Learning objectives: Describe the drivers that have contributed to the growing use of Fintech and the supply and demand factors that have spurred adoption of AI and machine learning in financial services. Describe the use of AI and machine learning in the following cases: customer-focused uses ...
Questions:
904.1. The Financial Stability Board's Financial Innovation Network (FSB FIN, November 2017) observes that "artificial intelligence and machine learning (AI&ML) are being rapidly adopted for a range of applications in the financial services industry." Specific use cases of AI&ML include (i) customer-focused applications; (ii) operations-focused uses; (iii) trading and portfolio management; and (iv) regulatory compliance and supervision. Further, according to FSB FIN, each of the following statements is true EXCEPT which is inaccurate?
a. Deep learning can be used for supervised, unsupervised, or reinforcement learning
b. The key risk of artificial intelligence is that its ability to contextualize implies it will soon be able to fully replicate human intelligence and therefore eventually replace humans
c. Machine learning is a sub-category of artificial intelligence (AI) that extends familiar statistical methods and generally deals with optimization, prediction and categorization but not causal inference
d. Reinforcement learning falls in between supervised and unsupervised learning, and it feeds an unlabeled dataset to the algorithm which chooses an action and then receives human feedback that helps it learn
904.2. In regard to the drivers that have contributed to the growing use of Fintech and the supply and demand factors that have spurred adoption of AI and machine learning in financial services, each of the following statements is true EXCEPT which is false?
a. A key supply factor is the declining cost of data storage and corresponding growth in datasets
b. Key demand factors include profitability (ie, cost reduction, risk management gains, productivity improvements), competition, and regulatory compliance
c. A key supply factor is weak-form efficient markets due to a lack of threshold structured data, a factor which is theoretically temporary because AI and machine learning should eventually arbitrage it away
d. Regulatory compliance is a salient demand factor (i.e., RegTech) but legal frameworks will be a complicating--and possibly dampening--factor on several fronts such as liability, anti-discrimination and credit system interpretability
904.3. The FSB FIN briefly discusses three customer-focused use cases: credit scoring applications, insurance-related technologies (aka, InsurTech including insurance policies), and client-facing chatbots. About the use of artificial intelligence and machine learning (AI&ML) specifically in these customer-focused use cases, which of the following statements is TRUE?
a. The black box is good because it will reduce discrimination
b. Machine learning algorithms are likely to reduce access to credit
c. Machine learning-based credit scoring models decisively outperform traditional credit models in over 90.0% of cases
d. In the insurance industry, AI&ML can improve profitability (via risk-based pricing and reduced costs) and augment underwriting and claims processing functions
Answers here:
Questions:
904.1. The Financial Stability Board's Financial Innovation Network (FSB FIN, November 2017) observes that "artificial intelligence and machine learning (AI&ML) are being rapidly adopted for a range of applications in the financial services industry." Specific use cases of AI&ML include (i) customer-focused applications; (ii) operations-focused uses; (iii) trading and portfolio management; and (iv) regulatory compliance and supervision. Further, according to FSB FIN, each of the following statements is true EXCEPT which is inaccurate?
a. Deep learning can be used for supervised, unsupervised, or reinforcement learning
b. The key risk of artificial intelligence is that its ability to contextualize implies it will soon be able to fully replicate human intelligence and therefore eventually replace humans
c. Machine learning is a sub-category of artificial intelligence (AI) that extends familiar statistical methods and generally deals with optimization, prediction and categorization but not causal inference
d. Reinforcement learning falls in between supervised and unsupervised learning, and it feeds an unlabeled dataset to the algorithm which chooses an action and then receives human feedback that helps it learn
904.2. In regard to the drivers that have contributed to the growing use of Fintech and the supply and demand factors that have spurred adoption of AI and machine learning in financial services, each of the following statements is true EXCEPT which is false?
a. A key supply factor is the declining cost of data storage and corresponding growth in datasets
b. Key demand factors include profitability (ie, cost reduction, risk management gains, productivity improvements), competition, and regulatory compliance
c. A key supply factor is weak-form efficient markets due to a lack of threshold structured data, a factor which is theoretically temporary because AI and machine learning should eventually arbitrage it away
d. Regulatory compliance is a salient demand factor (i.e., RegTech) but legal frameworks will be a complicating--and possibly dampening--factor on several fronts such as liability, anti-discrimination and credit system interpretability
904.3. The FSB FIN briefly discusses three customer-focused use cases: credit scoring applications, insurance-related technologies (aka, InsurTech including insurance policies), and client-facing chatbots. About the use of artificial intelligence and machine learning (AI&ML) specifically in these customer-focused use cases, which of the following statements is TRUE?
a. The black box is good because it will reduce discrimination
b. Machine learning algorithms are likely to reduce access to credit
c. Machine learning-based credit scoring models decisively outperform traditional credit models in over 90.0% of cases
d. In the insurance industry, AI&ML can improve profitability (via risk-based pricing and reduced costs) and augment underwriting and claims processing functions
Answers here:
