CFA Exam Adds Fintech, Big Data, and Data Analysis to 2019 Curriculum
Come 2019, the wealth managers and financial analysts aspiring to add the Chartered Financial Analyst designation to their credentials have one more subject to deal with. The CFA Institute has decided to add fintech to its 2019 exam curriculum. The new curriculum contains a section called fintech and adds study material on hot industry topics such as robo advisors, big data, artificial intelligence and data analysis. The new questions will appear in the CFA exam that will be administered in 2019.
The existing CFA syllabus already addresses many areas of financial technology such as trading systems, private wealth and quantitative methods. The new additions will make the overall CFA syllabus richer and equip the candidates with the knowledge they need in the fast changing financial world dependent on technology. For example, the new syllabus will have a reading on robo-advisors which talks about the new ways in which financial advice is provided to clients.
While fintech has a broad scope, there are four key relevant areas: These are:
- Financial analysis technology: This includes how the financial analysis landscape is changing with things such as big data analysis, artificial intelligence, machine learning, and algorithmic trading.
- Portfolio management technology: This includes robo-advisors, technology in enterprises such as asset management companies
- Capital formation: This includes peer-to-peer lending, shadow banking, and crowd funding.
- Market Infrastructure: This includes innovations such as cryptocurrencies, blockchain technology, high-frequency trading, and regulatory-related technology.
As an example, the robo-advisors are computer algorithms that help retail investors build and manage their portfolios with least human interaction. Such businesses have been on the rise since 2012 and now considered mainstream in the retail investment world.
Addition on fintech into the CFA exam syllabus is a welcome move. Investment managers are already collecting and processing huge amounts of data, for example, satellite images of activity in parking lots, weather data, tourism data, and more and use this data to guide them in their investment decisions. The addition of topics such as big data, data analysis, machine learning will boost the skillset of these investment managers.