Summary Bullets:
• Financial organizations are using artificial intelligence (AI) in multiple ways, including to improve service, better understand customers, gauge risk and predict market movements, and speed claims processing.
• Recent results from GlobalData’s 2021 ICT Customer Insight survey reveal that between 25-27% of digital spending by companies in finance will go towards artificial intelligence and machine learning.
For years the finance industry, which encompasses organizations in financial services, insurance, and banking, has been a strong adopter of AI. Financial organizations are AI in multiple ways, including to improve service, better understand customers, gauge risk and predict market movements, and speed claims processing. For example, chatbots and natural language processing (NLP) assist with customer support, optical character recognition (OCR) helps with the ingestion of information from documents, computer vision analyzes images and videos to speed claim processing, and machine learning (ML) models assess risk, detect fraud, and help determine rating and pricing.
As shown in Figure 1, recent results from GlobalData’s 2021 ICT Customer Insight survey reveal that between 25-27% of digital spending by companies in finance will go towards AI and ML.
Figure 1
Interestingly, GlobalData’s survey indicated that the portion of budget allocated to disruptive technologies is slightly higher for small financial organizations than for the largest businesses, as show in Figure 2. However, the distribution of spending among technologies is roughly similar, with 25-27% of disruptive tech spending going to AI across companies of all sizes. Figure 2
The key takeaway from these survey results is that AI platforms need to offer tools and capabilities for a broad audience, including companies that can afford to hire in-house AI experts, as well as organizations relying on non-AI specialists to incorporate AI-driven features into applications. Amazon is taking exactly this approach with its AI platform. It offers ML frameworks and infrastructure for expert practitioners, Amazon SageMaker for data scientists, and AI Services (such as Amazon Lex and Polly for chat tools, Comprehend for NLP, Textract to ingest documents, and Recognition to process images) for non-specialists.
Offering a myriad of capabilities adds complexity for platform providers; furthermore, it requires investment at multiple levels. However, as survey results show, one size does not fit all, even within the same vertical industry. And sometimes it’s the smaller organizations that demonstrate a greater propensity to embrace new technologies. Providing a comprehensive platform that is broad enough to allow organizations to seamlessly mature from AI services to developing their own ML models is key to streamlining the user experience and ensuring customer loyalty.
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