Fraud Detection Analysis

On This Page

• C15.1  Project Overview
• C15.2  Business Problem
• C15.3  Dataset Description
• C15.4  Objectives
• C15.5  Step-by-Step Solution
• C15.6  Complete Python Code
• C15.7  Expected Output
• C15.8  Visualizations
• C15.9  Key Insights
• C15.10  Business Recommendations

Capstone Project 15: Fraud Detection Analysis

C15.1  Project Overview

This final capstone project analyzes transaction data to identify patterns associated with fraudulent activity — a foundational, rule-based approach to fraud analytics before moving into machine learning.

C15.2  Business Problem

A payments company wants to understand what typically distinguishes fraudulent transactions from normal ones — by amount and by time of day — to build simple early-warning flagging rules.

C15.3  Dataset Description

Sample scope: 500 transactions, including 20 confirmed fraud cases.

C15.4  Objectives

• Compare transaction amounts between normal and fraudulent transactions
• Analyze whether fraud rate varies by hour of day
• Identify simple, explainable rules that flag likely fraud
• Recommend a practical monitoring approach for the business

C15.5  Step-by-Step Solution

• Step 1: Load the transaction dataset into Pandas
• Step 2: Compare amount distributions for Normal vs Fraud using a box plot
• Step 3: Calculate fraud rate by hour of day
• Step 4: Define a simple rule-based flag (e.g. amount > threshold)
• Step 5: Test the rule's accuracy against known fraud labels
• Step 6: Summarize a practical fraud-monitoring recommendation

C15.6  Complete Python Code

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("transactions.csv")

avg_by_label = df.groupby('label')['amount'].mean()
print("Average Amount - Normal vs Fraud:\n", avg_by_label.round(2))

fraud_rate_by_hour = df.groupby('hour')['label'].apply(lambda x: (x=='Fraud').mean()*100)
print("Fraud Rate % by Hour:\n", fraud_rate_by_hour.round(1))

# Simple rule-based flag
threshold = df[df['label']=='Fraud']['amount'].quantile(0.25)
df['flagged'] = df['amount'] > threshold

accuracy = (df['flagged'] == (df['label']=='Fraud')).mean() * 100
print(f"Simple Rule Accuracy: {accuracy:.1f}%")

C15.7  Expected Output

C15.8  Visualizations

C15.9  Key Insights

• Fraudulent transactions average Rs.8,650 — nearly 6x higher than the average normal transaction (Rs.1,480), making amount a strong initial flagging signal.
• Fraud shows no single obvious time-of-day concentration in this sample, suggesting amount is a more reliable signal than timing alone for this dataset.
• A simple amount-threshold rule alone can catch a meaningful share of fraud cases, showing that even basic rule-based analytics provide real protective value before adding ML models.

C15.10  Business Recommendations

• Implement an immediate manual-review flag for any transaction above the identified amount threshold, as a simple first line of defense.
• Continue collecting labeled fraud data to eventually train a proper machine learning classification model, using this rule-based analysis as the baseline to beat.
• Combine amount-based rules with other signals (device, location, transaction frequency) in future iterations for a more robust, layered fraud-detection system.