Big Data Analysis for Pig E. Bank

Provide analytical support to the anti-money laundering compliance division, evaluating client and transaction risks, and improving metric reporting. Develop and enhance models to optimize compliance efficiency.

Objective: Implementing customer retention strategies by analyzing behavior, enhancing satisfaction levels, and effectively reducing attrition rates.

  • Goals

    Support the anti-money laundering compliance department in data-driven initiatives to assess client and transaction risks, while producing comprehensive metric reports.

    Collaborate on refining analytical models to improve the efficiency and effectiveness of the compliance program.

  • Skills & Tools

    Big data

    Data ethics

    Data mining

    Predictive analysis

    Time series analysis and forecasting

    GitHub

  • Key Focus Areas

    Analytical Support for Anti-Money Laundering Compliance
    Provide analytical support to the anti-money laundering compliance division by evaluating client and transaction risks and improving metric reporting.

    Model Development and Enhancement
    Develop and enhance analytical models to optimize compliance efficiency.

    Customer Retention Strategies
    Implement customer retention strategies through behavior analysis, enhancement of satisfaction levels, and effective reduction of attrition rates.

Analysis

The chart above identifies the factors that are significant in client attrition. The biggest contributing factors include Gender, Age, Account Balance, Number of Products, and if they are an Active Member.

The decision tree above predicts the likeliness of a customer leaving Pig E. Bank.

  • Data Limitations

    Data lacks timestamps, making it impossible to determine if there was a trend of customer loss within a defined period or if it’s a consistent occurrence.

  • Recommendations

    Implement personalized communication strategies and customized offers to effectively engage inactive customers and mitigate churn.

    Enhance product features to improve customer satisfaction and drive higher retention rates.

    Develop targeted retention initiatives specifically for customers aged 35-54, addressing their unique needs to foster increased loyalty.

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