Artificial Intelligence in Fintech
Hybrid (Online & In-Person)
|
BHD 2,000 | SAR 20,000 *excl. VAT
Unlock the power of applied generative AI to transform financial institutions and drive innovation.
Duration
8 Weeks
Dates
6 Sep - 29 Oct, 2026
Location
Reboot Coding Institute
Earn a Certificate of Achievement
About the Course
This eight-week course is designed for non-technical participants interested in exploring how AI and Generative AI transform the fintech industry. Through hands-on, project-based learning, participants will gain practical skills in applying AI tools to real-world financial problems. The course covers key topics such as AI in payments, lending, risk management, and personalized financial services. The program is primarily held online with a few in-person sessions at Reboot Coding Institute.
Learning Outcomes
Understand how AI and Generative AI are applied in fintech.
Collaborate on project-based fintech applications using AI and Generative AI.
Gain hands-on experience with AI tools for payment systems, lending, and financial risk management.
Build confidence in leveraging AI for non-technical financial roles and business innovation.
Develop AI-driven solutions for fintech use cases like wealth management, compliance, and insurance.
Who is This Course for?
Non-technical Professionals in Fintech, Finance, and Banking.
Modules
Session 1: Overview of AI and its Role in Fintech
-
Definition of AI, machine learning, and generative AI
-
AI applications in financial services: payments, lending, insurance, wealth management
-
The transformative potential of AI in fintech
-
Case studies of AI in finance
Session 2: Fundamentals of AI: Key Concepts
-
Introduction to data-driven decision-making
-
Key AI technologies: machine learning, natural language processing, and computer vision
-
Overview of AI lifecycle: data collection, model building, and deployment
-
Session 3: Machine Learning Fundamentals for Fintech
-
Supervised vs. unsupervised learning
-
Application of machine learning models in financial forecasting and risk assessment
-
Understanding credit scoring models and fraud detection
Session 4: Hands-On Introduction to Machine Learning
-
Basic hands-on exercises in data analysis and model training (simplified using no-code/low-code tools)
-
Demo of a machine learning tool in the financial context
-
Session 5: Introduction to Generative AI
-
What is generative AI and its core components
-
Use cases of generative AI in fintech: personalized financial services,= chatbot development, and automated content generation
Session 6: Generative AI and Chatbots in Financial Services
-
Exploring AI chatbots for customer service and financial advice
-
Case studies of chatbot deployment in fintech companies
-
Session 7: The Role of Data in AI and Fintech
-
Importance of data quality and data governance in AI applications
-
Introduction to data analytics tools in fintech
-
Compliance and privacy concerns related to financial data
Session 8: Data-Driven Insights for Financial Decision-Making
-
Practical example: building basic financial dashboards using data analytics tools
-
Real-world case study: leveraging AI for personalized financial product recommendations
-
Session 9: AI for Fraud Detection in Fintech
-
Machine learning algorithms for detecting fraudulent transactions
-
AI-driven risk-scoring models
-
Case study: AI tools used by banks and payment processors
Session 10: Implementing AI for Risk Management
-
Identifying risks using predictive modeling
-
AI applications for credit risk, operational risk, and liquidity risk management
-
Demo: Basic predictive risk model using AI tools
-
Session 11: AI in Payments
-
How AI optimizes payment processing systems
-
Use cases: AI-driven payment gateways and fraud prevention
-
Case studies: PayPal, Stripe, and AI-driven payment solutions
Session 12: AI for Lending and Credit Scoring
-
AI’s impact on peer-to-peer lending and microfinance
-
Credit scoring models powered by AI
-
Case studies on AI in digital lending platforms
-
Session 13: Regulatory Landscape for AI in Finance
-
Overview of financial regulations impacting AI adoption
-
Compliance with data protection laws (e.g., GDPR, PSD2)
-
Addressing algorithmic bias and fairness in AI models
Session 14: Ethical AI and Responsible Innovation in Fintech
-
Importance of transparency, fairness, and accountability in AI models
-
Ethical considerations in customer data usage
-
Best practices for deploying ethical AI in fintech
-
Session 15: Future Trends in AI for Fintech
-
Trends shaping the future of AI in finance: decentralized finance (DeFi), blockchain, AI-driven asset management
-
How AI and fintech will evolve in the coming years
Session 16: Capstone Project Presentation and Course Wrap-Up
-
Team-based or individual capstone projects
