Artificial Intelligence (AI) has been taking the business world by storm. It’s been used in applications from determining good locations for solar panels and increasing crop yield to predicting which medical devices might fail and which type 1 diabetes patients may be at particular risk. And financial services is no exception. The importance of providing an outstanding customer experience and predicting customer behavior are some of the key reasons that AI is gaining popularity in financial services. In fact, banks are investing heavily in AI technology, with anticipated spending of $5.6 billion in 2019 according to IDC.
One visible way that AI is impacting financial services is through chatbots. These programs, based on an area of AI called Natural Language Processing (NLP), can mimic human communication in speech or text. They are frequently used by contact centers or for customer service support on websites to answer basic questions, such as – “When is my mortgage payment due?” or “What is the balance on my account?” – or to engage with customers for upsell opportunities. They provide great value to financial services organizations by allowing them to staff customer support lines 24/7 – which is critical in today’s global marketplace, yet near impossible to accomplish logistically and economically by humans alone.
Improving the customer experience
Chatbots significantly help financial services organizations boost the customer experience by providing answers to customer questions quickly rather than making them wait 10, 20 minutes or more to speak to a person while listening to annoying Muzak or being shunted around to different people. Chatbots also can be used in combination with humans to provide better customer service. For example, when a chatbot recognizes a phone number or uses voice biometrics to identify a caller, it can direct a valued customer to a more senior representative for quicker, VIP treatment.
Taking it a step further, AI programs can also increase the value customers receive when they speak with service representatives. Based on an individual customer’s history and current situation as well as aggregate customer information from the AI algorithm, agents can make real-time recommendations on financial service products that might be most appropriate for the customer.
This is similar to the way Amazon, Netflix and others make recommendations on products or films based on past behavior and the collective data they aggregate from a large dataset of users. Providing these customized, relevant recommendations results in a better customer experience and greater satisfaction.
Making better customer decisions with Predictive AI
In addition to enhancing the customer experience, following are four more ways AI is being used to add value in the financial services arena:
- Detecting fraud. Along with growth of online banking and investing comes new opportunities for fraud. Financial services firms are fighting back with AI programs that are designed to look for suspicious patterns of behavior that differ from the way specific customers typically transact. It also helps them determine which credit cards may likely be impacted if a breach has occurred, so firms can avoid the expense of re-issuing credit cards to all of their customers.
- Predicting customer churn. Based on patterns in specific customer behavior and historic data, AI programs enable organizations to predict which customers might be likely to leave, so they can take actions or provide specific promotions to entice them to stay.
- Developing risk models. There may be more accurate ways to determine credit worthiness than the traditional credit scores which are based on generic criteria. Now companies can look at specific customer information, evaluate different criteria and use AI algorithms to predict customers’ credit worthiness. For example, loan candidates who may not have a credit history accumulated because they are just entering the workforce or typically pay with cash, may be good credit risk because of other factors, such as debt-to-income ratio or the amount of years they have been employed.
- Ensuring regulatory compliance. With data on a large number of regulations, AI programs can flag potential issues and help ensure that organizations are in compliance with them. This is critical for avoiding potential problems and hefty fines that could come with non-compliance. For example, AI programs can analyze call center conversations to ensure that representatives comply with privacy regulations, and they can flag suspicious financial behavior, such as cyberattacks or money laundering to ensure that they are safeguarding customer accounts and not inadvertently breaking any laws.
According to a 2019 PWC survey, financial services firms expect AI to help them grow revenue and profits, improve the customer experience and develop innovative products. But there are challenges that need to be overcome. It’s difficult for financial services firms, often steeped in traditional processes, to implement new technologies which can involve retraining employees at multiple levels of the organization, establishing a new mindset and creating new ways of doing things. And, on the technical side, it can involve capturing, cleaning and aggregating data that is often poorly structured and may be walled off in silos.
It also requires the talents of data scientists to develop algorithms and continually fine-tune them so they are accurate and up-to-date, and data engineers to collect and prepare the critical data that is needed to run these programs. Financial services firms may lack employees with this expertise, and a current shortage of these skilled professionals makes it difficult to bring these services in house.
To address this, many companies are turning to outsourcers, and nearshorers in particular, to gain the strategic and technical expertise that is needed to develop custom AI programs that address their business needs.
The opportunities, business benefits and competitive edge that AI offers to financial services is enormous. And, in a fast-changing industry where insights can rapidly result in financial gains – or a lack thereof can lead to losses – innovators embracing this technology can achieve a critical advantage.