Python for Banking Operations and Compliance

Program Description

Rationale:

• Python has become the de facto programming language for financial services due to its simplicity, versatility, and powerful libraries for data analysis, automation, and compliance reporting. In banking operations, Python enables:
  • Automation of repetitive tasks such as reconciliations, transaction monitoring, and report generation.
  • Data analysis for compliance (AML/KYC checks, suspicious transaction detection, regulatory reporting).
  • Risk management and fraud detection using machine learning models.
  • Integration with core banking systems to streamline workflows.
• For Ghanaian banks, where regulatory compliance, operational efficiency, and digital transformation are top priorities, Python provides a practical toolkit to modernize processes while aligning with Bank of Ghana directives, ISO/IEC 27001, and the Ghana Data Protection Act.

Course Objective:

• Explain the role of data as a strategic asset in digital transformation.
• Develop a data strategy aligned with organizational digital goals.
• Apply data governance principles (ownership, stewardship, quality, security, compliance).
• Interpret Ghana’s Data Protection Act and map it to ISO/IEC standards.
• Evaluate data quality and lifecycle management practices.
• Design a mini data strategy and governance framework for their institution.
• Reflect on ethical perspectives in handling organizational data.

Course Content:

• Module 1: Introduction to Python for Banking
  • Why Python in financial services
  • Setting up the Python environment (Anaconda/Jupyter)
  • Python basics: variables, data types, loops, functions
• Module 2: Data Handling for Bank Operations
  • Working with CSV, Excel, and SQL databases
  • Data cleaning and transformation with pandas
  • Hands-on: Importing and cleaning transaction data
• Module 3: Automating Banking Tasks
  • Automating reconciliations and daily reports
  • Generating compliance dashboards in Excel with Python
  • Hands-on: Automating a daily transaction summary
• Module 4: Compliance and Risk Applications
• • AML/KYC checks with Python
  • Detecting suspicious transactions (rule-based + ML intro)
  • Regulatory reporting automation (Bank of Ghana templates)
  • Case study: Suspicious transaction monitoring
• Module 5: Integration and Advanced Applications
• • Connecting Python to core banking systems via APIs
  • Using Python for audit trails and log analysis
  • Hands-on: Building a compliance reporting script
• Module 6: Capstone Project
• • Group project: Automate a compliance or operational workflow
  • Presentation and peer review
  • Wrap-up: Future of Python in banking compliance.
    

Learning Outcome:

• By the end of this course, participants will be able to:
• Understand Python fundamentals relevant to banking operations.
• Automate routine banking tasks (reconciliations, transaction summaries, compliance reports).
• Use Python libraries (e.g., pandas, NumPy, openpyxl) for financial data analysis.
• Apply Python for AML/KYC checks, fraud detection, and regulatory reporting.
• Integrate Python scripts with Excel, databases, and core banking systems.
• Develop a mini project that demonstrates automation of a compliance or operational workflow