Direct from customer income capture at credit card activation for an enterprise banking client. Cut data vendor costs 20% and lifted offer conversion 12%.
A new microservice extending the client's core banking application to capture customer declared income directly at the point of credit card activation.
Previously, income data came from credit application forms, often 3 months or more out of date, or from third party income surveys that could take up to 6 months to return results.
This service adds a direct, first party data entry point at exactly the moment the customer is engaged, closing that gap.
Customer declares income during card activation. Apigee handles auth and rate limiting before the request reaches Spring Boot. Income is written to MongoDB, then folded into the existing Hadoop/Hive batch pipeline via extended SQL queries. The same data reaches both the offer targeting system and business analyst Tableau dashboards.
The existing process relied on stale credit application data, 3 months or more old, or expensive third party income surveys with a 6 month turnaround. The decision was to add a direct capture step at card activation, when the customer is already engaged and motivated to complete the process, rather than sending a separate survey later.
Income data at card activation is document shaped, not relational. Each activation event has a variable set of income fields depending on the customer's employment type. MongoDB was chosen as the transactional store to accommodate that variability without requiring schema migrations for each new income type.
The existing Hadoop/Hive batch pipeline was extended with new SQL queries, instead of building a parallel data pipeline for the new income data. This kept the data flowing to the same enterprise datalake and Tableau dashboards the business already used, with no new tooling or analyst retraining required.
Authentication, rate limiting, and routing were handled at the Apigee gateway layer rather than inside the Spring Boot service itself. This kept the microservice focused on business logic and meant security policy changes could be applied at the gateway without a service redeployment.
| Service | Role | Auth method |
|---|---|---|
| Apigee | API gateway, authentication, rate limiting, routing | API key / OAuth policies |
| Jenkins | CI/CD, automated build and deployment | Internal pipeline |
| MongoDB | Transactional store for captured income data | Internal service credentials |
| Hadoop/Hive | Batch pipeline feeding the enterprise datalake | Internal service credentials |
| Kubernetes | Container orchestration for microservice deployment | Internal |
| AWS ECS/Lambda | Scalable workload hosting post PCF migration | IAM roles |
| Tableau | Business analyst dashboards consuming datalake data | Internal service credentials |