AI and Digital Resources in Fintech: Creating an evolutionary analytic platform for “risk” estimation
This article describes the potential for AI to augment risk estimation for both individual investors and financial market assets. AI processes vast amounts of a variety of data to identify patterns underpinning processes and metrics. Evolving data resources including digital touch points provide AI with attributes that can enhance risk estimation to ultimately augment elements of modern portfolio theory.
There has been heightened emphasis on the utilisation of AI in various applications throughout industries. This algorithmic learning and replication technique that processes data to learn how activities work, has reached intense depths given the creation of vast data resources in the evolving digital era. The results of AI initiatives have been both mixed and alarming as applications including voice recognition, identifying fraud, understanding consumer propensities and replicating tasks performed by humans have intrigued business leaders and heightened concerns in the workforce as to the potential of labour displacement.
One particular AI-based initiative is in the financial sector that falls in the realm of FinTech. This has been the creation of robo-advisors or the use of algorithms to enhance the activities of financial advisers interacting with investors regarding asset allocations among various financial market based assets (e.g. equities, fixed income, commodities, etc) for wealth and retirement management. These platforms can augment the tool set of “human advisors” and also introduce a more total technology interface (e.g. evolving robo-advisors) that investors can access for investment advice.
The AI analytics “under the hood” of this technically intensive initiative processes vast data resources that describe investor attributes and financial market asset classes. Algorithms can be used to identify relationships that can exist between investor stage in life (similar to a product life cycle approach), wealth and risk appetite or attributes of foreign exchange based products, stocks, bonds etc. domestically or globally.
About the Author
Dr. Stephan Kudyba is an Associate Professor at the New Jersey Institute of Technology, Martin Tuchman School of Management. His research and teaching focus is in analytics and MIS. He has published numerous books and articles addressing data analytics and strategy. He has over 15 years of private sector experience developing computerised trading systems in the financial markets and devising data mining based strategic solutions for organidations across industry sectors.
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