← Use cases
Success and approval rates

You shipped a new model on Tuesday. Approvals dropped on Wednesday. Coincidence?

Bicycle watches approval rate and the features behind it, and ties each shift to the model release, rule change or operational change that caused it.

Book a demo Start for free

No credit card required

Before

Approval rates move for many reasons: a new model, a rule change, a shift in who applies, the market. Working out which one takes days, while the business waits for an answer.

With Bicycle

Each shift arrives with the change it lines up with, so risk, data science and operations start from the same explanation.

What happens when a KPI moves.

  1. 01

    Detect

    Approval rate, gross written premium or funnel conversion shifts for one group of applications.

  2. 02

    Explain

    The shift is tied to what changed: a model deployment, a rule change, an operational change, or a market signal.

  3. 03

    Act

    Bicycle recommends the next step: data science assesses the model, operations fixes a data or process issue, and the business explains the move to leadership.

  4. 04

    Learn

    Each anomaly and how it was resolved becomes shared knowledge across the teams.

Near real time

root cause, down from days or weeks

Seen at: a fintech innovator. Read the case study

Everyone can ask. Data & Analytics controls what gets trusted, published, and acted on.

Runs on
  • Application data
  • Model and rule history
  • Release notes
Used by
  • Risk
  • Underwriting operations
  • Data science

Common questions

Why did our approval rate drop after a model change?
Bicycle tracks approval rate, funnel conversion at each stage and the application features behind them in parallel. When a deviation is statistically significant, Bicycle surfaces the specific intersection where it is concentrated, such as a single feature in a single region for a single product, not just the aggregate movement. Bicycle then correlates the anomaly against the operational events that could explain it.
Do we have to move our data to find out why approval rates changed?
No. Bicycle works on the systems you already have: transactional data in your warehouse, the code repository that records model and rule deployments, and the documentation and internal knowledge bases where operational changes are written down. You keep those sources. Bicycle adds the tracking and correlation layer on top of them rather than asking you to migrate anything.
How do risk, data science and operations agree on why approvals moved?
Business operations, data scientists and operational leaders usually work from separate tools with different definitions. Bicycle gives these teams one shared, defensible view of the same evidence, so your team can decide whether a shift is a regression, an intentional outcome or an external market shift. Your data scientists then assess model impact without manual tracebacks, and your team decides the next step.

More on success and approval rates

See it on your own KPI.

Bring one revenue-critical KPI. We'll show how Bicycle detects the movement, explains the cause, and recommends the next step.

Book a demo