BANKS, FINTECH, PAYMENTS & INSURANCE
Keep model decisions subject to a consistent quality check
Evaluate model performance consistently, check the result against defined requirements, and keep unsuitable results from being published.
FOR
Fraud and risk teams, model-governance owners
STARTS WITH
Payment data + fraud labels
ENDS WITH
A validated scorecard in your systems
WHAT OPTICIQ ENABLES
Put model quality checks into the operating process
Turn model evaluation into a repeatable, controlled workflow, with the right data, the right logic, and a clear path to action.
Evaluate performance consistently
Run the same evaluation process whenever the model is assessed.
Compare results in context
See model performance alongside a meaningful baseline instead of relying on a single metric.
Control what gets published
Apply a defined check before the resulting scorecard is allowed downstream.
FROM DATA TO DECISION
Run the evaluation and control in one place
A single, end-to-end workflow that connects your data, runs the model evaluation, applies the quality gate, and writes the results back to the systems your team already uses.
OpticIQ Studio
Sources
Transforms
AI & Models
Scripts
Outputs
Governance
Utilities
SOURCE
Payment data
20,000 rows
TRANSFORM
Hold-out split
Evaluation slice
MODEL
Fraud classifier
Binary classifier
MODEL
Evaluate performance
Precision + baseline
SCRIPT
Quality gate
Threshold check
OUTPUT
Model scorecard
Write to marts.model_scorecard
WHAT CHANGES
Model checks become repeatable, visible and controlled
Run the same evaluation each time
The performance check lives inside the workflow instead of depending on a separate process.
Keep the comparison visible
The scorecard records model performance alongside its baseline so you can see whether the model is actually adding value.
Stop unsuitable results before publication
The quality gate checks the result before the scorecard is written downstream.
20,000
PAYMENT TRANSACTIONS
1
EVALUATION SLICE
1
SCORECARD
A REPEATABLE MODEL CONTROL PROCESS
SEE IT IN OPTICIQ
Fraud Model Scorecard
A working example of how model evaluation and a publication gate can run inside the same pipeline.
INPUT
20,000 payment transactions with fraud labels
EVALUATION
Hold-out evaluation slice
MODEL
Fraud classifier
CHECK
Precision reported alongside an always-legit baseline
CONTROL
Assert gate checks the scorecard before publication
OUTPUT
Scorecard written to marts.model_scorecard, with history accumulating across runs
Scorecard history
Run
Precision
Baseline
Status
Today, 16:37
0.93
0.61
Published
Aug 21, 09:12
0.91
0.60
Published
Aug 14, 11:28
0.78
0.60
Blocked
Aug 7, 10:03
0.88
0.60
Published
BEYOND MODEL VALIDATION
Use the same evaluation and control across your model workflows
Champion/challenger testing
Compare a candidate model against the model it would replace.
Drift monitoring
Track how model inputs and performance shift over time.
Model inventory
Keep a record of every model in production and its current status.
Audit reporting
Deliver evaluation history to the teams that need to review it.
OTHER USE CASES
RETAIL & DISTRIBUTION
Inventory & replenishment
Bring sales, inventory, supplier, and planning data into one workflow, and get updated decisions back into the systems your teams already use.
Sell-out data
Aggregate
Forecast
Planning DB
Explore
BANKING, TELCO, INSURANCE & SUBSCRIPTION
Customer retention & outreach
Bring customer signals, PII protection, and AI-assisted outreach into one controlled workflow.
CRM data
PII scan
AI outreach
Outreach
Explore
SUPPLY CHAIN & OPERATIONS
Supply chain risk
Combine operational, sales, and supplier information into one live view of risk.
Machine + sales + vendor
Join & normalize
Risk score
Report
Explore
Bring us your workflow.
See how your own data, systems, and logic would run in OpticIQ.