Mentor Based
AI in BFSI Risk Management
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Course Overview
Program Syllabus
Module 1
About This Course
This course offers a blend of intensive learning and practical application, spanning from foundational AI concepts to complex regulatory compliance and ethical considerations.
Module 2
Aim
This course aims to equip participants with a deep understanding of how artificial intelligence (AI) revolutionizes risk management within the banking, financial services, and insurance (BFSI) sectors. It focuses on both theoretical knowledge and practical insights to manage and leverage AI technologies effectively.
Module 3
Program Objectives
- To provide foundational knowledge of AI and its impact on risk management in BFSI.
- To explore AI applications in mitigating various types of risks including credit, market, and operational risks.
- To develop practical skills in deploying AI tools and technologies through hands-on workshops, case studies, and project work.
- To understand the regulatory and ethical considerations of deploying AI in financial services.
Module 4
MODULE 1 : Introduction to AI in BFSI
- Overview of AI Technology: Understanding the basics of AI, including machine learning, deep learning, and natural language processing (NLP).
- Impact of AI on BFSI: Exploring how AI is transforming risk management, customer service, and operational efficiency within the financial sectors.
- Case Studies and Applications: Examination of real-world applications of AI in major banks and insurance companies.
Lead Instructor
Dr. Sarah Chen
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
Limited SeatsClosing Soon
AI in BFSI Risk Management
Professional Certification Program
🎥
FormatLive + Recorded
📅
Duration8 Weeks
📜
CertificationVerified
Module 5
MODULE 2 : Types of Risks in BFSI and AI Applications
- Identifying and Categorizing Risks: Detailed analysis of credit, market, operational, liquidity, and compliance risks.
- AI in Risk Assessment and Management: How AI tools are used to assess and mitigate risks.
- Integrating AI into Risk Management Strategies: Insights into AI-driven tools for risk identification and real-world examples of AI in action.
Module 6
MODULE 3 : Foundations of Machine Learning and Data Analytics
- Machine Learning Models: Introduction to supervised and unsupervised learning models and their applications in BFSI.
- Data Analytics in Risk Assessment: Techniques for data collection, preprocessing, and visualization in risk management.
- Practical Exercises: Hands-on exercises using Python and data analytics libraries to analyze real-world BFSI data.
Module 7
MODULE 4 : Advanced AI Applications in Risk Management
- Deep Learning and NLP: Using advanced AI technologies for risk assessment and managing unstructured financial data.
- Systemic Risk and Predictive Analytics: Techniques for identifying and predicting systemic risks using AI.
- Interactive Workshops: Building AI models and using tools like TensorFlow and PyTorch for practical applications in fraud detection and credit scoring.
Module 8
MODULE 5 : Regulatory Compliance and Ethical AI
- AI and Regulatory Frameworks: Understanding how AI fits within GDPR, CCPA, and other regulatory standards.
- Ethics and Bias in AI Models: Addressing ethical challenges and biases in AI development, with methods to ensure fairness.
- Case Studies on AI in Compliance: Exploring AI applications in Know Your Customer (KYC) and Anti-Money Laundering (AML) processes.
Module 9
MODULE 6 : Real-World Applications and Case Studies
- AI-Powered Risk Management Solutions: Detailed reviews of successful AI implementations in risk management across global banks and insurers.
- Innovations in AI Strategies: Analysis of how top financial institutions are leveraging AI for better risk assessments and customer interactions.
- Strategic Insights and Practical Examples: Bridging theoretical knowledge with practical applications through extensive case studies.
Module 10
MODULE 7 : Developing AI Solutions for Risk Management
- Building AI Projects: From ideation and feasibility studies to project management and execution tailored to AI implementations in BFSI.
- Integrating AI Into Existing Systems: Technical and strategic considerations for effectively deploying AI solutions.
- Capstone Project: A comprehensive project where participants apply what they’ve learned to a real-world challenge in BFSI risk manageme
Module 11
Who Should Enrol?
This course is intended for professionals and students in the BFSI sector seeking to enhance their expertise in AI-driven risk management and financial compliance.
Module 12
Program Outcomes
- Comprehensive understanding of AI technologies applicable in BFSI.
- Ability to implement AI solutions to real-world risk management problems.
- Enhanced capability to navigate the regulatory landscape affecting AI in BFSI.
- Skills to lead AI projects and innovations within financial institutions.
Module 14
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
Module 15
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Module 16
Publication Opportunity
Get published in a prestigious open-access journal.
Module 17
Centre of Excellence
Become part of an elite research community.
Module 18
Networking & Learning
Connect with global researchers and mentors.
Module 19
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
Module 20
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