AI Applications in Financial Services
Training TypeLive Training
About the Course
Basic concepts related to Data Science, Machine Learning (ML) and Artificial Intelligence (AI)
History of Machine Learning and AI in Financial Services and FinTech
Closer look at several ML and AI financial services use cases (B2B and B2C)
Future trends and its potential impact on the financial services industry - including job displacement and new career options.
Intro to FinTech + Blockchain + AI
AI in Lending & Investment Management
AI in Blockchain & Insurance
RegTech, SupTech and Compliance
Who is the Target Audience?
Students interested in FinTech, AI and the application of these growing technologies to the financial services industry
Working Professionals + Corporate Innovators interested in leveraging Big Data, AI and Analytics to innovate and automate their businesses whether that be investment advice, institutional risk management or banking
Financial Regulators interested in understanding the implications of the development of the FinTech industry and enabling technologies like AI and Blockchain
Licensed Professionals, Investors, Entrepreneurs and Industry Aficionados seeking to accelerate their knowledge of the intersection of FinTech + AI + Finance
No prerequisites for this course.
Upto 9 Attendee 45% Off
10 or More Attendee 50% Off
- How has online lending evolved in the lastdecade?
- What is the field of Lending Analytics?
- Credit Scoring 101
- How are additional data sources used to better assess credit risk
- Can banks automate lending to minutes from days using AI?
- Cases: Kabbage, Affirm, Lending Club,SoFi
- How are BlackRock, Vanguard and Schwab utilizing AI to improve their bottom-lines?
- What are the trends in model-driven investments (e.g. Quant Funds, Smart Beta ETFs)?
- Introduction to factor-based investments
- How are algorithms and big data changing the way investments are made?
- Cases: Motif Investing, OpenInvest
- Introduction to personal finance.
- How AI has improved expense analytics and financial planning
- Cases: Highlights from the earliest examples of personal finance apps using AI for expense analytics. Ex. Mint, Credit Karma
- Can AI help us achieve our financial goals?
- How are model investment portfolios created using data and machine learning
- Cases: Personal Capital, Betterment, Digit, Acorns
- What is Distributed Ledger Technology (DLT)? What is Blockchain?
- Define Cryptocurrencies and Smart Contracts
- How does AI work with Blockchains?
- Can Blockchain prevent insurance fraud through it immutability?
- Case: Numerix, DAO, Bottos
- Key Themes: Real Time Underwriting, Telematics, Usage Based Insurance (UBI)
- Introduction to Insurance Technology
- How are Big Data & AI helping insurers improve risk management and better serve their clients?
- Notable applications of machine learning in insurance
- Case: WeSavvy, Earnix
Introduction to regulatory (regtech) and supervisory technology (suptech)
What is a FinTech Sandbox? How do regulators use them?
What are regulators doing to automate compliance worldwide?
Can supervisors to the industry leverage AI, Big Data and other Financial Innovations to improve their roles?
Cases: Trulioo, Fenergo, Lexis Nexis
How can Big Data and Machine Learning can help a bank:
- Identify good vs bad customers
- Establish a transactional baseline and identify anomalies early-on (e.g. market manipulation)
- Create machine-readable laws and proactive monitoring tools to track bills
- Comply with new regulations while reducing compliance costs