What AI banking means for banks and customers

By Karin Kelley, on | Banking

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More and more banking customers are going digital, especially those who grew up immersed in technology. A recent report by Accenture found that 20 percent of banking customers have already gone completely digital, conducting their business online or from a mobile device. With so many options at their fingertips, banks are faced with creating better and more personalized experiences for customers. To do this, many are in the process of adding artificial intelligence (AI) into mobile apps, online banking and their internal operations, including DevOps platforms and processes.

AI banking and DevOps

The financial services industry has always been an early adopter of emerging and innovative IT, including DevOps tools that break down the walls between application development and operations teams. According to O’Reilly, the largest banks are already using automated release management and automated build and deployment tools to quickly and efficiently introduce new products and services such as AI banking to the market. DevOps tools provide more visibility into the rapid process of mobile app development and release, which makes it easier to meet strict service-level agreements and compliance and governance requirements.

Benefits of AI banking

AI banking provides many benefits to financial institutions. By using machine-learning algorithms, banks can provide sales and customer service associates with real-time, contextual insights into customer accounts and preferences, allowing for more cross- and up-sale opportunities and better interactions overall. For mobile banking, which is becoming increasingly popular, banks can offer their respective customers secure and personalized services across multiple devices from anywhere on the network.

By accessing a mobile app equipped with AI, customers get more control over their experiences, often opting to use a self-service portal that eliminates the need to search for often obscured ways to contact a customer service representative over the phone or through live chat. More importantly, banking customers expect the same kind of personalized communications and relevant offers that they are already accustomed to with services such as Amazon or Netflix. Because the bank is gathering data from mobile devices and using analytics and machine learning on the back end, it can provide customers with personalized and relevant information or automatically reroute them to the right channel.

Use case scenarios

Some wealth managers are already using AI, particularly natural language generation technologies to automate investments, write portfolio commentary to help investors understand how their funds are performing and to help fund managers prepare for meetings with investors. AI and machine learning’s advanced analytics capabilities may also help banks identify fraud and money-laundering incidents that may have otherwise been missed by the human eye.

Today, AI is making its way into all industries as mobility and eventually the IoT become near-ubiquitous. The highly competitive banking industry has a massive opportunity with AI banking, and in order to take advantage, banks must develop agile DevOps practices and technologies that will satisfy increasingly tech-savvy and demanding customers.

About The Author

Karin Kelley

Independent Analyst & Writer

Karin is an independent industry analyst and writer, with over 10 years experience in information technology. She focuses on cloud infrastructure, hosted applications and services, end user computing and related systems management software and services. She spent nearly eight years at 451 Research, where she spearheaded coverage on emerging desktops-as-a-service (DaaS) markets. She has extensive expertise in enterprise infrastructure software and services, as well as a deep understanding of SMB, MSP and hosting markets.

Articles by Karin Kelley
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