For financial services providers that have long relied on highly developed infrastructure, high switching costs and worldwide networks as their only competitive differentiators, this new connected marketplace creates real challenges. All of a sudden, consumers have come to expect flexibility, speed and personalization from the financial services industry as well and they are more than willing to vote with their wallets if their expectations aren't met. This is the era of customer experience.

The Modern Customer Journey is Complex

Traditionally, the path to conversion followed a fairly linear route. An individual would recognize a pain point which needed to be addressed. They would gather information about different businesses that could service this need, and then they would identify a particular product or service that met their requirements in terms of pricing and quality.

Today, the same buyer might spend up to 60-70% of their time researching different brands through both offline and online channels on multiple devices, before they ever reach a purchasing decision. At each of these touchpoints, consumers will expect you to have detailed information available for their perusal, with consistent branding and messaging across each platform. Financial service providers can no longer hope to engage and retain customers through online banking apps and other automated transaction tools. While these platforms might offer speed and convenience, they don't offer personalization that digital users are looking for. In fact, data from Deloitte shows that 72% of banking customers still visit their local branches to seek out advisors that can point them towards more tailored financial products and services. Imagine if you could create similar relationships through your digital customer service channels? Of course, this raises the question - how do you deliver a seamless, personalized customer experience across multiple digital touchpoints without investing millions in UX and customer support? You use artificial customer service.

AI & Customer Service

The intersection of technology and customer service is nothing new. For years, businesses have automated their relationship management with the help of CRM systems that offer a unified view of customer data across the enterprise. From purchasing preferences and feedback to personal details, these integrated databases provide in-depth insights about each customer which were used to develop more targeted marketing and sales strategies. However, at the front-end, interactions were generally still handled by live representatives that could service specific customer requests in person or through phone calls and emails.

Unfortunately, when it comes to answering feedback through company websites, and business apps, these live agents simply do not provide the kind of accessibility and instant responsiveness that customers require.

Initially, AI couldn't either. Chatbots and other conversational interfaces have been championed by tech insiders since the launch of Siri back in 2011. But until recently these tools only proved useful for extremely simple actions. For example, you might use a chatbot to determine the departure time for an airline booking, or to ask about the day's weather conditions. Any attempt to gather more detailed information was usually met with irrelevant replies or repeated refrains of, "I did not understand your question". With these sorts of limitations in place, it makes sense why 40% of chatbot interactions fail to move past the first line of conversation.

Still, the potential for chatbots cannot be ignored. Online messaging has been our primary method for years now, and any technology that can reach customers through these channels on a consistent basis offers a significant competitive advantage to any business. Thankfully, a spate of innovation has begun to push chatbots and conversational AI, in general, in a more user-friendly direction.

Smarter Artificial Intelligence in Financial Services

Deep Learning

Traditional conversational interfaces reference user requests against a set of pre-programmed responses. Each response is triggered by specific user inputs that then dictate the ensuing conversational flow. The chatbot will reference a user input against its database, and if a match is found then the corresponding action is initiated. While this works well when users require quick, basic information, as the conversation goes on, the chatbot is forced to switch between different logic trees which usually results in a failed response.

Deep learning offers a whole new model for artificial customer service. By being able to detect irregular data, identifying fraudulent activities and spotting differences in financial data, Deep Learning is way ahead in spotting errors than humans and Machine Learning techniques. This technology uses neural networks that are modelled on the processes of the human brain. Each component of the network can analyze data for specific characteristics and determine how inputs should be classified based on this assessment. When you apply these capabilities to the wealth of customer data flowing through financial service providers, the possibilities are endless.

Robo Advisors

AI can fill the advisory role that financial customers are clamoring for. Many financial service providers have begun to arm their portfolio managers with bionic advisors that can crunch a variety of real-time market data around the world and filter these insights through specific risk profiles to come up with tailored buy or sell recommendations for clients. These automated platforms are particularly well-suited to novice investors that might shy away from wealth managers due to the high barriers to entry. A bionic advisor gives these individuals the capability to set up and adjust their portfolios at any time without having to jump through any additional hoops. These tools are also guaranteed to stay clear of any ethically dubious products that could fall foul of federal regulations.<'p>

Of course, when it comes to retirement or tax planning, these automated tools cannot offer the kind of complex assistance that consumers require. However, this is a perfect juncture at which to switch suitably matured clients over to human advisors who can provide the in-depth support that is needed at this point. This is an example of seamless, multichannel customer engagement strategy.

