Chatbots, Personal assistants, Robo-advisors, Machine learning, Cognitive computing and so much more. While the term artificial intelligence (AI) has been around for 60 years, it has finally become part of our daily lives—and how we bank, invest, and get insured. Some financial institutions have been investing in AI for years. Other firms are now beginning to catch up, thanks to advances in big data, open-source software, cloud computing, and faster processing speeds.
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AI means different things to different people. Each sector applies AI differently. For example, insurance leaders use AI in claims processing to streamline process flows and fight fraud. Banks use chatbots to improve customer experience. In asset and wealth management, AI adoption has been sporadic, but robo-advisors are rapidly changing that.
Going behind the scenes
Some firms use AI to model scenarios for capital planning, or use natural language processing and graph processing techniques to flag transactions for compliance reviews. These uses are lower profile, but they’ll have a big impact as they move toward mainstream.
Are more firms relying on machines?
Answer is “Yes”. Two thirds of US financial services respondents said they’re limited by operations, regulations, budgets or resources limitations, according to 2016 Global Data and Analytics Survey.
The road ahead
AI will gradually replace humans in some functions like personal assistants, digital labor, and machine learning. But challenges will persist because of bias, privacy, trust, lack of trained staff, and regulatory concerns. Augmented intelligence, in which machines assist humans, could be the near-term answer.
With advances in big data, open-source software, cloud computing, and processing speeds, more firms will use cognitive computing and machine learning to perform advanced analysis of patterns or trends. For example, firms may use AI to help spot non-standard behavior patterns while auditing financial transactions. Firms may also use AI to sift through and analyze thousands of pages of tax changes.
What to consider, Where to start?
We recommend that you pick two different types of problems as you explore AI technology solutions:
- Some should be operational so that you show productivity improvements. Review and select the various AI technologies that can solve these problems.
- Others should be more exploratory in nature. For example, if you’re asking whether you can “get better customer satisfaction and retention by analyzing the audio data from call centers,” you might not have a specific metric in mind. However, applying AI to this problem may yield insight that other techniques can’t.
Make AI an extension of your data analytics team
Mature organizations might choose to set up a new chief AI officer role. But if your firm is in the early stages of adoption, view AI as an extension of current analytics capabilities instead.
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Find the right balance between human and machine.
There’s a balance between servicing costs and the need for good customer service. You should design off-ramps, swapping customers over to live support if an AI customer interaction or other transaction should falter.
“Artificial intelligence can help people make faster, better, and cheaper decisions. But you have to be willing to collaborate with the machine, and not just treat it as either a servant or an overlord.”