Artificial intelligence in Banking

INVESTMENT

UGX 900000

DURATION

START DATES

DELIVERY MODE

Online

ASSESSMENT

COURSE OVERVIEW

This course delves into the evolving landscape of banking in the age of artificial intelligence. Participants will unravel how AI technologies such as machine learning, natural language processing, and robotics are transforming banking operations, customer service, risk management, and more. The curriculum offers a mix of theoretical understanding, practical applications, and ethical considerations of AI in banking, preparing students for a future where AI-driven solutions dominate the banking sector.

COURSE OBJECTIVE

Use Cases of AI in Banking • Credit scoring and risk assessment • Fraud detection and prevention • Customer service and chatbots • Personalized marketing and recommendations Data and Analytics for AI in Banking • Data collection and preparation for AI • Data privacy and regulatory compliance (GDPR, CCPA) • Data-driven decision-making in banking • Exploratory data analysis and feature engineering Module 5: Challenges and Ethical Considerations • Bias and fairness in AI • Regulatory challenges and compliance

TARGET AUDIENCE

IT and Tech professionals in banking. • Banking professionals • Financial consultants and advisors • Regulatory and compliance professionals • Startup founders in FINTECH.

WHAT YOU WILL STUDY

Upon successful completion of this course, students will be able to: • Understand & Articulate: Describe the core concepts of AI and machine learning and discuss their significance in modern banking. • Evaluate: Critically assess the advantages, challenges, and risks associated with various AI technologies used in banking. • Apply: Demonstrate the ability to apply AI-driven solutions to solve specific banking problems, such as credit scoring, fraud detection, or customer service. • Analyze: Evaluate data sources, data quality, and the potential for data-driven decision-making in banking scenarios. • Ethical Reasoning: Recognize and address the ethical dilemmas related to the use of AI in banking, including issues of bias, transparency, and data privacy. • Project Management: Plan and execute a basic AI project related to banking, ensuring alignment with stakeholder requirements and industry standards. • Future Vision: Predict and discuss future trends in AI and banking, highlighting possible opportunities and disruptions.