Banking has the potential to improve its customer service, loan applications, and billing with the help of AI and natural language processing.
When I was an executive in banking, we struggled with how to transform tellers at our branches into customer service specialists instead of the “order takers” that they were. This struggle with customer service is ongoing for financial institutions. But it’s an area in which artificial intelligence (AI), and its ability to work with unstructured data like voice and images, can help.
“There are two things that artificial intelligence does really well,” said Ameek Singh, vice president of IBM’s Watson applications and solutions. “It’s really good with analyzing images and it also performs uniquely well with natural language processing (NLP).”
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AI’s ability to process natural language helps behind the scenes as banks interact with their customers. In call center banking transactions, the ability to analyze language can detect emotional nuances from the speaker, and understand linguistic differences such as the difference between American and British English. AI works with other languages as well, understanding the emotional nuances and slang terms that different groups use.
Collectively, real-time feedback from AI aids bank customer service reps in call centers—because if they know the sentiments of their customers, it’s easier for them to relate to customers and to understand customer concerns that might not have been expressed directly.
“We’ve developed AI models for natural language processing in a multitude of languages, and the AI continues to learn and refine these linguistics models with the help of machine learning (ML),” Singh said.
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The result is higher quality NLP that enables better relationships between customers and the call center front line employees who are trying to help them.
But the use of AI in banking doesn’t stop there. Singh explained how AI engines like Watson were also helping on the loans and billing side.
“The (mortgage) loan underwriter looks at items like pay stubs and credit card statements. He or she might even make a billing inquiry,” Singh said.
Without AI, these document reviews are time consuming and manual. AI changes that because the AI can “read” the document. It understands what the salient information is and also where irrelevant items, like a company logo, are likely to be located. The AI extracts the relevant information, places the information into a loan evaluation model, and can make a loan recommendation that the underwriter reviews, with the underwriter making a final decision.
Of course, banks have had software for years that has performed loan evaluations. However, they haven’t had an easy way to process foundational documents such as bills and pay stubs, that go into the loan decisioning process and that AI can now provide.
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The best news of all for financial institutions is that AI modeling and execution don’t exclude them.
“The AI is designed to be informed by bank subject matter experts so it can ‘learn’ the business rules that the bank wants to apply,” Singh said. “The benefit is that real subject matter experts get involved—not just the data scientists.”
Singh advises banks looking at expanding their use of AI to carefully select their business use cases, without trying to do too much at once.
“Start small instead of using a ‘big bang’ approach,” he said. “In this way, you can continue to refine your AI model and gain success with it that immediately benefits the business.”
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