Normalized claim
Loan approval and verification turnaround time: 100% decrease
decreased from two days to just five seconds
BharatPe: Scaling data effortlessly for analytics and AI with Google Cloud to promote digital payments
Use case typeData platform modernizationUpdated May 22, 2026
BharatPe helps more than 10 million merchants accept digital payments and uses Google Cloud to scale analytics and AI for payments, underwriting, KYC, fraud detection, and marketing insights. The company built a serverless data platform around BigQuery and Vertex AI, using Cloud Composer, Dataflow, Pub/Sub, Cloud Functions, and Dataproc to orchestrate large-scale data pipelines and machine learning workflows. Google Cloud also supports merchant loan decisioning by extracting store details from photos with Vision AI and integrating model outputs into real-time decision microservices.
Reported outcomes
1,000 DAGs
workflow orchestration scaleAdoption & scale
−100%loan approval and verification turnaround time80 TB/daydata volume processed per day
Strategic outcomes
Risk & complianceAutomated KYC verification to reduce identity fraudSpeed & agilityReal-time decisions for underwriting and creditCustomer experience & trustFaster access to merchant loansScale & capacityServerless architecture supports lean-team operations at high scaleBetter decisions & insightInsights for critical decisions in seconds
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Data volume processed per day: 80 TB/day increase
Processes up to 80TB of data each day on BigQuery
Normalized claim
Workflow orchestration scale: 1,000 DAGs increase
runs more than 1,000 Directed Acyclic Graphs (DAGs) in its Cloud Composer environment
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- BharatPe
- Provider
- GCP
- Maturity
- Scaled Production
- Linked source
- Google Cloud Customer Stories
Built a serverless data platform with BigQuery for analytics at scale
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 5
- 1Data platform modernization
- 2Fraud detection
- 3Identity verification
- Scale and operationalize large-scale analytics and AI for merchant payments, loan decisioning, and KYC.
- Automate identity verification to reduce fraud and speed credit approvals.
- Replace legacy reporting and data processing that could not handle the required scale or latency.
- Built a serverless data platform with BigQuery for analytics at scale.
- Used Vertex AI for machine learning pipelines, model training, and online prediction.
- Used Vision AI to extract store name and address from merchant photos for underwriting and KYC.
- Orchestrated more than 1,000 workflows with Cloud Composer and used Dataflow, Pub/Sub, Cloud Functions, and Dataproc for autoscaling data pipelines.
- Integrated ML outputs into real-time microservices for underwriting, fraud detection, and credit decisions.
- Reduced loan approval and verification turnaround time from two days to five seconds.
- Enabled automated decisioning for fraud detection and credit decisions.
- Processes up to 80TB of data each day on BigQuery.
- Generates insights for critical decisions in seconds.
- Runs more than 1,000 DAGs in Cloud Composer.
Sources & evidence1
Evidence: Medium65/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: May 22, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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