AI Transaction Failure Detection

// THE CHALLENGE

In a high-volume mobile money system, transaction failures are inevitable — but most are discovered the worst way: a customer complains, support escalates, and engineers reconstruct what happened from logs hours later.

The patterns are there in the data long before the complaints arrive. The challenge is detecting them in real time across millions of transactions, and diagnosing the likely cause — a degraded partner API, a misbehaving release, a network issue — instead of just raising an alarm.

// THE SOLUTION

I am architecting an AI-powered detection and diagnosis system that learns the normal behavior of transaction flows and flags anomalies as they emerge: failure-rate shifts, latency drift, unusual error clusters by channel, partner, or transaction type.

Beyond detection, the system is designed to diagnose — correlating anomalies with deployments, partner status, and infrastructure signals to point operators at the probable cause, turning hours of log forensics into minutes of confirmation.

// THE OUTCOME

Currently in the architecture phase at a major Tanzanian telecom fintech. This is the kind of applied, operations-first AI I build: not a chatbot bolted onto a brand, but a system that protects revenue and customer trust.