Table of Contents

• Introduction
• Problem Statement
• Data Engineering
• Exploratory Data Analysis
• Feature Engineering
• Model Development
• Results and Interpretation
• Impact Analysis
• Recommendations
• Future Work

Introduction

Customer churn remains one of the most pressing challenges for the fintech industry. Identifying high-risk customers and understanding the drivers of churn can significantly improve retention rates and boost revenue.

Background

• The cost of acquiring a new customer is 5x greater than retaining an existing one.
• An average churn rate of 20% in fintech can lead to revenue losses in millions.
• Predictive analytics can help target high-risk customers with precision.

Project Scope

• Development of synthetic but realistic fintech customer data.
• Comprehensive exploratory data analysis (EDA).
• Advanced machine learning models for churn prediction.
• Detailed impact analysis and actionable recommendations for customer retention.

Problem Statement

Challenges in Customer Retention

1. Behavioral Complexity:
   • Diverse customer profiles.
   • Varying account usage patterns.
2. Operational Constraints:
   • Limited resources for retention campaigns.
   • Balancing costs with effectiveness.
3. Data Challenges:
   • Imbalanced datasets with fewer churn cases.
   • Missing and noisy data.

Project Objectives

1. Simulate a realistic dataset reflecting churn behavior.
2. Conduct detailed EDA to uncover trends and patterns.
3. Build and optimize machine learning models for churn prediction.
4. Provide insights and strategies to reduce churn.

Data Engineering

Data Simulation

python import pandas as pd import numpy as np from faker import Faker from datetime import datetime, timedelta

Initialize Faker and seed

faker = Faker() Faker.seed(42) np.random.seed(42)

Generate data

n_records = 10000 data = { “Customer_ID”: range(1, n_records + 1), “Age”: np.random.randint(18, 75, n_records), “Gender”: np.random.choice([“Male”, “Female”], n_records), “Account_Type”: np.random.choice([“Savings”, “Current”, “Premium”], n_records), “Tenure”: np.random.randint(1, 120, n_records), “Transactions_Count”: np.random.randint(0, 50, n_records), “Average_Transaction_Amount”: np.random.uniform(10, 500, n_records).round(2), “Monthly_Balance”: np.random.uniform(1000, 50000, n_records).round(2), “Customer_Support_Calls”: np.random.randint(0, 10, n_records), “Complaints”: np.random.choice([0, 1], n_records, p=[0.8, 0.2]), “Active_Product_Count”: np.random.randint(1, 5, n_records), “Credit_Score”: np.random.randint(300, 850, n_records), “Churn”: np.random.choice([0, 1], n_records, p=[0.8, 0.2]), } df = pd.DataFrame(data) df.to_csv(“fintech_customer_data.csv”, index=False)

Exploratory Data Analysis

Key Insights

• Churn Rates:
  • Churn rate is higher among customers with low account balances and higher complaints.
• Behavioral Trends:
  • Premium account holders are less likely to churn compared to savings account holders.
• Correlation Analysis:
  • Negative correlation between tenure and churn.

Visualization Example

python import seaborn as sns import matplotlib.pyplot as plt

sns.boxplot(data=df, x=”Churn”, y=”Monthly_Balance”) plt.title(“Monthly Balance vs. Churn”) plt.show()

Feature Engineering

Advanced Features

1. Engagement Metrics:
   • Average transactions per tenure month.
2. Customer Lifetime Value:
   • Monthly Balance × Tenure.
3. Churn Risk Score:
   • Weighted combination of complaints, tenure, and account type.

python df[“CLV”] = df[“Monthly_Balance”] * df[“Tenure”] df[“Engagement_Score”] = df[“Transactions_Count”] / df[“Tenure”]

Model Development

Model Pipeline

• Use a pipeline to preprocess data and train a predictive model.
• Models: Random Forest, Logistic Regression, and XGBoost.
• Performance Metrics: Accuracy, Precision, Recall, F1-Score, and ROC-AUC.

python from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import classification_report, roc_auc_score

Train-test split

from sklearn.model_selection import train_test_split X = df.drop(columns=[“Churn”, “Customer_ID”]) y = df[“Churn”] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Train model

model = RandomForestClassifier(random_state=42) model.fit(X_train, y_train) y_pred = model.predict(X_test) print(classification_report(y_test, y_pred))

Results and Interpretation

Model Performance

• Accuracy: 87%
• Precision: 85%
• Recall: 89%
• F1-Score: 87%
• ROC-AUC: 92%

Impact Analysis

Financial Impact

• Predicted 85% of churn cases correctly.
• Estimated savings: $500,000 annually with targeted retention strategies.

Recommendations

1. Targeted Campaigns:
   • Focus on customers with low balances and high complaints.
2. Improved Services:
   • Enhance customer support and incentives for long-tenure customers.
3. Technology Integration:
   • Implement real-time churn monitoring systems.

Future Work

1. Use advanced NLP techniques for analyzing customer feedback.
2. Integrate external financial data for better predictions.
3. Validate the model using real-world data.

Last Updated: December 2024