference first.
Trueclaim Relabel — Human-in-the-loop Correction
Trueclaim Corrections — Stored Human Corrections
claim_idtransaction_idcorrect_labelstagejustificationlabeled_bylabeled_at
Click Refresh or submit a correction first.

Retraining

Retraining Configuration
With new standardization, training is forced to New Training.
Recent Retraining Runs
Run ID Run Name Mode Standardization Status Created Actions
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MLflow Analytics

Experiment tracking, model registry, drift/performance signals, and governance workflow.
Training Runs (MLflow-linked)
—
Candidate Models (MLflow-linked)
—
Registered Models
—
Open Alerts
—
Retraining Recommendation
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NO DATA
Run monitoring to compute recommendation.
Model Registry Control
Select a model and register to MLflow Model Registry.
Candidate Metrics Comparison
Drift Alert Trend (Recent Monitoring Runs)
Production Performance Trend
MLflow-Linked Model Registry
model_id training_run_id mlflow_run_id registered_name version
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Open Monitoring Alerts
severity alert_type message created_at action
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Lineage & Artifacts

Trace provenance per business session: dataset → standardization → training → deployment.
Session Pipeline Tree
All artifacts generated within the active session.
Select a business session to load lineage.
Model Drill-down
Full chain and artifact paths for a selected model.
Select a model from the tree above to trace its lineage.

Access & API Keys

Role-based access control for business vs technical personas.
Create API Key
Keys are scoped to role and environment. Rotate regularly.
Users & Roles
Business users see operational screens; technical users manage lifecycle operations.
user role last_login actions
claims_manager business 2026-01-21
mlops technical 2026-01-21

Danger Zone – Delete Options

Permanently remove business sessions, datasets, standardizations and training runs, including their files on disk and objects in MinIO.
Heads up. These actions are irreversible. Deleting a session cascades to every dataset, standardization, training run, deployment, and claim record attached to it. Deleting a dataset cascades to every standardization, training run, model, leaderboard and artifact attached to it. Use the Force delete toggle only when you intentionally want to wipe active deployments, inference audit rows, monitoring history and retraining links that reference the target.
Business Sessions
Deleting a session wipes every dataset, standardization, training run, deployment, and TrueClaim record scoped to that session.
name session_id datasets standardizations training runs disk size blockers actions
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Datasets
Deleting a dataset wipes its raw CSV, every standardization, every training run, every transformation, and the MinIO prefix for that dataset.
dataset_id name rows standardizations disk size blockers actions
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Standardizations (Preprocessings)
Deleting a standardization removes its pipeline artifacts and every training run that depends on it.
preprocessing_id dataset_id strategy status training runs disk size blockers actions
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Training Runs
Deleting a training run removes its models, leaderboard, COC data and all other artifacts under the run directory.
training_run_id dataset_id preprocessing_id name status models disk size blockers actions
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