Facebook, Google, Microsoft and Baidu spent at least $8.5 billion beefing up their AI talent. Amazon spends $228 million a year just to find people to run Alexa and related machine learning initiatives. Even small to medium businesses in every sector – from fast food chains and personal fitness centers – are reaping the business benefits of technologies like natural language processing, image recognition, and machine learning.

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SwissCognitive LogoYet only a few outliers in the banking sector, such as Capital One, have been able to ship AI products as quickly as their counterparts in Silicon Valley. While many financial institutions have publicly announced ambitious plans to integrate artificial intelligence and machine learning, customers are still waiting months later for these proposed products and services.

So why are banks – who are typically the most capable and tech-intensive players in the business world – acting like Luddites with AI? And how can AI entrepreneurs and developers building products for the industry nail their pitches and drive home deals?

Financial Institutions Are Complex Beasts

To be fair, banks have employed AI. – at least in rudimentary forms – for decades. Computer automation has been used by the financial industry for back office and customer-facing operations since the 1960s. AI investments picked up in the 1980s in the form of expert systems.

Despite this strong IT heritage, many banks lack the agility to fundamentally transform their business with modern artificial intelligence. A joint study by National Business Research Institute and Narrative Science revealed that traditional financial institutions are still in the early stages of AI adoption, with a mere 32 percent of surveyed respondents confirming use of recommendation engines, predictive analytics, voice recognition and allied technologies.

Security and other Issues

For AI enablers in finance such as Kasisto, Voyager Labs, IBM Watson Cybersecurity, and Amazon’s Alexa platform, the underserved market presents vast opportunities for engagement, but only if they can creatively overcome the industry’s daunting challenges. 12% of financial institutions surveyed that weren’t already using AI blamed the technology for being new, untested, and risky. Other firms cited “siloed data sets, regulatory compliance, fear of failure, and unclear internal ownership of emerging technologies” as main factors thwarting innovation. A similar study by PWC showed that two in every three financial services firms in the US were hindered in AI adoption by “operations, regulations, and limitations in budget or resources.”

Disaster recovery is also critical and must be performed on premise. While most smaller AI companies rely on the cloud, banks require “hot-hot” recovery where downtimes only last a few seconds at most. The data-hungry nature of AI solutions, which often need to capture and mine vast volumes of consumer information and input, also creates challenges with compliance.

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