FINANCE

AI’s role in the finance industry’s customer experience

AI’s role in the finance industry’s customer experience

By Clint Oram, cofounder and CMO, SugarCRM 

AI is the buzzword of the moment and the resultant technology trends, such as chatbots and Natural Language Processing (NLP), are transforming the way that businesses engage with their customers. AI enables companies to interact with clients across a multitude of channels, while collecting and aggregating data to drive increased insight into their lives. But what is the value of this technology within the financial industry and how will it impact the customer, and employee, experience?

Are customers ready?

Our recent survey with Flamingo found that three-quarters of business professionals are comfortable using chatbots and believe they improve the online experience. There is tremendous potential for combining voice technology and CRM, with voice-activated assistants such as Siri and Alexa paving the way. People are interacting with these devices in ways which seemed impossible even a few years ago, and every connection becomes a data point. The potential is endless in the financial services industry and It’s going to have a huge impact on customer engagement and the foundation that CRM sits on.

Adding value through AI

Machine learning is only useful if it’s benefitting the customer experience and improving the way financial professionals work. For example, if a customer wants to carry out a simple task like setting up a direct debit, then a human is typically not essential to the delivery of this information. This is where AI tech should come into play – automating jobs where humans aren’t needed.

With 98 per cent of all customer interactions being simple queries of some kind, bots can be immensely valuable for scaling and streamlining engagement. You don’t want to be delighted by the answer; you just want the answer. That’s the value of AI: the ability to learn without the human on the ordinary stuff.

Financial businesses are routinely rolling out this technology – for example, RBS’s chatbot ‘Luvo’ which has the ability to solve basic customer issues; and therefore has the potential to reduce the need for as many customer services employees. Even more complicated sectors are experimenting with AI with UK start up Habito providing customers with the world’s first ever mortgage advice chatbot- disrupting what is conventionally thought of as a lengthy procedure.

But chatbots have limitations as they do not have the capabilities to understand complex issues or emotional signals such as tone of voice. For example, a chatbot wouldn’t be best served delivering news regarding rejected loans as the lack of empathy would likely cause offence. This is where human employees are still needed, to maintain customer relationships and avoid frustrating customers – or causing them to take their business elsewhere.

It’s fair to say, to date, the most noise around AI has been the role it can play in customer-facing businesses. But there is exciting potential in the financial industry too – particularly when it comes to analysing customer data, and optimising sales and marketing strategies based on the data. In fact, UBS predicts that AI could boost banks’ revenues by 3.4 per cent and cut costs by 3.9 per cent over the next three years. 

Quality data management is crucial for driving meaning from AI 

Artificial Intelligence, machine learning and predictive technologies all hinge on the quality of the data set they are interpreting and learning from. The whole purpose of this technology is to study patterns of behaviour from data, and construct algorithms that can learn from and make predictions, boosting efficiency and cutting down on manual processes.

The ultimate aim is to reduce the investment and resource needed to programme machines – it’s called machine learning for a reason. Customer Relationship Management (CRM) systems can be at the heart of this, driving insight and valuable learnings from rich, robust data.

As CRM systems become more adept at consuming large amounts of data, and leverage machine learning algorithms to generate insights more quickly, they will allow every user to better know every customer, and to anticipate and predict customers’ needs more effectively.

Collecting a variety of unstructured data, including social media posts, emails, and call centre recordings, and combining this behavioural data with transactional data, CRM systems will be able to deliver deeper insights on customer preferences, which deepens the customer relationship. Social data in particular can help organisations learn from and engage with customers at a more holistic level.

We are already seeing this become a reality with the launch last year ofSugar Hint. It helps marketers within the industry gather a wealth of relationship intelligence about businesses and individuals from just a name and email address. It eliminates the need for lots of manual research and data entry and gathers customer intelligence from a broad range of social data sources so users can quickly and efficiently learn more about their prospects to establish a productive relationship.

While chatbots were the first noticeable manifestation of AI in action, innovations such as relationship intelligence are the next step. They will revolutionise the way we interact with our customers – telling us things we don’t already know about them or that would take hours to discover manually. Down the line, through AI, businesses will be able to obtain intelligent recommendations for best actions, priorities and likely outcomes and to use this insight to engage in ways that truly resonate. By combining AI and CRM, all of our interactions will become more meaningful and effective.

Read more in the Essential Guide to CRM.

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