Topic 9.1 - 9.5: Big Data & Machine Learning in the Financial Industry Flashcards
Identify factors that may contribute to increases in third party dependencies
among financial institutions.
- network effects
- scalability of new technologies
Explain why unexpected forms of interconnectedness among institutions
could be created.
Institutions’ ability to make use of big data from new sources lead to dependencies on previously unrelated macro variables, financial prices
Explain why new forms of macro-level risks could emerge.
Algorithms generating uncorrelated returns will be exploited on a scale whereby correlations will increase
Describe the two recent developments that have contributed to increased
interest in AI.
Advances in the availability of financial sector data and infrastructure
List factors contributing to making the markets more efficient
More efficient processing of information (e.g. credit decisions, financial markets, insurance contracts) may contribute to a more efficient financial system.
Describe the relationship between AI, machine learning, and the three algorithms appearing in Figure 1.
machine learning is a sub-category of AI.
Describe the relationship between AI, machine learning, and the three algorithms
appearing in Figure 1.
Categories of Machine Learning:
- Supervised learning (algo is fed labeled training data)
- Unsupervised learning (algo is fed unlabeled training data)
- Reinforcement learning (falls between sup and unsup).
- Deep learning (works in layers)
Describe the role of machine learning algorithms in determining causality
vs. correlation.
Machine learning cannot determine causality but they can identify correlations.
Define ‘augmented intelligence.’
augmentation of human capabilities (rather than replacing them).
Discuss the supply factors related to advances in computing technologies
and changes in the financial sector.
- computing power, lower hardware costs, and better access to computer power via cloud services.
Discuss the demand factors related to the search for higher profits, increased
competition, and changes in the regulatory environment.
Profitability: Potential for cost reduction, revenue gains, risk reduction
Competition: “arms-race” with other firms
Regulation: Prudential regulations, data reporting, AML
Describe customer-focused uses, such as credit scoring, insurance, and client-facing chatbots.
Credit scoring - ML designed to speed up lending decisions, while limiting risk.
Insurance - analyze complex data to lower cost and improve profitability.
Client-facing chatbots - automated programs use NLP to interact with clients in natural language.
Describe operations-focused uses, such as optimal capital allocation,
risk management modeling, and market impact analysis.
Capital optimization - Maximisation of profits given scarce capital.
risk management modeling - back-testing and model validation. (banks use it to evaluate how well their risk models are performing).
market impact analysis - obtain more information from sparse historical models or help identify non-linear relationships in order flow.
Describe portfolio management and trading uses.
- identify new signals
- make more use of vast amount of data available
Describe regulatory compliance and supervision uses by financial institutions,
central banks, macroprudential authorities, and market regulators.
- RegTech: facilitates compliance more efficiently and effectively (ML+NLP)
- Monitoring behavior of traders
- Interpret data inputs (e-mails, spoken word, IM)
- To cope with new regulations
How is machine learning used in KYC
- evaluating images in identifying documents
2. calculate risk scores to determine who needs to receive additional scrutiny.
Benefit of artificial intelligence and machine learning in information
gathering and processing their potential impacts on financial markets.
AI/ML can enhance the efficiency of information processing, reducing information asymmetries. This has the potential to strengthen the information function the financial system.
Describe the uses of artificial intelligence and machine learning in information
gathering and processing their potential impacts on financial markets.
- collect & analyze information on a greater scale to better understand the relationship between market prices and various factors.
- Lower market participants trading costs
Describe the uses of artificial intelligence and machine learning in improving
the efficiency of financial institutions.
AI/ML can increase profitability and reduce risk:
- enhance machine based processing of various operations
- Earlier and more accurate estimation of risk
- Encourage collaboration between FIs
Describe the uses of artificial intelligence and machine learning by financial
institutions and their potential impacts on customers and investors.
- Lower fees
- Wider access to financial services
- Customized/personalized financial services
Describe economic growth and enhanced economic efficiency that could result
from artificial intelligence and machine learning applications to financial services.
ML/AI can contribute to efficiency and growth of the economy through:
- Enhancing the efficiency of financial services
- Facilitating collaboration and realizing new economies of scope
- Stimulating investments in AI and ML
Describe the implications of uses of artificial intelligence and machine learning
by financial institutions for market concentration and the systemic importance of
those institutions.
- Emergence of small number of advanced third-party providers which increases concentration
- Access to big data might be of systemic importance/only big firms can afford large investments for ML/AI.
Describe how financial institutions’ uses of artificial intelligence and machine
learning could be sources of greater instability and vulnerability in financial markets
- trading algos might be less predictable
- HFT might increase market volatility through large sales or purchases
- Minimizing of margins may increase risks
Describe how the uses of artificial intelligence and machine learning by the
insurance industry could affect both moral hazard and adverse selection problems.
- using AI/ML to continuously adjust insurance fee in accordance with behavior of policy holder reduces moral hazard.
- Offer customized insurance policy reduces adverse selection.
Algorithm
set of computational rules to be followed to solve a mathematical problem
Artificial intelligence
the theory and development of computer systems able to perform tasks that traditionally have required human intelligence
Augmented intelligence
augmentation of human capabilities with technology
Cluster analysis
Statistical technique where by data are classified into distinct groups