This week, AI Business’guest writer is Ian Foley, founder and CEO of acuteIQ, which uses predictive analytics to help finance companies find new customers. Foley gives an interesting insight to how artificial intelligence can be beneficial to the finance world, while also addressing its challenges.

Ian Foley founder and CEO of acuteIQ
Ian Foley founder and CEO of acuteIQ

In 1950, Alan Turing posed the question: Can machines think? Since then, the development of “Artificial Intelligence” – machines that think – has garnered worldwide interest and investment. AI uses computer programming to search and analyse databases, then issues reports specific to the data. Search engines are excellent examples of AI in action

Finance world benefits
The finance industry has embraced the possibilities presented by AI to offer its customers personalised financial services, tailored to their individual habits, activities and characteristics. Automated software routinely scans for market information, trends and events. It then analyses that data against the customer’s preferences, and makes recommendations based on real-world financial sector activities. On the business side, AI programs scan corporate data for anomalies that might indicate fraud, to generate portfolio proposals, and even to suggest add-on products or services for existing clients.
To capture all marketing opportunities, AI significantly speeds up processing times for virtually every transaction or proposal. Digital analysis offers actual financial opportunities in almost real-time and with greater depth and comprehension than ever before. In 2015, Charles Schwab introduced its “Intelligent Portfolios” product, which uses only computing algorithms to make investment decisions. Eliminating the need for human actions within the investment process significantly reduces the costs of trading, while almost eliminating the occurrence of less-lucrative, emotion-based decisions. The programming focuses on investors’ risk appetite, diversification and other market factors, then triggers trades that conform to that database.
Customer service improves
For consumer banking customers, AI is implemented as an element of customer service, another cost-saver to the institution. Instead of connecting with a live person through chat, email or text messages, customers with concerns now access the AI assistant that scans for responses to their query and makes recommendations for possible solutions.
Challenges to be addressed
While the possibilities of AI in the financial sector appear limitless, challenges still lie ahead. As with every computer and programming system, AI programming can crash, causing potentially expensive downtime, lost data or worse. And the programming is only as good as the programmer. As modern technology continues to evolve, developers will need to update the institution’s legacy computer systems, or at least tell the computers how to do that for themselves. The full impact of AI on aspects that are integral to the financial services industry, such as customer privacy, consumer and enterprise security, and regulatory.
Fundraising soars
The benefits of AI in fintech far outweigh the challenges to institutions. According to CBInsights, 2014 and 2015 saw over $700 million dollars in funding to AI companies, almost 175% more than 2010-2013 combined. The pace of investment has continued into 2016 through additional investments in financial technology and new venture funds to support financial technology. Hedge Fund manager Steven Cohen is beginning a venture capital unit that will provide early stage capital and advice in data mining, AI, and machine learning. In April, a hedge fund named Numerai, which is focused on AI, raised $1.5 million led by a founder of Renaissance Technologies. Numerai utilizes a tournament style approach for data scientists to make stock market predictions. It is clear big money investment has soared in fintech companies that utilitse AI in their analysis algorithms, and those resources are overhauling all aspects of the financial world, including the processes of credit scoring, risk management and customer acquisition.

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