While artificial intelligence (AI) isn’t able to solve every problem, there is good reason for the hype around it in the financial-services business today, according to the vice president of Fidelity Institutional.
“[T]here are two common uses that I think will provide the most clarity around the technology,” wrote Andrew Brzezinski in a column published on ThinkAdvisor. He identified the applications of natural language processing and generation, which includes a machine’s ability to understand what it’s being asked via text or voice, as well as recommendation engines, which generate a best suggestion or answer based on an input or question.
“AI can also be used to identify anomalies in data, isolate or identify features and objects inside videos and images, and more,” he continued.
He pointed to the current use of AI to power chatbots and virtual assistants, to enhance customer service by predicting why a client is calling, and to assist advisors with suggested next best actions for specific client cases.
As for future opportunities in AI, Brzezinski said it could help advisors determine natural niches of clients outside of traditional groupings, estimate the lifetime value of prospects and clients, forecast attrition within an advisory firm, and predict upcoming life events like a wedding or a child going to college.
“A package of capabilities like this will help advisors be more efficient with their time, drive business goals and potentially help to scale their books of business,” he declared.
But even with all its potential, Brzezinski said, AI is not going to replace people. It can be programmed to significantly take on routine tasks, as well as help identify priorities, enhance advisors’ performance in their roles, and mitigate risk. But in the end, it is only able to augment financial professionals’ judgment, intuition, and creativity so that they can focus on helping clients reach higher-level goals like fulfilling their life’s purpose or leaving a legacy.
He also drew a distinction between AI and descriptive analytics, which he said will continue to be vital in business management. “The real opportunity is for AI to complement descriptive analytics to help generate predictive insights and support business decision-making,” he said. And in certain scenarios, a situation posed as an AI use case can be solved more simply through other means such as data analysis.
“Good use cases for AI solve business problems, have data ready to be applied, have limited downside if the algorithm is wrong and (of course) are ethical,” he concluded.
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