Agentic analytics · for risk and approval teams

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.

Start for free Explore your playbook

No credit card required

app.bicycle.ai approvals · morning briefYour KPIs
Morning brief · what changed
Approval rate · thin-file cohort · post-deploy 318-4.1pp

Ranked #1 by impact. Est. exposure $42k. It found you, before you opened a dashboard.

Rank#1 · by revenue exposure
Also movingDecline rate · thin-file +3.2pp
Surfaced8:42am · ranked for you
Know Understand Act
You own the approval rate, not the answer

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.

Without Bicycle With Bicycle
too late
You find approval rate moved late, on a dashboard
By the time the drop stands out, approvals have already skewed on the cohort.
real time
Approval moves reach you ranked by approval impact
You see the cohort that moved the moment it moves, not when you open the dashboard.
ticket
"Why did approval rate move?" means filing a ticket and waiting
Three days later you get a chart back, not a decision the risk review can use.
with the alert
The ranked cause, segment shift or model effect, arrives with the alert
Cause, evidence, and ruled-out drivers on a governed approval definition. No ticket, no waiting.
still slow
Knowing isn't fixing, and approvals keep skewing
Even with the answer, lining up the risk-and-model-team fix is a separate scramble.
act fast
The next step recommended to the risk and model team comes with it, so you act before approvals skew
A scoped recommendation to review the model or adjust policy, not just a chart to interpret.
The capabilities

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.

Rapid activation on your stack

You wait for the analytics project to finish. You want a working operating view tied to the KPIs you own, not a blank dashboard canvas.

What Bicycle does

1

Skip the blank canvas. Relevant KPIs, starter alerts, and prompts are populated for your role.

2

Speak your language. The system arrives configured for your business, not the database schema.

3

Use it from day one. The starter agent is reviewable and live in days, not a six-month rollout.

In practice

Open your risk home on day one. Approval rate, decline rate, auth rate, and funding rate are already monitored, ranked by impact.

Capabilities work together in one continuous loop. Detect → Explain → Act → Learn.

The playbookKnow · Understand · Act

Your part of the loop is three moves: know what changed, understand why, act on it.

Knowwhat changed
→
Understandwhy it moved
→
Acton it, inside guardrails
Pillar 01Know

Know what changed before the review, down to where it moved.

Bicycle watches the KPIs you own, approval rate by cohort, decline reasons, auth rate, across cohort, segment, and model version, and flags the move first, on the exact cohort, before approvals skew.
How Bicycle watches Every KPI you own, watched across segments; the move that matters surfaces ranked by impact.01 / 03
WATCHING EVERY KPI × SEGMENT · CAUGHT AT THE COHORT BEFORE THE AVERAGE MOVESBlended average −0.3% · within range · hides the cohortAndroidWebIn-storeEUAPACCohortApproval rateDecline rateAuth rateFunding rate−4.1ppsurfacedranked by impact#1Approval rate · thin-file−4.1pp$42k at riskranked first#2Decline rate · thin-file+3.2pp · watching$12k#3Auth rate · near-prime−0.9pp · in range$6kYou see the move ranked by impact, on your segments, before the average reflects it.
The alert inbox, ranked by impact
Dashboards on every KPI you own
Pillar 02Understand

Understand why approval rate moved before you ask.

The first-pass cause arrives ranked, with the evidence behind it: applicant-segment shift, model-version effect, feature drift, so you see why approval rate moved without filing a ticket and waiting.
How Bicycle finds the cause It narrows to the affected segment, tests the drivers, and ranks the cause with the evidence behind it.01 / 03
ONE MOVE → MANY DRIVERS TESTED → ONE RANKED CAUSEApproval rate−4.1ppThin-file cohortdetected 09:58 · 24h windowSegment shift · deploy 31873%Model-version effect27%Seasonalruled outFraud eventruled outData qualityruled outFeature driftruled outPRIMARY CAUSE · 73% CONFIDENCEApplicant-segment shiftDeploy 318 · thin-file cohortDeploy logDecision logFeatures4 drivers ruled outRecommended next step: review the model after deploy 318The first-pass cause arrives ranked, with the evidence behind it and the wrong drivers ruled out.
The ranked cause with evidence
One shared data story for the review
Pillar 03Act

Act on it, scoped and routed to the team that owns the fix.

Take the scoped next step, review the model or adjust policy and route it to the risk and model team that owns the fix, previewed and approved. Everyone can ask; the data team controls what gets trusted, and every action stays bounded, reversible, and logged.
How Bicycle acts on it It recommends the next step scoped to the cause, previews it, and runs it inside your guardrails.01 / 03
FROM CAUSE TO THE NEXT STEP · THROUGH THE GUARDRAIL · LOGGEDCause: applicant-segment shiftRECOMMENDED ACTIONReview model deploy 318scoped to thin-file cohortTHE GUARDRAILPreview what it will doScope: thin-file cohortOwner: Risk and modelRollback readyevery attempt loggedSlack alertAUTOJira ticketAUTOAdjust approval policyAPPROVALRoll back deploy 318APPROVALRun what you are cleared to; the rest carries approval, rollback, and a full audit trail · previewed · bounded · reversible · logged.
The recommended next step, scoped
Preview the action before it runs
Explore your playbook
A day in the lifeFirst pass by Bicycle · the call is yours
Your risk review

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.

