Self-Hosted Billing Anomaly Detection with FastAPI and Stripe Webhooks

Introduction

Billing systems are the backbone of any subscription-based business. However, they can be vulnerable to anomalies, such as unexpected usage spikes or incorrect charge calculations. In this article, we'll explore building a self-hosted billing anomaly detection system using FastAPI and Stripe webhooks.

Why Self-Hosting?

While SaaS solutions like Stripe Billing offer ease of integration, they also come with limitations on customization, control, and cost. By hosting the solution ourselves, we can ensure:

1. Data Privacy: Protecting sensitive customer data from unauthorized access.
2. Cost Control: Avoiding unnecessary subscription fees or charges for usage beyond our needs.
3. Customization: Tailoring the system to meet our specific billing requirements.

Architecture

Our system will be built around the BillingWatch architecture:

1. Stripe Webhook Processing: Receiving and processing webhooks from Stripe for events like charge creations, updates, or deletions.
2. Anomaly Detection Logic: Analyzing incoming data to detect anomalies based on predefined rules (e.g., unexpected usage spikes).
3. Alert Dispatch: Sending notifications to designated recipients upon detecting an anomaly.

Code Snippets

Here are some code snippets showing how we can implement these components:

Webhook Handler

from fastapi import FastAPI, Request
from pydantic import BaseModel

app = FastAPI()

class StripeEvent(BaseModel):
    id: str
    type: str

@app.post("/stripe_webhook/")
async def webhook_handler(request: Request):
    # Receive and process the incoming webhook data
    data = await request.json()
    event = StripeEvent.parse_obj(data)
    if event.type == "charge.succeeded":
        # Trigger anomaly detection logic for successful charges
        await detect_anomaly(event.id)

#### Anomaly Detection Logic

python
import pandas as pd

def load_billing_data():
return pd.read_csv('billing_data.csv')

def detect_anomaly(charge_id):
data = load_billing_data()
charge = data[data['charge_id'] == charge_id]
# Implement custom rules for anomaly detection (e.g., unexpected usage spikes)
if charge['usage_spikes'].max() > 10:
raise AnomalyException(f"Anomaly detected: Usage spike of {charge['usage_spikes'].max()} units")

Alert Dispatch

python
import smtplib

def dispatch_alert(subject, message):
    server = smtplib.SMTP('smtp.gmail.com', 587)
    server.starttls()
    server.login("[e-mail]", "your_password")
    server.sendmail("[e-mail]", "[e-mail]", f"Subject: {subject}\n\n{message}")

### Conclusion

In this article, we've built a comprehensive self-hosted billing anomaly detection system using FastAPI and Stripe webhooks. By leveraging the power of custom alert dispatch, anomaly detection logic, and Stripe webhook processing, our system provides an efficient way to detect and respond to anomalies in our billing systems.

Note: Make sure to replace placeholder values (e.g., `[e-mail]`) with actual credentials when implementing this solution.