The CFA Institute, a global association of investment professionals, has introduced a guide for AI and Big Data services in investments, published by the CFA Institute Research Foundation. The guide details how asset managers use AI and big data technologies to streamline the investment process and enhance investments and business performance.
With contributions from data scientists and investment leaders at market leaders, the CFA Institute Research Foundation AI handbook provides a detailed insight into the investment industry’s adoption of data science to offer investment plans to deliver more resilient portfolios, make more informed trading decisions, streamline client plans, generating client-focused services and create additional business intelligence.
Margaret Franklin, the CEO of the CFA Institute, explains that their business considers the combination of AI and human intelligence a winning formula for success in finance in the future. As AI and big data solutions become more pronounced in financial markets, industry leaders must be well-prepared to effectively measure and incorporate these services. Franklin hopes the AI handbook will support the industry in adopting AI and big data solutions meaningfully to benefit their customers.
AI handbook details
The AI handbook is presented from the industry perspective, including real-world examples and tested solutions. Larry Cao, senior director of research at the CFA Institute, explains that industry requirements have expanded from asking for details on how AI and big data work to requesting an action plan supporting their business strategy as AI and ML measures become part of the mainstream. The AI guide is the latest in a series of research from the CFA Institute, focusing on supporting practitioners and policymakers with the necessary services to evaluate and implement AI and big data to the highest standards.
No single operating model for data science integration can work for all finance and asset management businesses. Technology must adapt to work for culture, structure, core values, budgets and strategic priorities. The guide will support companies in commencing, refining or planning the next stage of their data science vision.