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Predictive Analytics for Banking
YOUR PATHWAY TO SUCCESS
The Predictive Analytics for Banking course explores how data science and machine learning are transforming the banking industry.
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Course Duration
5 Days
Course Details
The Predictive Analytics for Banking course explores how data science and machine learning are transforming the banking industry. Predictive analytics helps banks forecast customer behavior, detect fraud, optimize lending decisions, and enhance risk management.
This five-day course covers key predictive modeling techniques, including regression analysis, machine learning algorithms, and real-time data analytics. Participants will learn how to implement predictive models to improve decision-making in banking operations.
By the end of this course, learners will be able to:
- Understand the fundamentals of predictive analytics.
- Explore machine learning techniques used in banking.
- Build predictive models for loan approvals and risk assessment.
- Use AI-driven analytics for fraud detection.
- Interpret customer behavior trends using predictive analytics.
- Analyze the impact of big data in banking.
- Implement real-time predictive models for decision-making.
- Identify ethical considerations in AI-based banking analytics.
By the end of this course, learners will be able to:
- Understand the fundamentals of predictive analytics.
- Explore machine learning techniques used in banking.
- Build predictive models for loan approvals and risk assessment.
- Use AI-driven analytics for fraud detection.
- Interpret customer behavior trends using predictive analytics.
- Analyze the impact of big data in banking.
- Implement real-time predictive models for decision-making.
- Identify ethical considerations in AI-based banking analytics.
Course Outline
5 days Course
Introduction to Predictive Analytics in Banking
- Overview of predictive analytics and AI in finance
- How banks use predictive modeling
- Case study: Predictive analytics in fraud detection
Machine Learning and Data Processing
- Regression analysis and decision trees
- Data cleaning and feature engineering
- Practical exercise: Building a predictive model in Python
Credit Risk Modeling and Loan Approvals
- Credit scoring models and predictive algorithms
- Assessing risk in loan applications
- Case study: Using AI for credit risk assessment
Fraud Detection and Anomaly Detection
- Identifying fraudulent transactions using machine learning
- Real-time analytics for fraud prevention
Practical exercise: Implementing a fraud detection model
Future Trends and Ethical Considerations
- AI bias and ethical concerns in banking analytics
- The future of predictive analytics in finance
- Course review, assessment, and certification
