Three Ways AI Enables Personalized and Engaging Customer Experiences in Financial Services

Three Ways AI Enables Personalized and Engaging Customer Experiences in Financial Services

A lot of data would be useless without context. Many words in the human language have multiple meanings, making it hard to understand individual phrases without being aware of its context (where it was said, from whom, what prompted its delivery). Think about the term “bank” within the context of the short phrase: “I went to the bank.” From those five words alone, it is not clear what type of “bank” is being referenced in the sentence. The term “bank” can refer to a financial institution as well as a riverbank. The shorter the sequence of words the more relevant the context becomes. The same dynamic applies to digital content: the lower the degree of the structure for content the more its context becomes relevant and necessary to generate meaning.

Understanding context from data is crucial in order to make a correct interpretation and make useful decisions surrounding customer interaction. For many financial institutions’ business processes, digitizing content was only the starting point, and simply one step in the process of deriving meaning and measurable value from their data. However, after digitization, many banks maintain standalone and siloed systems, thereby inhibiting the quality of service modern consumers now expect. As a next step, the digitized data needs to be analyzed and enriched in order to be fully utilized. In a similar way that a human interprets data based on a given context, a machine also needs context. 

Only when unstructured content is paired with advanced technologies that derive meaning from content will that data provide the most business value. The following are three ways AI enables personalized and engaging customer experience.       

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1. Connecting Experiences 

Customers today expect their interactions with a financial services company to be personalized, efficient and engaging, and do not want to deal with long response times or have to repeat the same information from one request to the next. For example, when a customer calls into their bank, they expect the institution to be able to immediately retrieve all relevant data that details who they are, how long they’ve been a customer, what accounts they have active, and what they called about last time. Siloed digital systems cannot provide the level of service and personalization that customers have come to expect. It’s important that all systems and platforms are connected and that customer data is always available. 

By integrating advanced AI technologies into customer-centric operations, companies can provide their customers with 24/7 service, even when their doors are not open. To stand out amongst the competition and increase customer loyalty, give customers the opportunity to interact with your brand in every setting of their life: on their way to work, on the train, at home, on any device. Banks must provide customers with interaction possibilities that are both easy and convenient. AI technologies coupled with intelligent automation platforms play a key role in providing the tools needed to create dynamic, responsive and customized interactions with customers.

2. Contextualizing Data

Within the area of AI technologies, information extraction (IE) and natural language processing (NLP) are powerful approaches in providing relevant context to customer data and enabling personalized customer experiences. Take email customer service requests as one example. Email messages, by themselves, are unstructured content. Without leveraging IE and NLP technologies, reading and classifying large volumes of digital customer service requests are cumbersome and time-consuming. These AI solutions simplify the process by automatically classifying the content, extracting relevant data, and answering questions such as: Did the customer already file a support ticket? In what ways are they utilizing the product? Did they have the same concern previously? Are they a new customer? AI makes it possible to sift through volumes of customer service inquiries at scale and provide internal employees with the data they need to support customers more comprehensively, rapidly and responsively than if done without connected technology.    

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AI technologies also play a role in detecting the attitude towards a topic by providing context to customer data. NLP paired with sentiment analysis are powerful tools for deriving meaningful, actionable insight from unstructured content (an email customer service request, for example). These technologies can identify:

  • The relations between entities mentioned in the email
  • The object of the content
  • The classification of the content (i.e. is it a complaint, inquiry, technical issue?)
  • The names of products involved, if any
  • The attitude of the customer towards the product or company

By identifying and extracting these data points, you identify whether the customer service inquiry was about their account that needs routine maintenance, or if the message was from an unhappy customer complaining about an inaccurate deposit posting. Without using AI technologies, extracting this same information would be more time consuming, less cost-effective and contribute to bottlenecks and lengthy customer service wait times.

3. Predicting Customer Behavior  

AI and NLP enable a greater degree of granularity in evaluating customer interactions. They can support proactive customer engagement by predicting which customers are most likely to leave and which customers to offer special treatment to in order to build or reward loyalty. AI supports targeted and tailored communications and enables interactions that make customers feel more valued. 

Without adopting the right technologies, it would be challenging for financial institutions to fully meet customers’ demands. Standalone and siloed digital systems are no longer effective for providing the quality of service that modern consumers have come to expect. Leveraging AI technologies to connect systems, and provide context and meaning to content gives banks the opportunity to derive meaning from unstructured digital content and create a more personalized, engaging and high impact customer experience.   

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Dr. Marlene Wolfgruber1 Posts

Dr. Marlene Wolfgruber is Director of Product Marketing - Mobile and is responsible for driving ABBYY's mobile portfolio and go-to-market strategy at ABBYY.

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