Credit Systems Production • Active

High-Throughput Credit Operations

Secure, low-latency systems designed for data integrity and sustained operational trust in Nigeria's credit ecosystem. Supporting 7M+ users across payment processing, credit eligibility, and operational workflows.

The Challenge

Credit operations at scale face competing pressures: users need instant responses, data must stay consistent, and the system cannot lose track of money or eligibility decisions. A failure in any part of the chain—from user-facing API to internal settlement—can cause cascading problems.

  • Throughput: Millions of transactions per day across multiple product lines
  • Latency: User-facing responses required in under 100ms for eligibility checks
  • Consistency: Credit decisions must be auditable and reversible without data loss
  • Availability: System degrades gracefully; core flows never go dark

System Architecture

The system is built as a layered, fault-tolerant architecture with clear boundaries between concerns:

API Gateway & Load Balancer Eligibility Service Real-time checks Cache layer Transaction Service Debit/Credit flows Settlement Audit Service Decision log Compliance PostgreSQL Source of truth Transactional Redis Session cache Rate limits Kafka Event log Async workflows TimescaleDB Metrics Audit trail Key Performance Targets • P99 Latency: <80ms (eligibility), <150ms (transaction) • Availability: 99.95% uptime (0 data loss events) • Throughput: 50K+ requests/sec, 1M+ daily transactions • Consistency: ACID guarantees for all credit movements

Design Decisions

Separation of Concerns

Each service owns one domain: eligibility reads don't block transactions, transactions don't block auditing. This lets us scale and fail independently.

Event Sourcing for Critical Flows

Every credit decision is logged as an immutable event. If something breaks, we can replay the log and always know what happened and why.

Cache Strategy

Eligibility results are cached, but with short TTLs and constant validation against the source. If the cache diverges from truth, we refresh it.

Graceful Degradation

If Redis goes down, the service keeps working slower. If Kafka fills up, we circuit-break non-critical workflows. Core flows stay alive.

Operations & Monitoring

The system runs on Kubernetes with auto-scaling. Each service has dedicated alerting for latency, error rates, and data consistency checks. We run continuous chaos testing to find failure modes before users do.

  • Auto-scaling: scales up on traffic spike, down during low periods
  • Circuit breakers: protect downstream services from cascade failures
  • Distributed tracing: every request is traced end-to-end for debugging
  • Regular audits: monthly reconciliation of all transactions

Results & Impact

7M+ Active users supported
99.95% Uptime (3 years)
0 Data loss events
50K/sec Peak throughput sustained

The system has become a trusted backbone for the ecosystem. It handled the leap from 1M to 7M users without degradation and continues to scale.

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