Dispute Processing Automation: 60% Fewer Defects and 45% Lower Costs

+30%
Performance
−45%
Operational Cost
−60%
Defects
0
Sequencing Failures

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Full Stack Engineering Team
Dedicated Delivery
Dedicated Team
Engagement Model
Main Technologies
Java Apache Spark AWS Jenkins SonarQube REST APIs
Industry
Fin-Tech

The Summary

A global banking institution faced critical instability in its dispute processing pipeline. Events were arriving out of order, duplicate records were being processed, and concurrency was creating inconsistencies in a revenue sensitive system.

A dedicated engineering team designed and implemented an automated event handling system using Java and Spark on AWS. The solution validated event sequencing, eliminated duplicates, and ensured deterministic processing under high load.

The result: 30 percent performance improvement, 45 percent reduction in operational cost, 60 percent fewer defects, and a stable dispute processing system delivered within two sprints.

client

Max Fanwick

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warning
The Challenge

Out of order event processing, duplicate records, and operational risk in a live dispute handling system under concurrency.

setting
The Solution

End to end automation for event validation, deduplication, and sequencing built on Java, Spark, and AWS under strict delivery timelines.

result
The Result

30% faster processing, 45% lower operational cost, 60% fewer defects, and stable dispute workflows under concurrency.

The Challenge

Dispute processing is directly tied to financial accuracy and customer trust.

The system relied on event driven architecture.

Under concurrent load, it started to break.

  • Events arrived out of order
  • Duplicate records were processed
  • Downstream systems received inconsistent data
  • Manual corrections increased operational overhead
  • System load increased due to redundant processing

The system worked, but it was unreliable under pressure. And it was live

Goal
Success Was Clearly Defined
  • Validate dispute events before processing
  • Eliminate duplicate records at ingestion
  • Ensure correct sequencing of event flows
  • Reduce manual intervention and operational cost
  • Deliver within a strict two sprint timeline

The Solution

“Determinism over patchwork.”

The focus was not on fixing symptoms.

It was on controlling event behavior under concurrency.

Execution was built around one principle
Every dispute must be processed correctly, every time

Java
Apache Spark
AWS
Jenkins
SonarQube
REST APIs
Execution In Practice
1
Review
Event flow architecture analyzed. Failure points in sequencing and duplication identified
2
Validate
Automated intake validation introduced to filter incorrect or malformed events
3
Deduplicate
Deduplication logic implemented at ingestion to eliminate redundant records
4
Sequence
Event ordering enforced before downstream processing to ensure consistency
5
Automate
End to end automation built for dispute handling without manual dependency
6
Monitor
Real time monitoring integrated using AWS services for visibility and control

The Outcome

The dispute processing system became stable, predictable, and scalable.

  • Event flows became deterministic
  • Manual correction effort reduced significantly
  • System load optimized
  • Downstream consistency improved
30%
Performance Improvement
45%
Reduction in Operational Cost
60%
Reduction in Defects
2 Sprints
Delivery Timeline Achieved
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