Retaining clients is the third activity. Einstein monitors client-related activities to spot patterns that indicate which clients are at risk for leaving the adviser. IBM's Watson Client Insight for Wealth Management solution provides a similar forecast. "We're able to predict with a high degree of accuracy when clients are going to leave the firm -- 30, 60, 90 days ahead of time," says Stanich. Smaller firms are also integrating AI into private wealth management. ForwardLane in London and New York City is combining quantitative investment models and financial planning. Responsive Capital Management in Vancouver offers its Alpha Digital Advisor Platform and AI Research Platform. And New York City-based Synechron has developed its Neo suite of AI services for financial advisers, which includes natural language processing, chatbots, machine learning, robo-advisers, and other services.
The era of artificial intelligence means machines could conceivably be better than fund managers at investing clients' money. It may also level the playing field between large and small firms, finds Kit Klarenberg. Last year, mainstream commentators declared the era of artificial intelligence (AI) had arrived. Major investments in AI were announced by Facebook, Google and Microsoft, with the promise of ground-breaking consumer applications to follow. Financial professionals may have wondered what the fuss was all about.
James Williams, managing editor at Hedgeweek, assesses how data analytics techniques can be used to personalise client experiences for investment managers. The amount of data is growing exponentially. According to IDC, there were 16.3 zettabytes of information generated in 2017 alone; one zettabyte is 1 billion terabytes. However you cut it, that's a huge number. One that is too large to comprehend. In simplistic terms, according to one industry professional "if every piece of data were a penny, it would cover the earth's surface five times over". Indeed, with Amazon and Apple both hitting the trillion dollar market cap mark, and Alphabet and Microsoft sitting at over USD900 billion, it is clear that the stock market values data as the most valuable resource, not oil or consumer products. Against this growing tsunami, investment managers and service providers alike are looking for ways to ingest and make sense of it all. To find information that they can translate into insights and turn into knowledge, that if done correctly, could lead to improved business performance and enriched customer relationships.
Artificial intelligence (AI) is rapidly gaining momentum as a vital business resource as organizations discover new use cases in their efforts to improve processes, increase efficiency and automate costly, manual tasks. Industries such as financial services are ideal for AI-driven applications and a related technology, machine learning (ML), because they can bolster customer service and leverage data to increase competitiveness. AI includes software that's designed to work in ways similar to the human brain, while machine learning encompasses programs that alter themselves based on data that's fed into the programs in order to train them. Recent industry research gives a sense of how AI usage is on the rise. Global spend on AI is forecasted to double during the next four years, growing to $110 billion in 2024, according to research firm IDC's Worldwide Artificial Intelligence Spending Guide.
A few weeks ago, I attended the Fintech Forum (Montreal) in the scope of my mission as Machine Learning lead at Swish. Between two talks and fascinating discussions, I held a workshop to discuss the applications of AI in the fintech industry. If you attended the workshop and wanted more, below is a lengthier version with examples and technical explanations. If you did not have the chance to participate in the workshop, this analysis will provide you with everything you should know about AI in the finance industry. The financial industry follows technological advancement with keen interest.