CareEdge: a predictive credit-risk Early Warning System on Spring Boot microservices and Angular 14
India
A leading credit analytics firm needed to see borrower stress before it became a non-performing asset, across several banks' portfolios at once. ATS designed and built a full-stack Early Warning System — Spring Boot microservices for the analytics, Angular 14 for the risk dashboard, REST APIs into five core banking systems — that processes 100,000+ borrower records a day.
The challenge
Backward-looking reports in a business that needs foresight
CareEdge advises banks on credit risk, and its value depends on timing: a relationship manager who learns of borrower stress after the account has slipped into non-performing status can only manage the loss. The legacy systems in use produced backward-looking reports with no real-time predictive capability, so intervention before NPA escalation was not possible in practice.
The firm needed a mission-critical platform that would proactively identify potential credit risks in bank borrower portfolios — ingesting data from several sources and several banks, applying rules that risk officers could tune themselves, and putting the resulting signals in front of banking officials as they arose. ATS built it as a full-stack application on Spring Boot microservices with an Angular 14 front end.
Our approach
Ingest everything, let risk officers own the rules
Delivered by our Full-Stack Engineering practice.
ATS designed and built the EWS end to end. The backend ingests multi-source borrower data — financial statements, transaction history and external bureau data — and applies a predictive rule engine with configurable thresholds to it. Rather than hard-coding risk logic, the engine exposes its thresholds to non-technical risk officers, so the people who understand credit can adjust the signals without a release.
Alerts are delivered in real time to banking officials through an Angular 14 dashboard built as a responsive single-page application. RESTful APIs were architected for integration with bank core banking systems so that data flows in and signals flow back without disrupting the banks' existing platforms. The result is a system that surfaces the borrower who needs a call today, not a report on who needed one last quarter.
- Multi-source ingestion: financial statements, transaction history, external bureau data
- Predictive rule engine with configurable thresholds
- Real-time alert dashboard for banking officials (Angular 14 SPA)
- Secure REST APIs into bank core banking systems
- Multi-bank data integration without legacy disruption
Architecture
Java 11, Spring Boot microservices, Angular 14, Oracle, REST
The platform is Java 11 throughout the backend: Spring Boot microservices for ingestion, the rule engine and alerting, deployed on Tomcat with Oracle DB as the system of record and GitHub for source control. Splitting the analytics into services is what lets the system keep ingesting and scoring at volume — 100,000+ borrower records a day — while queries from the dashboard return in under three seconds.
The front end is an Angular 14 single-page application consuming the same REST APIs that the bank integrations use, so there is one contract for data in and signals out. It is the shape we favour for full-stack engineering in regulated finance: a services backend with a clear API boundary, a modern SPA on top, and integration through secure REST rather than shared databases. The same Spring Boot-plus-Angular stack, on Liferay, runs the Bank of Baroda EWS.
Building blocks
- Java 11 · Spring Boot microservices · Tomcat
- Angular 14 SPA front end
- Oracle DB · GitHub
- Predictive rule engine with configurable thresholds
- REST APIs for core banking integration
- Real-time alert dashboard
Technology stack
What changed
NPA escalation response 60% faster, 100,000+ records a day
Real-time credit risk alerts reduced NPA escalation response time by 60%. The configurable rule engine allows non-technical risk officers to manage thresholds independently. The system integrated with 5 core banking systems via secure REST APIs without disrupting the banks' legacy platforms.
It scales to process 100,000+ borrower records daily with sub-3-second query response. For another risk platform built for a bank on Spring Boot and Angular, see Bank of Baroda; for the services behind it, see Spring Boot development.
Key results
Real-time credit risk alerts reduced NPA escalation response time by 60%
Configurable rule engine allowed non-technical risk officers to manage thresholds independently
Integrated with 5 core banking systems via secure REST APIs without legacy disruption
System scaled to process 100,000+ borrower records daily with sub-3-second query response
From the blog
All articlesBuilding an Early Warning System on Spring Boot
How we build banking early warning systems on Spring Boot microservices: multi-bank ingestion, rules risk officers own, maker-checker audit, real-time alerts.
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