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Compliance workflows across financial services are no strangers to AI. Noncoding enterprise leaders are often awash in reports on how robust the AI vendor market is for AML and KYC or KYCC (know your customer's customer) compliance. Celent estimated that spending in the financial services sector on AML/KYC compliance tech and operations would reach $37.1 billion last year, an increase of 13.7% from 2020. 

Insurance is a growing arena for AI adoption and in many cases, automation is leading the way to streamline customer experiences and the organizational pipelines behind them along the entire customer journey.

Around five years ago, the promise of chatbots and conversational interface (CI) vendors to significantly enhance operations and customer satisfaction led to a bubble in investment from financial institutions (FIs) and many observers — including us at Emerj — to slap vendors with the "hype" label.

Financial services is an industry rife with repetitive processes and workflows that do not ultimately require human judgment to complete. Nearly three out of five leading banks polled [pdf] in a survey from Arizant, American Banker, and robotic process automation (RPA) vendor UiPath reported that their organizations had already implemented "moderate or significant" automation technologies by March 2022 to accomplish these tasks at their organizations. 

The main difficulty in starting with AI is knowing exactly where to begin. As we will elaborate in this article, there are many exaggerated and misleading claims about the various types of AI technology. Moreover, some executives are too trigger-happy, wanting or needing to integrate AI into their enterprise sans a foundational education or understanding of the challenges. 

Security analysts spend disproportionate amounts of time investigating suspicious transactions. False positives – or transactions incorrectly flagged as fraudulent – cost both the analysts and the employer time and money.

The banking sector has come a long way from the days of standing in lines to withdraw cash or report an issue. Today, customers just tap on a phone screen for most of a customer's needs. According to a survey published last year by Oracle, 56% of customers believe their customer experience has improved the most in the last two years with new entrants like digital wallets and digital payments. 

What does developing an AI strategy look like? Most executives openly embrace the technology and the automation possibilities within their organization. They may just need a bit of help getting started.

The advent of big data and the promise of AI signal a revolution in efficiency for wealth managers. Wealth management is "all about the numbers," and AI technology can uncover patterns and crunch numbers better, faster and more accurately than any human. Yet to look at AI in wealth management as one big calculator and data repository is to miss the mark.

AI plays a more significant role in our lives every day, and cybersecurity is hardly an exception – for both the "cat" and "mouse" sides of the table. According to a study from the Ponemon Institute in partnership with Accenture, cybercrime alone cost financial services firms $18.5 million on average in 2018.