customer_support · finance · workflow
Snap Finance cuts AHT 40% and raises containment 5.5x with Cresta generative AI
Snap Finance was experiencing rapid growth and struggled to scale its contact center while managing costs and compliance. Reporting systems were inadequate and quality management relied on random call sampling, giving supervisors little visibility into overall agent performance.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Customer contacts via voice or chat
Customer interactions arrive through voice and chat channels.
Tools used
CrestaVirtual AgentAgent Assist
Outcome
Cresta enabled Snap Finance to reduce average handle time by 40%, raise deflection rate from 6% to 33%, achieve 100% QA automation across all calls, and improve customer satisfaction by 23% along with employee engagement scores.
What failed first
Previous technology was described as anemic with major reporting problems, and quality management was effectively arbitrary—only a handful of random calls were reviewed each month.
Results
Time saved40%
Volume5.5x
Grounding & classification
Source type: vendor customer story
36 fields verified against source quotes.
agent assistchatbotconversational aiquality inspectioncall recordingchat transcriptfailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedtools describedvendor confirmedworkflow describedfinancial servicesautomation ratecustomer satisfactioncycle time reductiondeflection rateemployee productivityvendor customer storycall center aicompliance monitoringcustomer supportquality assuranceautonomous resolutionescalation workflow