Brad Neuman: Gathering Intelligence on A.I.
When we talk about how the future will be driven by artificial intelligence (A.I.) and how it will change the world, clients often seem enthusiastic but skeptical at the same time. There seems to be a question lingering just beneath the surface, afraid to actually be uttered aloud: how will we use A.I.?

Recently, we had a chance to attend the Bank of America annual conference on A.I., an event we have attended for the past three years. These conferences are helpful in that they feature real companies discussing real products and services that either enable or utilize artificial intelligence—in contrast to a group of consultants discussing fictional market sizes that will be spurred by products that have not yet been invented. At the event, we heard examples of real world use-cases of how A.I. is being implemented today.

Bank of America discussed how it uses A.I. for what it calls “intelligent receivables.” Often times, when a bank receives a client’s payment, it may arrive separately from information such as the identity of the payer and what the payment is for. This creates a matching problem for the bank, which can tie up cash and create customer dissatisfaction. It is also expensive and labor intensive—one bank had 40 people just looking at two screens, trying to match payments with invoices all day long. Bank of America offers an A.I. solution for banks so that they can automate this matching process. The program scans payments and emails for data, identifying payers and matching payments to invoices. Over time, the A.I. program learns patterns and improves accuracy to 90%, so most of the job is completely automated.

Using A.I. to automate processes as in the accounts receivable example is probably the most productivity enhancing application in the near term, but plenty of other uses for A.I. exist. According to McKinsey, the majority of the activities for more than one third of all occupations can be automated with return on investment typically in the triple-digits.i

Indeed, many other examples of real-world A.I. applications were given by conference participants. One company cited a large health insurer using A.I. to help automate patient intake, saving money and speeding up turnaround time that benefitted the patient. In the financial industry, examples included deploying A.I. bots to automate tasks such as insurance claims processing and complying with anti-money laundering procedures. In retail banking, IBM has deployed its Cora bot, which can handle 40% of customer inquiries—Bank of America cited a forecast that by 2025, 95% of bank customers will interact with artificial intelligence. In transportation, one company used A.I. to onboard drivers for its trucking business and was so successful it will now spin off that process to aid other trucking companies in that area. In energy, a large integrated oil company was cited as using A.I. to improve the efficiency of its drilling operation.

Most of these automation examples are facilitated by bots that can learn on the job but come generally pre-trained with certain skills, such as claims processing. However, no one knows the skills required for these tasks better than the company that will be employing them. That is why going forward, we will likely see more A.I. bots where the deploying enterprise will use machine learning and “bring its own model.” A machine learning model is in essence a learning algorithm that forms the underlying logic of how the bot makes inferences. By utilizing the employer’s more optimized model, the A.I. bot should perform and learn better at the specific job function, ultimately enabling higher accuracy and efficiency. Interestingly, the companies that are employing these A.I. programs to date are not firing workers. On the contrary, the human employees are freed up to do more value-added work, enhancing output and productivity. So what can A.I. do in the real world? Make your job less repetitive and maybe even more fun.

​​​​iMcKinsey, “Driving impact at scale from automation and AI,” February 2019.

The views expressed are the views of Fred Alger Management, LLC as of August 2019. These views are subject to change at any time and they do not guarantee the future performance of the markets, any security or any funds managed by Fred Alger Management, LLC. These views are not meant to provide investment advice and should not be considered a recommendation to purchase or sell securities.

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