Implementing a Fraud Detection System with Levenshtein Distance in a Django Project

Levenshtein distance can be used in a fraud detection system to compare user-entered data (such as name, address or email) with existing data in order to identify similar but potentially fraudulent entries.

Here is a step-by-step guide to integrating this functionality into your Django project.

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1. Use Case

A fraud detection system can compare:

• Similar emails: to detect accounts created with slight variations (e.g., user@example.com vs. userr@example.com).
• Near Addresses: To check if multiple accounts are using nearly identical addresses.
• Similar Names: to spot users with slightly modified names (e.g., John Doe vs. Jon Doe).

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2. Steps for Implementation

a. Create Middleware or Signal to Analyze Data

Use Django's signals to check for new user data at the time of registration or update.

b. Install a Levenshtein Calculation Function

Integrate a library to calculate the Levenshtein distance or use a Python function like this:

from django.db.models import Q
from .models import User  # Assume User is your user model

def levenshtein_distance(a, b):
    n, m = len(a), len(b)
    if n > m:
        a, b = b, a
        n, m = m, n

    current_row = range(n + 1)  # Keep current and previous row
    for i in range(1, m + 1):
        previous_row, current_row = current_row, [i] + [0] * n
        for j in range(1, n + 1):
            add, delete, change = previous_row[j] + 1, current_row[j - 1] + 1, previous_row[j - 1]
            if a[j - 1] != b[i - 1]:
                change += 1
            current_row[j] = min(add, delete, change)

    return current_row[n]

c. Add a Fraud Detection Feature

In your signal or middleware, compare the entered data with that in the database to find similar entries.

from django.db.models import Q
from .models import User  # Assume User is your user model

def detect_similar_entries(email, threshold=2):
    users = User.objects.filter(~Q(email=email))  # Exclure l'utilisateur actuel
    similar_users = []

    for user in users:
        distance = levenshtein_distance(email, user.email)
        if distance <= threshold:
            similar_users.append((user, distance))

    return similar_users

d. Connect to Signal post_save for Users

Use the post_save signal to run this check after a user registers or updates:

from django.db.models.signals import post_save
from django.dispatch import receiver
from .models import User
from .utils import detect_similar_entries  # Import your function

@receiver(post_save, sender=User)
def check_for_fraud(sender, instance, **kwargs):
    similar_users = detect_similar_entries(instance.email)

    if similar_users:
        print(f"Potential fraud detected for {instance.email}:")
        for user, distance in similar_users:
            print(f" - Similar email: {user.email}, Distance: {distance}")

e. Option: Add a Fraud Log Template

To keep track of suspected fraud, you can create a FraudLog model:

from django.db import models
from django.contrib.auth.models import User

class FraudLog(models.Model):
    suspicious_user = models.ForeignKey(User, related_name='suspicious_logs', on_delete=models.CASCADE)
    similar_user = models.ForeignKey(User, related_name='similar_logs', on_delete=models.CASCADE)
    distance = models.IntegerField()
    created_at = models.DateTimeField(auto_now_add=True)

Save suspicious matches in this template:

from django.db.models import Q
from .models import User  # Assume User is your user model

def levenshtein_distance(a, b):
    n, m = len(a), len(b)
    if n > m:
        a, b = b, a
        n, m = m, n

    current_row = range(n + 1)  # Keep current and previous row
    for i in range(1, m + 1):
        previous_row, current_row = current_row, [i] + [0] * n
        for j in range(1, n + 1):
            add, delete, change = previous_row[j] + 1, current_row[j - 1] + 1, previous_row[j - 1]
            if a[j - 1] != b[i - 1]:
                change += 1
            current_row[j] = min(add, delete, change)

    return current_row[n]

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3. Improvements and Optimizations

a. Limit Comparisons

• Compare only recent users or those from the same region, company, etc.

b. Adjust Threshold

• Set a different threshold for acceptable distances depending on the field (for example, a threshold of 1 for emails, 2 for names).

c. Use of Advanced Algorithms

• Explore libraries like RapidFuzz for optimized calculations.

d. Integration into Django Admin

• Add alerts in the admin interface for users with potential fraud risks.

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4. Conclusion

With this approach, you have implemented a fraud detection system based on the Levenshtein distance. It helps identify similar entries, reducing the risk of creating fraudulent accounts or duplicating data. This system is expandable and can be adjusted to meet the specific needs of your project.

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