Bank of Baroda: Early Warning System migrated to Spring Boot and Angular on Liferay DXP 7.4
India
After absorbing Vijaya Bank and Dena Bank, Bank of Baroda's Early Warning System had to handle far more data under tighter regulatory scrutiny than its monolithic architecture allowed. ATS led the migration to Spring Boot microservices and Angular portlets on Liferay DXP 7.4 — with a maker-checker workflow, predictive risk analytics and zero data loss.
The challenge
Three banks' worth of data in a system built for one
An Early Warning System is where a bank spots financial risk before it becomes a loss. Bank of Baroda's EWS was built for one bank's data; after the mergers with Vijaya Bank and Dena Bank it had to ingest data from the merged entities' systems as well, and the legacy monolithic architecture could not support that scale. Review cycles on risk alerts stretched to two days.
At the same time, new regulatory requirements mandated enhanced audit trails and maker-checker workflows — every risk decision recorded, made by one officer and approved by another. The bank needed a modernised EWS that could scale with the merged data volumes, satisfy RBI compliance, and get there without losing a single record on the way. ATS led the Liferay migration and the re-architecture around it.
Our approach
Re-architect for scale, build compliance into the workflow
Delivered by our Liferay DXP practice.
ATS migrated the system to a Spring Boot and Angular architecture on Liferay DXP 7.4. The monolith was decomposed into Spring Boot microservices behind Liferay, and the user interface was rebuilt as Angular portlets — dynamic screens for risk officers that run inside the bank's DXP with its authentication and role model. Multi-bank data ingestion pipelines were built to bring data from the merged entities' systems into the EWS seamlessly.
Compliance was designed into the flow rather than added as a report. A multi-level maker-checker approval workflow governs risk actions, producing the audit trail RBI requires. Predictive analytics modules flag financial risk in real time and surface it on alert dashboards, so review starts from a prioritised queue rather than a batch. The migration itself was planned and verified to preserve 100% data integrity.
- Migration to Spring Boot microservices + Angular portlets on Liferay DXP 7.4
- Multi-bank data ingestion pipelines for the merged entities
- Multi-level maker-checker approval workflow with full audit trail
- RBI compliance modules
- Predictive risk analytics with real-time alert dashboards
- Zero-data-loss migration plan and verification
Architecture
Java 11, Spring Boot microservices, Angular portlets, Oracle
The platform runs on Java 11: Spring Boot microservices handle ingestion, analytics and workflow, with Oracle DB as the system of record and Tomcat as the runtime. Liferay DXP 7.4 provides the portal layer — authentication, roles, navigation — and hosts the Angular portlets that risk officers use, so the front end is modern and dynamic without leaving the bank's DXP estate. Source control and delivery run through GitHub.
Splitting the monolith into services is what let the system absorb three times the data: ingestion pipelines scale independently of the analytics and the approval workflow. The pattern — Spring Boot services behind Liferay, Angular or React on the front — is the same one we used for ICICI Bank's portal consolidation, and it is a standard shape for banking platforms that must keep a DXP front door while modernising what sits behind it.
Building blocks
- Java 11 · Spring Boot microservices · Angular portlets
- Liferay DXP 7.4 portal layer
- Oracle DB · Tomcat · GitHub
- Multi-bank data ingestion pipelines
- Maker-checker workflow and RBI compliance modules
- Predictive risk analytics and real-time alert dashboards
Technology stack
What changed
3x data volume, risk reviews from 2 days to 4 hours
The system successfully scaled to handle 3x data volume after the bank merger. The maker-checker compliance workflow is in place and meets RBI regulatory requirements. Real-time financial risk alert dashboards reduced review cycles from 2 days to 4 hours.
The complete technology migration was delivered with zero data loss — 100% data integrity maintained. For a Liferay platform in Islamic finance built on the same Liferay-plus-Spring-Boot stack, see IFMS.
Key results
System successfully scaled to handle 3x data volume post-bank merger
Maker-checker compliance workflow implemented, meeting RBI regulatory requirements
Real-time financial risk alert dashboards reduced review cycles from 2 days to 4 hours
Zero data loss during complete technology migration — 100% data integrity maintained
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