Catch the model shift early. Segment shift, or a model bug?
When approval rate moves after a deploy, Bicycle ranks whether it's an applicant-segment shift or a model-version effect, on the affected cohort, before approvals skew.
A dashboard shows approval rate dropped. A chatbot answers what you type. Bicycle has the cause and the next step before approvals skew.
No credit card required
You own approval rate, yet the "why did it move?" answer waits in someone else's queue.
Same number, two operating models. One sends you to a ticket and a dashboard. The other brings the answer, the why, and the next step to you.
One governed system, six capabilities behind every answer.
Six capabilities, set up once and run continuously. Business teams move faster, the data team keeps governance, everyone trusts the number. Pick one to go deeper.
Capabilities work together in one continuous loop. Detect → Explain → Act → Learn.
Your part of the loop is three moves: know what changed, understand why, act on it.
Your risk review starts with the first pass done.
Approval rate dropped on a thin-file cohort right after model deploy 318. Bicycle has already checked the cohorts, ranked the likely causes, attached the evidence, and kept the definition governed. You review the move, check the evidence, recommend the fix to the risk and model team, and reuse it on the next deploy.
Different teams own different metrics. Bicycle keeps the investigation governed.
You own approval rate. Move to the payments teams around dispute rates, authorization performance, settlement timing, and revenue performance, all on the same loop.
See it on your numbers.
Start with one recurring metric you own: why approval rate moved for a cohort. Bicycle watches the governed KPI, ranks likely causes across applicant segment, model version, feature drift, and data quality, and hands you a reviewable first pass, before approvals skew.
No credit card required · analyst-reviewed · governed from day one