-
Presentations and feedback ○ Course review and next steps
-
Modules
(In-person sessions)
Session 1: Overview of AI and its Role in Fintech
-
Definition of AI, machine learning, and generative AI
-
AI applications in financial services: payments, lending, insurance, wealth management
-
The transformative potential of AI in fintech
-
Case studies of AI in finance
Session 2: Fundamentals of AI: Key Concepts
-
Introduction to data-driven decision-making
-
Key AI technologies: machine learning, natural language processing, and computer vision
-
Overview of AI lifecycle: data collection, model building, and deployment
-
(Online sessions)
Session 3: Machine Learning Fundamentals for Fintech
-
Supervised vs. unsupervised learning
-
Application of machine learning models in financial forecasting and risk assessment
-
Understanding credit scoring models and fraud detection
Session 4: Hands-On Introduction to Machine Learning
-
Basic hands-on exercises in data analysis and model training (simplified using no-code tools)
-
Demo of a machine learning tool in the financial context
-
(Online sessions)
Session 5: Introduction to Generative AI
-
What is generative AI and its core components
-
Use cases of generative AI in fintech: personalized financial services, chatbot development, and automated content generation
Session 6: Generative AI and Chatbots in Financial Services
-
Exploring AI chatbots for customer service and financial advice
-
Case studies of chatbot deployment in fintech companies
-
(Online sessions)
Session 7: The Role of Data in AI and Fintech
-
Importance of data quality and data governance in AI applications
-
Introduction to data analytics tools in fintech
-
Compliance and privacy concerns related to financial data
Session 8: Data-Driven Insights for Financial Decision-Making
-
Practical example: building basic financial dashboards using data analytics
-
Real-world case study: leveraging AI for personalized financial product recommendations
-
(Online sessions)
Session 9: AI for Fraud Detection in Fintech
-
Machine learning algorithms for detecting fraudulent transactions
-
AI-driven risk-scoring models
-
Case study: AI tools used by banks and payment processors
Session 10: Implementing AI for Risk Management
-
Identifying risks using predictive modeling
-
AI applications for credit risk, operational risk, & liquidity risk management
-
Demo: Basic predictive risk model using AI tools
-
(Online sessions)
Session 11: AI in Payments
-
How AI optimizes payment processing systems
-
Use cases: AI-driven payment gateways and fraud prevention
-
Case studies: PayPal, Stripe, and AI-driven payment solutions
Session 12: AI for Lending and Credit Scoring
-
AI’s impact on peer-to-peer lending and microfinance
-
Credit scoring models powered by AI
-
Case studies on AI in digital lending platforms
-
(Online sessions)
Session 13: Regulatory Landscape for AI in Finance
-
Overview of financial regulations impacting AI adoption
-
Compliance with data protection laws (e.g., GDPR, PSD2)
-
Addressing algorithmic bias and fairness in AI models
Session 14: Ethical AI and Responsible Innovation in Fintech
-
Importance of transparency, fairness, and accountability in AI models
-
Ethical considerations in customer data usage
-
Best practices for deploying ethical AI in fintech
-
(Online sessions)
Session 15: Future Trends in AI for Fintech
-
Trends shaping the future of AI in finance
-
How AI and fintech will evolve in the coming years
Session 16: Capstone Project Presentation and Course Wrap-Up
-
Team-based or individual capstone projects
-
Presentations and feedback
-
Course review and next steps
-
The AI in FinTech course exceeded expectations; the right balance of technical depth and real-world application. The hands-on sessions and case studies were genuinely impactful, and building my project on AI for NASDAQ historical data analysis made the learning tangible and immediate. Beyond the content, the cohort itself was a highlight: a sharp, forward-thinking community that made collaboration as valuable as the curriculum. An exceptional program for anyone serious about AI-driven financial services.
Abdulaziz Aldayaf
CEO, MicroProcess
The UC Berkeley AMENA AI in FinTech workshop bridged complex AI concepts and real banking applications in a way that was immediately actionable. The hands-on use of no-code tools to build predictive risk models and explore generative AI; without needing a technical background; was a standout. I left with a clear framework for ethical AI and data-driven decision-making. Essential for any finance professional navigating the future of banking.
Abeer Albaitam
VP Risk Management, ila Bank
Instructor
Dr. Tanya Roosta
Fellow and Lecturer at UC Berkeley | Senior Research Science Manager at Amazon AI