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FinFlow · Financial Technology · 2024
FinFlow's legacy batch-processing pipeline couldn't keep pace with their 40 million daily transactions. We rebuilt it as an event-driven system that processes data in under 200ms — unlocking real-time fraud detection and live P&L dashboards.
Key outcomes — Financial Technology
<200ms
Processing latency
from 18 hours
−94%
Pipeline incidents
vs. previous year
$2.1M
Fraud prevented
in Q1 post-launch
−60%
Engineer on-call
incident load
§ 01 / Problem
FinFlow's data team was running nightly ETL jobs that produced reports 18 hours out of date. As transaction volume grew, jobs regularly failed mid-run, leaving analysts with incomplete data and compliance teams scrambling. Their on-call rotation was spending 60% of their time on pipeline incidents rather than building new capabilities.
§ 02 / Approach
We began with three weeks of system archaeology — running the existing ETL jobs in shadow mode, tracing every data dependency, and interviewing the analysts who depended on the output. That gave us a precise map of what needed real-time processing versus what could tolerate batch latency. We designed the new event-driven architecture iteratively with FinFlow's data engineers, running both systems in parallel for six weeks before switching the production dependency.
§ 03 / Solution
We designed a Kafka-based event streaming architecture with stateful Flink processors for fraud signals and a Snowflake data warehouse fed by CDC from their PostgreSQL operational database. A React dashboard with WebSocket streaming gives risk analysts live exposure views. The entire infrastructure is defined in Terraform and deployed via a zero-downtime GitHub Actions pipeline.
§ 04 / Result
Pipeline incidents dropped by 94%. Fraud detection latency went from T+18h to under 200ms, directly preventing an estimated $2.1M in fraudulent transactions in the first quarter post-launch. The data team redirected 60% of their on-call time to building new analytical products.
<200ms
Processing latency
from 18 hours
−94%
Pipeline incidents
vs. previous year
$2.1M
Fraud prevented
in Q1 post-launch
−60%
Engineer on-call
incident load
Tech stack
stackloader didn't just fix our data pipeline — they changed how our engineering team thinks about reliability. The incident rate dropped by 94%, but the bigger win was that our engineers stopped dreading Mondays. That cultural shift is hard to put a dollar figure on.