The financial services industry has led from the front in embracing artificial intelligence and machine learning technologies for improving efficiencies, increasing productivity, and launching new formats of engaging with their customers.
From introducing self-service chatbots and personalized customer experiences to “smart” risk assessments and the automation of processes like underwriting, the benefits are well recognized and desired. According to The Economist, around 85% of financial service providers have a “clear strategy” for adopting AI in the development of new products and services.
The same survey by The Economist also points out that around two-thirds of businesses know the benefits of AI and ML technology but think that the risk and complexity of adopting them outweigh the benefits to the customer experience. So, what’s holding them back? Could it be that rolling out these features is a daunting and complex task, with concerns about how they might integrate with legacy systems?
It’s time to dispel that myth. Many consumers are now seeking seamless and convenient digital means of interacting and engaging with financial service providers. Digital-native challenger companies have disrupted the industry by placing the customer experience at the core, and incumbents need to act now if they are to remain relevant and cater to modern customers’ needs.
Customers want feature-rich experiences, self-service options, frictionless transactions and applications, and above all, they want speed. Here are just some of the ways AI and ML are transforming the industry, and why industry leaders must no longer hold themselves back.
A world of self-service: Customers like to be in control and prefer 24/7 access to their services. Chatbots that leverage natural language processing (NLP) technology are capable of understanding and responding to customer queries, even allowing customers to complete transactions and access information without the need for human intervention. More than half of millennials, for instance, choose live chat as their preferred support channel, and most customers say they would rather attempt to take care of matters themselves before reaching out to a human representative.
Cross-channel personalization: Financial services companies have a lot to offer, but not all products will be relevant to all customers. Using AI and ML, banks, insurers and other service providers can use behavioral and historical customer data to promote relevant services across channels, increasing uptake and driving engagement. According to one source, 70% of customers rate tailored offers as “highly important” for banks and other financial service providers. But only 14% of banks currently provide contextually relevant experiences. That’s an opportunity to be seized.
Enhanced risk management: AI and ML can also help organizations manage risk more effectively by analyzing large amounts of data and identifying anomalous patterns. For instance, JP Morgan Chase uses machine learning to analyze customer data and identify potential risks in real time, speeding up the application review process and reducing risk for themselves and their customers. Assessing creditworthiness can be a time-consuming process that’s prone to bias and human error. Deploying algorithms to carry out these assessments using real-time intelligence can reduce the labor burden and speed up the application process – it’s a win-win for banks and customers.
There are other AI and ML applications too valuable to ignore, such as AI-based fraud detection, predicting market trends and the automation of back-office processes. While this level of data science might seem like a daunting task, particularly for financial services players with legacy systems in place, there are routes to AI and ML optimization that involve limited risk and high potential gain.
AI and ML are already a part of the financial services landscape, and the benefits of processing automation and enhancing the customer experience now far outweigh the levels of investment and risk involved in adopting the technology. Much of the technology has been democratized to the point where open integration and API-first development make for seamless integration with legacy systems and processes. Bank on AI, and the dividends will follow.
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