Unit5- Data and Techfin
Page 1: Title
DATA AND TECH FIN UNIT-5
Page 2: Content Overview
1. History of Data Regulation
2. Data in Financial Services
3. Digital Identity
4. Change in Mindset: Regulation 1.0 to 2.0 (KYC to KYD)
5. AI & Governance
6. New Challenges of AI and Machine Learning
7. Data, Metadata and Differential Privacy
8. Data is the New Oil: Risk of Breach
Page 3: History of Data Regulation
1970s: Early Beginnings
Germany's Hesse Data Protection Act (1970)
US Privacy Act (1974)
1980s: Framework Development
OECD Guidelines (1980)
European Convention on Data Protection (1981)
Page 4: Continuation of History
1990s: Rise of the Internet
EU Data Protection Directive (1995)
US Safe Harbor Agreement (1998)
2000s: Globalization
Significant data privacy laws worldwide
US-EU Safe Harbor Invalidated (2015)
Page 5: Modern Regulations
2010s: Stricter Regulations
EU General Data Protection Regulation (GDPR, 2018)
California Consumer Privacy Act (CCPA, 2020)
Present and Future
Focus on cross-border data transfers and global standards
Page 6: Data in Financial Services
Data drives efficiency and innovation in financial services.
Page 7: Data Roles in Financial Services
Customer Insights and Personalization
Customer profiling and personalized financial products
Risk Management
Credit scoring, fraud detection, and market risk analysis
Page 8: Further Data Applications in Finance
Regulatory Compliance
Essential for laws like AML and KYC
Improved Decision Making
Data-driven investment strategies and predictive analytics
Page 9: More Contributions of Data
Operational Efficiency
Automation and cost reduction in financial transactions
New Financial Products
Innovations in fintech and blockchain technologies
Page 10: Customer Experience and Security
Customer Experience
Seamless transactions and user interface improvements
Data Security and Privacy
Cybersecurity measures and compliance with data regulations
Page 11: AI in Finance
Predictive Models and Robo-Advisors
Challenges in Data Use
Data silos, quality issues, and regulatory barriers
Page 12: Digital Identity
Importance of digital identity in online interactions.
Page 13: Defining Digital Identity
Collection of data defining individuals in the virtual world.
Page 14: Components of Digital Identity
Identifiers and Authentication: Key roles in identity verification.
Data Points and Attributes: Various data contributing to digital identity.
Page 16: Types of Digital Identities
Attribute-based, biometric, anonymized, and federated identities described.
Page 17: Examples of Digital Identities
Social media profiles, online banking credentials, e-commerce profiles, and government-issued IDs.
Page 18: Importance in Financial Services
Critical role in verification and trust in banking.
Page 19: Trust and Compliance
Digital identity’s role in regulatory compliance and fraud prevention.
Page 20: Future of Digital Identity
Increasing integration and sophisticated management required in the digital world.
Page 21: Benefits of Digital Identity
Security enhancement, reduced human reliance, and cost savings outlined.
Page 22: Use of Digital Identity
Integral to banking processes like age verification and transaction authentication.
Page 23: Verification Process Explained
Steps involved in digital identity verification such as ID record checks and biometric verification.
Page 25: Choosing a Service
Factors to consider: technology, user experience, inclusivity, and fraud prevention.
Page 26: IAM and Digital Identity
Digital identities essential for identity and access management systems.
Page 27: Who Uses Digital Identities?
Relevant across various sectors: retailers, healthcare, governments, and cloud providers.
Page 29: Common Types of Digital Identities
Device, payment, email, social media, user/account, and online reputation identities discussed.
Page 30: Protecting Digital Identities
Key steps include avoiding phishing, securing personal information on public Wi-Fi, and strong passwords.
Page 32: Regulation 1.0 (KYC)
Traditional framework focusing on identity verification during customer interactions.
Page 33: Regulation 2.0 (KYD)
Emphasis on data-driven approaches and continuous monitoring.
Page 34: Advantages of KYD
Highlights include real-time risk detection, enhanced customer experience, and better fraud prevention.
Page 35: Transition Necessity
Reasons for shifting from KYC to KYD due to evolving financial crime complexity and data explosion.
Page 37: Additional Advantages of KYD
Lists benefits like efficiency in compliance and scalability.
Page 38: Challenges for KYD
Privacy, regulatory compliance, high costs, and data quality issues noted.
Page 43: AI and Governance Overview
Discussion on ethical, legal, and societal implications of AI governance.
Page 44: Key Aspects of AI Governance
Importance of policies, stakeholder engagement, and transparency established.
Page 47: Need for AI Governance
Highlights ethical challenges, safety, accountability, and the impact of AI on society.
Page 51: Defining AI
Development of systems for tasks requiring human intelligence described.
Page 52: Key Components of AI
Major elements of AI like machine learning, NLP, computer vision, and robotics outlined.
Page 56: Challenges in AI and ML
Discusses ethical implications, transparency issues, data challenges, and security risks.
Page 67: What is Data?
Explanation of data as raw facts critical to AI and ML.
Page 68: Metadata Definition
Metadata defined as data describing data, aiding in understanding and decision-making.
Page 73: Differential Privacy
Technique for protecting individual identities in data analysis while sharing insights.
Page 76: "Data is the New Oil"
Discussion on the value of data and risks, especially regarding data breaches.
Page 78: Risks of Data Breaches
Losses, reputation damage, regulatory consequences, and national security risks elaborated.
Page 80: Mitigation Strategies
Recommendations: Encryption, access control, audits, anonymization, and incident response plans.
Page 81: Conclusion
Emphasizes the need for robust data protection measures in the current digital economy.