Using a Customer-Centric Approach to Drive Revenue
Artificial intelligence investments in financial services is no longer just an option for organizations looking to compete in the financial services space. Finance organizations that have invested in AI are already seeing widespread benefits in revenue increase, reduced costs, and greater security compliance. This is especially important given the financial services industry faces mounting pressure to reduce costs and improve security measures.
Still, the pathway to scale an AI initiative from pilot to production hasn’t been smooth for everyone. One of the most common mistakes organizations of any industry run into is not selecting the right problem to solve from the start. Focusing on the right problem (i.e., one that solves a core business problem) from the start of a pilot enables scaling to production and becomes easier to navigate second and third use-cases as a fast follow.
According to a Deloitte survey, most financial service frontrunners explore AI for revenue enhancements and customer experience initiatives given the breadth of customer touchpoints and transaction data inherent in company processes. These explorations should be focused on media capabilities, insights, and optimization.
Media capabilities include the ability to see, hear, and talk through the means of areas like voice to text, text to speech, language translation, and more. The applied applications of media capabilities within software are still incredibly low. Despite the number of current applications, this area has a massive opportunity for growth. Companies should look at the data available to them to see how it can be mapped to something that provides business value. For example, if financial service institutions have difficulty managing customer wait times during high volumes of calls, consider how natural language processing can be utilized to serve customers better and remove call representatives from spending time routing calls – that way, they can be available to make call resolution, instead.
This also opens doors for leveraging audio and text conversations for digital chatbots or servicing customers across multiple geographic markets by providing services in their natural languages. The results are already promising: 64% of agents with chatbots are able to spend most of their time solving complex problems, versus 50% of agents without AI chatbots, according to Salesforce’s State of Service report.
Analytics allow companies to know their customer needs within a market and identify new product/service opportunities. As artificial intelligence investments in financial services continue to modernize organizations, consumer preferences are changing, too. According to Accenture, 81% of consumers want brands to understand them better and know when to and when not to approach them. Doubling down on the importance of understanding your client and embracing personalization, CMO.com found that over half of consumers are willing to pay more for a speedy and efficient customer experience.
Utilizing micro-segmentation allows financial institutions to directly engage with their customers rather than using personas, creating direct channels for conversations, building trust, and growing loyalty. Personalization is so impactful that the Boston Consulting Group estimated that a bank could garner as much as $300 million in revenue growth for every $100 billion it has in assets through customized client interactions.
The financial services industry can tap into personalization as well by evaluating available consumer data, starting with demographic details, transaction data, website analytics, merchant data, and more. These can be further supplemented with other datasets that may be on hand, such as insight on past experiences, reviews, purchases, clicks, web and app traffic, and data from offline channels. From here, machine learning models can be used to draw patterns that can make suggestions based on hyper-personalized learnings. Financial service organizations can use these models to develop (or identify existing) offerings, products, and services specific to the client, based on fine-tuned behavioral insights.
New competitors, increased regulatory oversight, compliance demands, and cybersecurity concerns have made the cost of doing business jump. Because of this, many financial services investments in AI turn to cost-saving initiatives. While optimization can help with labor-intensive and tedious tasks, optimization should also be used to identify and pursue revenue-generating functions through forward forecasting.
Financial institutions should be careful to avoid approaching AI initiatives as a way to automate entire functions, which may interject additional frustration on customers. Instead, drill down on how parts of the business can be enabled to be more productive with technology.
Customer-centric optimization is a very cost-effective tactic for AI initiatives. By utilizing virtual assistants, for example, banks can offer a range of services from expenditure tracking and analysis, personalized financial advice, predictive spending, and routing more complicated asks and tasks to agents. By automating some basic processes, customers will spend less time waiting, and agents can double down efforts on resolutions, making them happy. According to a study conducted by Juniper Research, chatbots can save at least four minutes of a customer service agent’s time – saving USD 0.70 per query in the process.
While implementing chatbots and improving search isn’t limited to artificial intelligence investments in financial services, it has been an incredibly impactful opportunity as financial services are often bogged down with enormous customer bases, limited human resources, and lack of time to troubleshoot day-to-day issues for each customer.
Optimization is not limited to chatbots, though. By looking at transaction information in correlation with behavioral insights, financial institutions can optimize their processes when they release new products using AI to forecast based on similar products. Optimization can also be useful for claims automation like fraud detection based on anomaly detection, improving operational efficiency, customer verification, and more.
High-Quality Training Data is Critical for Financial Services Investments in AI
While organizations need to think creatively about impactful business use cases, it’s also essential for them to consider what data they have available to train AI models. Partnering with a data provider is highly recommended, as a partner can manage data collection and annotation and provide expertise in successfully scaling an AI model to production. As AI becomes ever more critical to the financial services industry, it will be those who invest in high-quality training data that are likely to come out ahead.
Download our ebook, AI Solutions in Financial Services, to learn more.