When running a business, it’s not enough to only care about its operations and costs; customer care has always played an essential part to their success and growth. Notably, companies with higher customer experience tend to enjoy higher revenues, while a PwC study found that most customers deem experience as an important factor in their purchasing decisions.

In today’s world, they are expecting more for less from such industry players, and sectors like finance have leveraged the game-changing capabilities of AI in order to adapt to these demands.

Human-based interactions that were once the bread and butter of customer support like over-the-phone and in-person assistance are steadily being replaced by more efficient AI systems. With more financial institutions looking to invest in AI in 2024, it makes sense to pay attention to these changes, especially for jobseekers looking to join this sector.

Tailoring a personalised experience for consumers

The bulk of data managed by an organisation doesn’t just end at monetary records; it also includes consumer information. Financial institutions are essentially a service provider, and what sets brands apart from one another is their ability to connect with their client’s needs and wants. This is usually done through a consistent analysis of statistics gathered from their consumer habits.

Similar to big data processing, AI can be used to help crunch the numbers and offer insights that lead to actionable steps in order to achieve goals such as consumer retention or acquisition.

By being able to collect, store and access substantial amounts of personal information, analysts have all the elements necessary to develop a hyper-comprehensive profile of different clients. This allows them to better understand what drives consumer behaviour in the wake of economic trends, and in turn, develop or recommend products and services accordingly.

These statistics can be drawn from many sources. A prime example of this is the types of loans a person takes out or the kind of investments they engage in. In the former case, if an individual requests for a housing loan, they may receive marketing on housing insurance that the bank offers, which compliments the loan.

By tailoring their customer experience according to the needs of the client, businesses are thus able to make their customers feel more valued. In fact, consumers are increasingly agreeable with companies collecting their personal data as long as it's used to improve their customer experience. Ultimately, financial institutions are able to build a bond that goes beyond the regular buyer-seller dynamic, which in turn, boosts customer satisfaction and retention.

Predict consumer behaviour

The use of data doesn’t just stop at customised products and services; it can also be used to forecast future consumer behaviour. This includes assessments of a person’s credit worthiness for future loans, sentiment evaluation on how they perceive the brand and potential transactions based on current habits.

The predictive analytics market across the world is stipulated to have a compound annual growth rate of 23.1% from 2024 to 2032, according to Fortune Business Insights. Its popularity is no surprise considering its wide scope of capabilities, allowing it to execute appraisals on key subjects like profit rates from customers and recommend steps to prevent losses.

This is done through customer lifetime value (CLV) reviews and churn predictions. The former forecasts the total net profit that a customer can generate for the bank when using their service via an evaluation of their transaction history and demographic data. The latter, on the other hand, is used to ascertain which customers are likely to leave for a competitor.

By using these two methods of analysis, financial institutions can utilise the findings to enhance their marketing strategies and distribute resources more efficiently. Those looking to increase profits can channel their efforts to selling new products that better align with their consumers’ needs by using a CLV analysis. For those looking to maintain profits by retaining customers, they can employ competitive pricing or other persuasive tactics to secure existing clients through the use of churn predictions.

As data accumulates and AI advances, this will enhance the precision levels of projection and the amount of data the system can handle at a time, which in turn offers significant yield during analysis.

Round the clock availability of services

Gone are the days when customer service meant a department full of people seated at desks with headsets mounted on their heads being on call. There are now a great many tools that can achieve the objectives and with a wealth of other advantages.

Alternative financial firms like digital banks are now running a full suite of interactive features such as chatbots, facial recognition and voice commands that prove to be more effective. By leveraging operating systems like Natural Language Processing (NLP) that are found in chatbots, service providers can now attend to the needs of their consumers with minimal disruptions.

According to Comm100, the average wait time in 2023 for live chat decreased by 23%, now averaging just 23 seconds. This is in stark contrast to the results seen in 2022, where wait time for live chat dropped just 17%, averaging 30 seconds. Similarly, the average response time in 2023 decreased by 19%, to 46 seconds, as opposed to the 4% increase in 2022, which means an average response time of 57 seconds.

This is achieved through the use of artificial assistants that customers can “speak” directly to. They are programmed to execute basic duties like opening new accounts, responding to queries and offering simple troubleshooting advice, enhancing the timeliness of the experience.

Traditional banks are also hopping on the bandwagon and recognising the benefits of these systems. Self-help kiosks and automated tellers are now popping up at physical branches, allowing consumers to engage in fiscal transactions without needing an appointment, or even when the bank is closed.

The terminal is equipped to guide individuals on how to proceed with their activities and has the option to contact a member of staff via a live stream connection on the screen. This greatly reduces both the customer’s and the organisation’s reliance on manpower, leading to lower costs in areas like labour and rental.

The impact of AI on customer service in the finance industry

As customer expectations continue to evolve, the adoption of AI among companies will likely continue as well. Given the rapid changes of this technology and the adoption of advanced models in the field, it is soon becoming a mainstay in the role of customer support.

Due to its capabilities ,the responsibilities associated with this role, such as client servicing and sales, are now being blended with AI systems that take over the majority of routine tasks. With this, employees can now focus on other aspects of the job that require the human touch that’s missing from these platforms, like spending time to build a relationship with clients.

Candidates who are interested in pursuing this as their career path should recognise this vital shift in the industry and stay on top of ongoing advancements to remain relevant. This will not only give them an easier time transitioning into the job market, but they’ll be able to build a skillset that compliments the emerging changes, adding value to themselves as a new employee.