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Many of us may use Artificial Intelligence (AI) daily, such as asking Apple’s Siri about a local sushi joint, but behind the scenes, machine learning is a powerful tool that is shaping financial technology or fintech. When it comes to the private wealth industry, AI may be used to build better lead management, better databases, and better systems overall.
AI is at its essence a set of computer systems that can process and analyze data. In the finance industry, these systems can be used to assess stock markets, trends, and to create predictions based on past events. While similar to standard algorithms, AI can learn from the data it receives and predicts realistic outcomes.
In the finance sector, AI is becoming a necessity for banks and wealth management services. The term ‘private wealth’ generally refers to individuals with over $20 million in invested assets and stocks. These clients are more demanding than smaller entities, looking for personalized solutions to better manage their portfolios. Unlike corporate entities, they are potentially finite as well.
Though AI may seem like an almost magical term, a deus ex machina to fix all the world’s financial and global problems, it does come at a hefty financial cost. In Japan, Fukoku Mutual Life Insurance cut over 30 employee jobs in favor of using an AI system. Though paying staff would set the company back 140 million yen annually, implementing the system cost 200 million yen to install and a further 150 million yen annually to maintain. Though, it was ultimately to the benefit of the company.
The old way of generating leads was similar to pretty much every other sale-focussed industry. Wealth managers would use cold calling, direct mail, events, or other tedious methods to find prospective clients. Those avenues may have worked in a pre-2010 world, and are still being used today, but they are outdated and mostly dead in the water due to the advancement of technology and global communication.
With AI, financial managers can use learning tools to assess potential clients and their portfolios for greater lead management. Systems may also be put in place to generate a substantial increase in relevant leads, such as website forms, while an AI would filter out warm leads to approach.
Due to the flexibility of AI, bespoke solutions may be created not just for companies, but for the private wealth industry as well. For example, if a client only wants to invest in stock associated with tech companies, an AI solution would look at all the tech companies on the New York Stock Exchange, analyze past financial trends, and create predictions based on outcomes.
AI can also be used to analyze not just raw financial data, but real-world events as well. The changing of a company’s CEO or the launch of a new product can be factored into financial calculations to figure out which companies are worth an investment.
Machine learning isn’t a perfect solution, but it does yield fewer mistakes than a human being would.
According to CB Insights, there are over 100 companies in the US using AI in the financial services industry, ranging from debt collection to insurance.
For example, New York-based Kasisto uses an AI chatbot to help users with banking. Regardless of whether a client asks a question about analyzing their spending, or managing different accounts across platforms, Kasisto’s deep AI knows the ins and outs of banking. The start-up must be doing something right as it recently raised $9.2 million in funding.
Incubators and accelerators are starting to take note of AI’s potential in business. The Hong Kong-based Zeroth.AI was created to nurture AI-based start-ups, while Go Ignite, which is primarily based out of the US, focusses on AI in the telecoms space.
Should AI be used in the further evolution of fintech and better lead management in the private wealth industry? Yes. The applications are almost limitless, allowing for a range of ways to process, sift through, and analyze financial data, resulting in an overall better economy.
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