Financial Management

A number of third party vendors have begun to integrate virtual assistants into their financial management applications. Tools such as Plum or Cleo can track customer spending habits across multiple bank accounts and analyze where individuals are spending too much of their income, and where they could be saving more. Some of these apps also have voice functionality, so users can actually interact directly with their financial information to find out how much they spent over a particular period, or they could implement certain limits to help move them towards their financial objectives. The Bank of America's new chatbot Erica provides similar functionalities, and also gives you the ability to make quick fund transfers through its voice interface.

Fraud Detection

Cybercrime has been a constant thorn in the side of financial service providers. McAfee estimates that these incidents cost the global economy at least $600 billion every year, and as online transactions become more commonplace, these trends look set to continue. AI platforms can be used to examine the underlying relationships between transactions and related customer data to derive standard behavioural patterns for each account. Using these patterns as a baseline, systems can instantly identify and flag any strange spending patterns (attempts to process transactions from a different country, attempts to withdraw money in excessive amounts, etc.).

This will help financial service industries catch fraudulent transactions in real-time. Deep learning can also help AI adjust its pattern recognition capabilities with time, so if a customer indicates that they will be changing their spending patterns then the fraud detection platform can adjust its criteria to reflect the new circumstances.

How Artificial Customer Service Can Benefit Your Business

As you can imagine, these capabilities offer endless possibilities for customer service. According to Gartner, at least 40% of all customer service interactions may be resolved through AI by the year 2019. Other experts predict that AI could be involved in 85% of customer interactions by 2020. But how will these implementations help to differentiate your brand in practice?<'p>

Reducing Call Volume

More than half of customer service calls involve routine requests that can be dealt within a few minutes. These tasks could range from password change requests to cancelling transactions and basic troubleshooting. Instead of employing a large roster of support specialists, highly effective conversational platforms can be positioned to monitor social media, apps and website interfaces to provide 24/7 assistance with these issues. This is particularly helpful for companies with a strong millennial audience. Younger customers prefer to tackle technical product and service issues on their own, and chatbots perfectly cater to these self-service tendencies.

Instead of maintaining complex knowledge base portals and FAQs on your website, you can simply filter this information through an intuitive artificial customer service agent that can provide detailed support to customers of all types, at any time.

Proactive Services

If you've ever been offered relevant product recommendations on Amazon or Netflix then you've already had some experience with predictive analytics. This technology is used to identify patterns in customer behaviour, which are then used to inform a number of proactive actions.

From a customer service perspective, predictive analytics could influence the way financial service chatbots deal with users, depending on their level of familiarity with the company. For example, if you're a first time visitor to a company's website, you might be welcomed with a basic greeting and invitation to learn more about the company's products and services. However, if you're a repeat visitor with items in the checkout basket, then the chatbot might offer a more personalized greeting which is designed to move you along towards a conversion. Similarly, your chatbot can be trained to identify distress signals that are linked to poor customer experience issues. If a customer has communicated negative feedback on a product, or they are navigating through your website FAQ section, the chatbot can offer proactive assistance based on the user's history.

Increases Live Agent Effectiveness

Chatbots should not be seen as a complete customer service solution. Ideally, AI-enabled agents should augment your existing team of front-end specialists. While AI works to automate and streamline many of the mundane tasks associated with sales and support, your agents should be able to dedicate the bulk of their time to more complex complaints and technical issues. Again, many of these cases require more empathy and attention than chatbots can provide. However, chatbots can be trained to identify these issues and escalate them to available live agents. In a world where 1 out of 6 customers in the financial services sector report unsatisfactory interactions with call center agents, these capabilities could be extremely helpful.

Cost-Effectiveness

Chatbots allow you to scale up your customer service offerings without increasing expenditure. In fact, you can actually streamline your support team to only a small cadre of highly trained specialists. Since the average call representative costs over $8,000 to train and hire, these savings represent a massive boost to your bottom line. As your product and service line expands you can also update your AI platforms with ease. This helps to ensure that customers are always provided with the most accurate information at all times. Summing It Up

Artificial customer service represents an exciting synergy of some of the most important business trends today. From the increasing focus on user experiences and the demand for more data-driven decision making to the influx of automation across the enterprise, this technology can deliver on all of these promises and more. Any financial services provider that is struggling to differentiate their business can leverage these tools to deliver more accessible and efficient front-end services for customers across every digital touchpoint.