A lightweight, real-time ML model classifies transactions based on amount, time, merchant string, and user history — continuously learning from corrections to deliver instant, accurate categorization at scale.
Live Transaction Feed
LIVEStarbucks #4521
Today, 9:42 AM
-$5.75
Food & DrinkAWS Cloud Services
Today, 8:15 AM
-$247.50
BusinessWhole Foods Market
Yesterday, 6:30 PM
-$89.23
GroceriesSpotify Premium
Yesterday, 12:00 PM
-$9.99
EntertainmentProcessing rate
2,847 tx/sec
Transactions automatically categorized without user intervention
End-to-end inference latency for real-time categorization
Continuous learning from user corrections, improving accuracy daily
The Problem
The Solution
The Pipeline
Transaction data is parsed: amount, timestamp, merchant string, location, and user's historical patterns are extracted in real-time.
A lightweight neural network processes features and predicts category with confidence scores. Optimized for sub-50ms latency.
Category is pushed to the user's dashboard in real-time. High-confidence predictions are auto-applied; low-confidence ones prompt for review.
User corrections feed back into the model. The system learns from each interaction, improving accuracy and personalization over time.
Stack
Outcomes
96% auto-categorization rate
Up from 68% with rule-based systems.
<50ms end-to-end latency
Real-time categorization at scale.
12 min/week saved per user
Eliminated manual categorization work.
+34% user engagement
Users spend more time in the app with better insights.
Let's design a lightweight ML pipeline that delivers instant, accurate transaction categorization at scale.