FAQs
Platform basics
What is Corridor, and how does it fit into my existing stack?
Corridor is an integrated analytical workbench: data, features, models, and decision policies live in one governed environment. Instead of stitching together a data platform, a modeling notebook, and a rules engine, you build, simulate, approve, and export everything as a single deployable artifact that plugs into your existing scoring engines and LOS.
Can I use models trained outside Corridor?
Yes. The Model Registry imports externally trained models in several formats (Pickle, PMML, H2O MOJO, ONNX, or custom). Once registered they become governed objects that can be simulated, explained, approved, and reused like any natively trained model. See Register a Model.
What is the analytical lifecycle?
Work flows through eight stages: register data, build features, develop models, compose strategy and rules, simulate and review, approve and export, bring outcomes back in from production, and monitor performance. Stages 1–6 happen inside Corridor before production; stage 7 closes the feedback loop; stage 8 watches health continuously. See Understand the Analytical Lifecycle.
Do I have to approve every change before I can work with it?
No. You can build, edit, and simulate in Draft freely. Approval is only required when you want to publish for broader use or production deployment. The approval workflow itself is configured by your admin per object type.
Registry objects: when to use what
What is the difference between a Table, Data Element, and Feature?
- A Table is raw source data (an application table, a credit bureau dump).
- A Data Element is a governed wrapper around one column or one aggregated computation derived from Tables (for example
annual_incomeorbankruptcies_last_10y). - A Feature is a transformation on top of Data Elements (or other Features) that produces a reusable variable for downstream use (for example
risk_tierderived from FICO buckets).
You always go Table → Data Element → Feature before something reaches a Model or Policy.
Why must I register columns as Data Elements before using them?
The Data Element layer is where governance lives: permissible-purpose tags, lineage, descriptions, and ownership. Without it, raw columns would flow straight into Models and Policies and there would be no consistent way to know what a value means or where it came from.
What is the difference between a Simple and an Aggregated Data Element?
A Simple Data Element is a one-to-one map onto a single column. An Aggregated Data Element rolls up multiple rows for an entity using Python, Pandas, or PySpark (for example "total bankruptcies for this applicant in the last 10 years").
What is the difference between a Feature and a Model?
A Feature is hand-written transformation logic; it produces a variable (a number, category, or flag). A Model is a trained artifact (XGBoost, sklearn, PMML, ONNX) that produces a score or class label. Models carry training data references, quality metrics, and explainability artifacts that Features do not.
Can a Model use another Model as an input?
Yes. This is how stacked models and ONNX reader models are wired together: the upstream Model's output becomes a registered input on the downstream Model.
What is a Policy, and how is it different from a Model?
A Model produces a score. A Policy is the decision workflow that turns that score into an actual outcome: approve / decline / refer, plus offer terms. A Policy chains together blocks (application decisioning, derived variables, offer creation, offer filtering, offer optimization) and is what gets deployed to your LOS or scoring engine.
Lifecycle, versions, and approvals
What do Draft, Pending, and Approved mean?
- Draft: editable, scoped to the creator/workspace.
- Pending Approval: locked from edits (unless the active reviewer has edit rights) while reviewers vote.
- Approved / Published: locked. Any further change creates a new Draft version that has to go through approval again.
Can I edit an object after it is approved?
Not in place. You make a new version, which starts in Draft. Once that version is approved, it replaces the previous version as the published one. Older versions stay available in the history.
What is "Veto Power" in an approval workflow?
A reviewer with Veto Power can single-handedly reject the request: one rejection from them fails the whole approval regardless of how other reviewers voted. While a veto reviewer is active, the object is typically locked from further edits.
Can I compare two versions of a Model, Feature, or Policy?
Yes. The Run menu offers a comparison simulation that scores two versions side by side on the same dataset. Use this for champion/challenger checks and to document the impact of a change.
Can I roll back to a previous version?
Versions are preserved with their timestamps, authors, and comments, and deployments point to a specific version. You can return to a previous version by re-publishing it (it does not auto-promote on upgrade).
Simulations, what-if, and portfolio analysis
What is a Simulation, and when should I run one?
A Simulation runs the logic of any registered object (Data Element, Feature, Model, or Policy) on historical data. Run one to validate correctness, measure metrics, generate evidence for approval, or compare against a baseline. You don't need approval to simulate. See Run a Simulation.
What is a What-If analysis?
A What-If simulation always runs against a chosen baseline simulation. The output highlights how the candidate's metrics differ from the baseline (approval-rate delta, bad-rate delta, score-shift distributions), so you can quantify the impact of a proposed change before approving it.
What is the difference between a Simulation and a Single Record Review?
A Simulation is a batch run on many records producing aggregate metrics. A Single Record Review traces one record through every block of a Policy, showing which rules fired, which offers were generated, and why — useful for debugging or explaining a specific decision.
Can I schedule a Simulation to run regularly?
