30th May 2024

Better business. Better community

Business Industry and Financial

AI moves to the front office to help marketers and customer service reps in financial services

The trend: Traffic to OpenAI’s ChatGPT may have fallen off recently for the first time since its November release. But within the financial services sector, generative artificial intelligence (AI) and large language models (LLMs) are still having a moment.

Banks, credit unions, and fintechs have continued to explore how the tech can help analyze text and speech from consumer interactions to improve the customer experience.

Beyond chatbots: Chatbots and virtual assistants are the most flashy, public-facing examples of generative AI helping the financial services industry attain a new level of efficiency. But FIs are also using AI and machine learning in less-visible functions like marketing, designing the customer experience, and providing customer service.

Marketing: Internal teams and contracted agencies are leveraging AI to analyze customer behavioral patterns and automatically perform customer segmentation.

  • Tools like look-alike models help marketers find common attributes among their customer bases and other users of similar products and services to determine what characteristics make a consumer more likely to convert on an offer.
  • Generative AI also assists with surfacing news themes while tracking event-driven news, enabling marketers—and traders and quants—to gather and analyze signals from news and social media to identify positive and negative customer and investor sentiments, confidence, and story counts.
  • For instance, LLMs can identify new trends in consumer behavior from social media content by clustering posts with a similar meaning and assigning the clusters an aggregate measure of sentiment. That helps them quickly identify and summarize negative sentiment associated with specific content, such as a new advertising campaign, helping marketers respond promptly to this feedback.
  • Content teams are exploring uses for ChatGPT, such as blogs and forms that need to be populated with explanations and descriptions. As smaller banks and credit unions grow, they might adopt ChatGPT to augment internal marketing staff, rather than tapping freelancers.

Customer experience: Consumers expect the institutions they do business with to know more about them and to make their experiences faster, easier and more customized.

  • FIs are offering facial recognition and voice command features as options for logging into financial apps.
  • To streamline the customer onboarding and know-your-customer (KYC) processes, they leverage machine learning in the form of computer vision, optical character recognition, and natural language processing.
  • Generative AI can help firms adapt the conversational style of their chatbots to match that of the customer (for example, casual conversation mode or formal conversation mode).
  • LLMs shine at enabling long-form answers to open-ended questions. For example, they can search thousands of pages of legal or technical documentation and summarize key points that answer the question.

Customer service: AI is also being leveraged to automate tasks, make predictions, and transfer users quickly to the best source of help. One example is call center assist functionality, which provides human agents with automated assistance, contextual recommendations, and next-best actions.

  • Without LLMs, firms typically would anticipate questions in advance and human authors would create a fixed set of answers.
  • With LLMs, answers can be generated on the fly and new information can be incorporated as it becomes available, into the answers provided.

For example, when an account manager is speaking with a customer, AI allows for instant automation of various background tasks while the conversation is in progress. That could include drawing up a contract, identifying the warranty, or calculating the right commercial discount—while also measuring customer satisfaction by reading their face and listening to their voice.

In a real-life example, Ally is piloting ChatGPT in its call center for post-call documentation. Ordinarily, when a customer service representative finishes assisting a call, they write up notes about it afterward. In the pilot, ChatGPT is inputting the notes. The objective is to see if this will free up time for the representatives to handle more calls.

Augmenting, not replacing, the human: Generative AI tools can help marketers, product innovators, and consultative sales professionals become more efficient and effective in their roles.

  • Instead of spending time searching for, aggregating, and summarizing key sections of text and images, the human professional checks the accuracy and completeness of answers the generative AI models provide.
  • Humans retain edit rights and final say, and can instead focus on other more value-added activities.

The human in the loop remains critical because information automatically produced by AI can have consequences for a quotation, a communication, or the processing of a customer file. If that information is incorrect or imprecise, it can hurt the customer and the brand’s reputation—and in the worst cases, lead to regulatory blowback and financial penalties.

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