Understanding the Uses of Machine Learning and AI in Finance
In this episode, we continued our ongoing series on fintech in Asia by interviewing David Hardoon, the Chief Data Officer of the Monetary Authority of Singapore (MAS). We spoke with him about the innovative uses of machine learning and the leveraging of big data among banks and the financial system more broadly.
David walked us through how the new Data Analytics Group at MAS is approaching the ethical use of data when so many financial institutions are employing new AI applications. We also discussed the need for awareness of the potential for unsupervised algorithms to either help or hinder financial inclusion. Some of our key takeaways include:
- One MAS initiative, FEAT, is focused on four guiding principles for financial institutions concerning the usage of data in AI innovations: fairness, ethics, accountability, and transparency.
- Machine learning can be applied in wide range of financial services from insurance to wealth management, for example, by using behavioral data to lower premiums and by offering algorithm-based robo advisory services that market services to a wider swath of people.
- Regulators ought to ensure financial institutions understand the risks involved in using algorithms and unsupervised machine learning – are these risks acceptable?
- Applications of AI – artificial intelligence – will more likely augment, not replace, financial jobs of the future. The technology is likely to transform the services provided by banks and the roles of bank branches, and will alter the relationship between banks and customers and underscore the importance of data.