Revolutionizing Wealth Management with Artificial Intelligence
Artificial Intelligence (AI) is currently disrupting several sectors of the economy and finance is no exception. There are many interesting uses of AI in the finance sector, including use cases with disruptive potential and opportunities for both consumers and financial institutions. These use cases span the front office (e.g., chatbots), the middle office (e.g., Know Your Customers) and the back-office (e.g., credit risk assessment) of financial institutions.
Semi-automated or fully automated wealth management based on roboadvisors is one of the back-office applications. Robo-advisors are AI-based software agents for investment decisions. They can be considered as robots that provide investment advice and can be used to take faster, more intelligent and more automated investment decisions. Their outcome can be a set of recommendations, as well as accompanying explanatory models that explain certain decisions and ultimately improve the asset managers’ knowledge.
Roboadvisors can assist individual traders, external asset managers and wealth management departments of banks in offering cost-effective services and to compete against institutional investors. Specifically, they improve risk management processes and help reducing front-office and back-office costs. Furthermore, they could help wealth management professionals to provide high quality asset management to customers with lower portfolios, instead of giving service to high wealth customers only. Also, they can provide a basis for personalizing investment recommendations.
The technology behind robo-advisors includes Big Data infrastructures (e.g., data warehouses and data lakes) that comprise large volumes of investment/trading data, customers’ data, as well as alternative data. Artificial Intelligence algorithms (i.e. Machine Learning, Deep Learning) algorithms such as Regression Models, Random Forest, Artificial Neural Networks (ANNs) / Recurrent Neural Networks (RNNs) and Boosting meta-algorithms are commonly used for producing prescriptive (investment) recommendations and risk assessments.
The H2020 INFINITECH project develops a scalable and high performance Big Data infrastructure that will enable various personalized asset management use cases, aiming at effective risk assessment, automated investment recommendation and construction of highly personalized investment portfolios for retail customers.
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