Increasing market competition and economic uncertainties are prompting banks to re-examine customer needs and business processes in order to improve service aptitude and operating efficiency. Banks’ massive data accumulation in financial transactions, customer portraits, market analysis and risk control provide a great environment for AI to flourish.

In 2018, Chinese financial institutions invested about CN¥160.4 billion (US$23 billion) in technology, an increase of 10 percent over 2017, of which AI hardware and software-related investment accounted for 10.4 percent. The banking industry was the biggest investor in AI-related applications, accounting for 70 percent of all market purchases.

A wide range of software programs, including those tasked with precision marketing and intelligent risk control platforms, accounted for two thirds of Chinese banks’ AI purchases. AI-powered cameras and document identification machines and other hardware accounted for the balance. According to statistics from China’s Banking and Insurance Regulatory Commission, the total investment made by Bank of China in technology increased by 13 percent from 2017 to 2018; while relevant staff hires also increased by 10 percent.

Driving the investments is the belief that AI technologies will help banks become more flexible and specialized. The step beyond traditional banks and online banking, smart banks use data-driven methods and state-of-the-art technologies to redefine existing services, products, operations and business models. These banks of the future favour economies of scale and offer improved efficiency and reduced costs.

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Although banks are busy finding talent for their new AI facilities, integrating resources to build data platforms, and structuring and securing data storage, there are limits to what they can do with AI technologies. At present most AI systems are not sufficiently robust for all situations — for example intelligent customer service bots may fail at voice recognition in noisy environments, as may facial recognition in extremely bright or dark environments.

It is estimated that applying AI systems in the banking industry takes about one year from procurement to deployment. From the start, there tend to be system integration issues particular to different banking systems, and compatibility issues due to multiparty software and hardware suppliers.

Regulatory uncertainties may also also hinder AI innovation in banking and financial services industries, mainly regarding data use standards, privacy protection of user information, financial license procurements and so forth.

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