Approval rate · thin-file cohort · post-deploy 318Approval rate below baseline on the cohort, with the evidence already assembled for your review.
8:42 AM-4.1pprisk-ops queue
Your risk review8:42 AM↓ -4.1ppbrief ready
Today · what moved on your KPIs
HighApproval rate droppedThin-file cohort · post-deploy 318-4.1ppvs 31.2% baselineTop segmentThin-file cohort · post-deploy 318$58Kat riskYour KPIapproval rate8:42 AM
MedRefund rate upApparel · returns+1.8ppvs baselineTop segmentApparel · US$24Kat riskOpsowning team7:40 AM
MedAverage order value downWest region-0.9%vs baselineTop segmentWest · all devices$17Kat riskMerchandisingowning team7:12 AM
LowAdd to cart rate downPaid traffic-0.6ppvs baselineTop segmentPaid · US$9Kat riskGrowthowning team6:58 AM
+ 4 KPIs · 1 high · sorted by revenue at risk
SnoozeOpen the move
Approval rate · ranked causefirst pass by Bicycle↓ -4.1pp
Triaged to one segment · Thin-file cohort · post-deploy 318
-4.1pp approval rate
baseline 31.2% · post-deploy · high volume
1 of 6 segments affected · 5 within expected range
Likely driversranked · confidence
Applicant-segment shift → thin-file cohortprimary73%
Model-version effect / feature driftsecondary27%
Pricing & discount changesruled out
Recommended actionsscoped
Recommend fix to the risk and model teamscoped
Alert #risk-ops on Slackscoped
Review the model after deploy 318preview
scoped · previewed · reversible · logged
+ Detected 8:02 · first pass 8:03 · top segment thin-file · post-deploy
Adjust modelSee the evidence
Check the evidence · Thin-file cohort · post-deploy 3188:42 AM↓ -4.1ppreview
Impact overview
-4.1pp
approval rate · vs baseline 31.2%
184Kaffected applicants
1.92Mdecisions
Top affected segments
1
Thin-file cohort · post-deploy 318
-4.1pp
2
Thin-file cohort · near-prime
-2.6pp
3
Prime cohort · returning
-1.9pp
Evidence attached
Approval trenddecision to fund
Segment mixthin-file share
Model performanceversion 318
Deploy activitymodel deploy 318
Ruled out · not drivers
Seasonalno overlap with window
Fraud eventwithin range
Data qualityfreshness stable
New vs returningboth affected
Definition governed · Source read-only · sources fresh
+ Reviewed by you · 8:11 AM
Send backTake the action
Act on it · approval rate8:42 AM↓ -4.1ppact
Recommended actionscoped
Review the model after deploy 318 on the thin-file cohort, the change that skewed approvals.
Thin-file cohort · post-deploy 318
The guardrailbefore anything runs
Preview the change firstrequired
Scope to the affected segmentThin-file cohort
Owner approves the changeRisk and model team
Rollback stays one click awayreversible
Where it goesrecommended
Alert #risk-opsauto
Recommend to the risk and model teamapproval
Adjust the approval policyapproval
scoped · previewed · reversible · logged
+ Scoped to thin-file cohort · reversible · logged
Adjust scopeRecommend & assign
Reused next drop · operating memoryToday 6:54 AM↓ -3.6ppauto-applied
Similar situation detected: Approval rate dropped · Thin-file cohort · post-deploy 318
Bicycle applied your accepted cause path.
Detected pattern
Segment shift
→
Applied path
Thin-file cohort
→
Likely driver
Model deploy 318
→
Recommended action
Review the model
Caught 68 min earlier · 6:54 AM vs 8:42 AM on the first run
No re-investigation · path auto-applied, your review optional
+ Memory: approval-rate drop · run 2
ReviewView saved path
What changes for you: less waiting on the analyst queue, more reviewing the first pass and acting on it.First pass by Bicycle · the call is yours
More roles on Bicycle · same loopYour team's KPI · your screens

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.

Payments
Payments AnalyticsApproval rate and routing by issuer and acquirer Payments Data EngineeringMetric and pipeline trust: freshness, model drift Payment OperationsPayment success and decline by PSP and method Merchant OperationsChargeback rate and merchant health by merchant Scheme & Interchange OperationsDowngrade and interchange by card type
Risk & Approval OperationsApproval rate by cohort, model and segment · you are here
Explore
Retail Shopify Travel Payments
Vibe Analytics

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.

What to bring
A recurring metric you own: approval rate, decline rate, or auth rate.
An approved read-only path to the relevant sources: the warehouse, event streams, app & web, payments, or ad networks.
The metric definition, default dimensions, and the owner who approves the next step.
What we do
Configure the investigation path around your KPI, dimensions, and evidence rules.
Monitor device, OS, channel, region, and segment movement.
Prepare a first-pass cause analysis with evidence, ruled-out paths, and governed definitions.
Start for free Explore your playbook

No credit card required · analyst-reviewed · governed from day one