The month-end failure that cost a bank $2.3 million.
A digital banking platform degraded between the 25th and 30th of every month. The pattern repeated for six months before Edge was deployed. Inside the first 24 hours, the cause was found.
What month-end looked like
The existing tools alerted only after customers were affected. The operations team resorted to emergency infrastructure scaling, which proved both costly and ineffective.
The constraint was not capacity. It was visibility.
Transaction failures rose from 0.5% to 12%
A twenty-four fold increase over three days
Mobile response times went from 800ms to over 15 seconds
An eighteen-fold degradation under month-end load
Over 50,000 complaints in three days
Call volumes rose 400%
Social media backlash, with #BankDown trending
Reputational damage compounding the financial loss
An estimated $2.3 million in lost revenue
From failed bill payments and transfers alone
Regulatory concern over service level breaches
The regulator requested a full incident report
What Edge found in the first 24 hours
Query time up 450% at peak
- Balance inquiries 8 to 12 seconds instead of 200ms
- N+1 query pattern causing 50+ database calls per session
- Missing indexes on high-volume tables
Connection pool exhaustion
- 95% of 500 connections saturated at peak
- Over 200 deadlocks per hour
- Memory pressure triggering garbage collection delays
Payment processing bottlenecks
- Payment microservice could not handle month-end concurrency
- Thread pool exhaustion causing request queuing
- No circuit breaker on the third-party gateway calls
Partners were failing quietly
- Gateway responses past 8 seconds
- Credit bureau 15% failure rate
- SMS provider queue 5 to 10 minutes
- Fraud system adding 2 seconds per transaction
After the Edge-driven optimisations
"The constraint was never capacity. It was visibility."
Four layers, one investigation
The reason the failure was not caught earlier was not a lack of monitoring. It was a lack of correlation. Four separate tools, each watching one layer, none of them connected.
Edge found the root cause in 24 hours because it correlates database, infrastructure, application and third-party signals in a single investigation, not four.
Discuss your own peak periodContext
In banking, a one second delay can reduce transactions by 7%. In e-commerce, every 100ms costs about 1% of sales. After a major outage, churn rises 20 to 30%. The numbers from this case study are not unusual. They are the expected consequence of uncorrelated monitoring.
Figures come from Edge product documentation and describe one documented scenario. The client is not named, and results depend on your environment.