Yes. The Simulation form has a Scheduling section (start date, time, recurrence). Each scheduled run is preserved as its own job so you can trend metrics across runs and configure alerts on them.
What is a Portfolio Analysis?
A portfolio analysis evaluates a Policy across a population to see distributional outcomes — approval mix, expected loss, offer assignment — rather than testing correctness on a single dataset.
Monitoring, alerts, and reports
Once a Model or Policy is live, how do I know it is still healthy?
Set up a Monitoring Dashboard. Dashboards refresh on a schedule (daily, weekly, monthly) by pulling outcomes back from production. They show stability metrics (PSI, CSI), discrimination metrics (KS, AUC), and policy-level metrics (approval rate, bad rate), and trigger alerts when a metric breaches a threshold.
What are PSI, CSI, and KS?
- PSI (Population Stability Index): detects shift in the distribution of a continuous variable versus a reference period.
- CSI (Characteristic Stability Index): PSI's counterpart for categorical variables.
- KS (Kolmogorov–Smirnov): measures how well a model separates the positive and negative classes.
Corridor computes these automatically as part of model simulations and monitoring.
How do I set up email or in-app alerts?
In a Monitoring Dashboard, add an alert rule under the Alert Settings tab: pick a metric, a threshold, and a recipient list. Triggered alerts appear in the Triggered Alerts tab and can be sent via email if your admin has configured an email provider. See Set up Alerts.
What is lineage, and why does it matter?
Lineage shows everything an object depends on (upstream) and everything that depends on it (downstream). Before changing a Feature, lineage tells you every Model and Policy that will be affected.
Security, permissions, and governance
What is Permissible Purpose?
A set of tags applied to Data Elements, Features, and Models that declare where the object is allowed to be used (e.g. underwriting, marketing, PII-restricted). The platform flags downstream uses that violate the tags, so a PII-marked Data Element will not silently end up in a marketing Policy.
Who can approve my work?
The set of approvers is defined by the Approval Workflow attached to the object's type. Workflows are configured by admins, can mix users, roles, and external tools, and can require sequential or parallel approval gates. See Configure an Approval Workflow.
Can the same workflow have parallel and sequential steps?
Yes. Responsibilities within the same sequence group review in parallel. Sequence groups run in order: every responsibility in sequence 1 must accept before sequence 2 unlocks.
What is recorded when I edit a Model, Feature, or Policy?
Every change is recorded with the author, timestamp, version number, and an optional comment. The Change History tab on each object exposes that audit trail and supports compliance reviews.
Can someone outside my workspace see my draft work?
Drafts are scoped to your workspace. Other users only see an object after it has been published through an approval workflow and they have the right permissions.
Data sources and integrations
What data sources are supported?
Tables can be registered against Parquet, ORC, CSV, Hive, Snowflake, and JDBC-compliant databases (Redshift, PostgreSQL, etc.). The connection or path is captured in the Table registration. See Register a Table.
Can I preprocess data before Corridor reads it?
Yes. A Table can carry Read Logic: Python or PySpark code that runs before any downstream job reads the Table. Use it for type casts, normalization, joining auxiliary tables, or filtering down to a window.
How does an approved Model or Policy get into production?
Approved objects can be exported as artifacts (scorecards, JSON definitions, or model bundles) suitable for deployment into your LOS or scoring engine (examples include MeridianLink, Fiserv, Origence). The exact bundle depends on the deployment target you select. [UNCONFIRMED] Exact deployment mechanics are environment-dependent — check with your platform admin.
Can I use custom Python libraries in my Feature or Data Element logic?
Standard libraries (numpy, pandas, pyspark, math, datetime) are available out of the box. Adding new libraries is an admin / environment task; ask your platform admin to confirm the policy for custom dependencies.
Admin and configuration
How do I set up an approval workflow for my team?
Go to Settings → Approval Workflow, create a workflow, pick the object types it covers, then add Responsibilities (the reviewers). Each Responsibility can have veto power and a sequence number. Activate the workflow so it appears in the workflow dropdown when authors register an object. See Configure an Approval Workflow.
Can I retire an old approval workflow without breaking pending requests?
Yes. Set the workflow to Inactive: it disappears from new selections but in-flight approvals continue with the workflow they were submitted under. Use the Usage Statistics view to migrate existing objects to a successor workflow if needed.
What is the difference between a Simulation and a Monitoring Dashboard?
A Simulation is an ad-hoc job: pick a dataset, run, get results. A Monitoring Dashboard is a recurring, multi-object view that pulls fresh production data on a schedule and is the right home for ongoing health tracking and alerting.
Where do I report a bug or ask for help?
See Common Troubleshooting first for known errors and resolutions. If your issue isn't covered, capture the exact error text (with any API-XX code), the object you were working on, and the action that triggered it, and share with your platform admin or support contact.
See also
- Common Troubleshooting — symptom-to-fix lookup for errors.
- Glossary — definitions of every platform term.
- Getting Started — guided introduction.