Digital banking was gaining momentum as consumers’ primary banking method well before the pandemic, but COVID-19 accelerated the need for remote or contactless banking services. In the race toward digital transformation, financial institutions are investing in artificial intelligence and machine learning to capitalize on the data generated by the new digital channels.
However, research by Pega during the pandemic highlights that approximately two-thirds of consumers don’t believe banks care about their needs. Banks are still conversing with their customers about what they consider to be priorities, rather than what customers care about.
Centralized AI-powered decisioning can help address this issue and inject the necessary empathy into customer interactions. Allowing organizations to choose from thousands of potential ‘conversations,’ based on live indications of customer needs, breaks down those barriers between sales and service to focus on making more meaningful connections with customers.
Of course, AI-powered decisioning comes with considerations. The COVID-19 crisis caused customer support queries to soar, and AI has helped human workers respond to customers efficiently. However, AI bias can creep in and – if left undetected – can lead to harmful, discriminatory practices.
Algorithms are not silver bullets, nor are they inherently evil. Humans are responsible for building AI systems that generate fair and ethical outputs. To accomplish this, AI developers need to be aware of their own biases and any bias that could be prevalent in the datasets fed into machine learning algorithms.
In highly regulated industries like banking, centralized decisioning can be an invaluable transparency tool to demonstrate why offers are being presented to specific customers and prove that no unconscious bias is at work. This is especially true when it comes to credit risk and ethical lending. Some solutions can now enable businesses to adapt their models and apply ‘ethical’ AI decisions to identify and avoid specific types of biases.
AI also creates opportunities to step up and support customers in exceptional circumstances. One example: In 2020, Australia experienced one of its worst wildfire seasons, with nearly 6,000 buildings destroyed and at least 34 people killed. Commonwealth Bank of Australia (CBA) quickly integrated emergency assistance packages and recovery grants into their customer interactions to encourage empathetic conversations and action.
As AI evolves and banks learn how to apply it in real time, they will begin realizing dramatically increased returns on their investment. Then AI can indeed be part of a bank’s strategic goals to drive customer centricity and support business transformation.
AI has emerged as an engine of growth by providing valuable insights and intelligence, unlocking the potential to strengthen customer relationships with technology. However, it’s not a set-it-and-forget-it situation. We are still far from achieving sentient AI that is functionally equivalent to the human mind. Even the most innovative AI solutions will always require human input, especially for industries with high advisory needs like financial services.
The wealth management division of a large UK bank set out to bring a more personalized touch to banking by delivering tailored experiences in every customer interaction. While clients received some personalized in-person engagements, siloed systems and a lack of real-time customer data made it difficult to extend the same level of experience to digital channels.
By leveraging real-time data and decisions, the bank connected cross-channel insights to create a complete view of the customer, better identifying needs and responding in real time with AI-driven decisioning that determines the next best action for each customer on their channel of choice. Crucially, the bank got the right balance of when to have a personalized automated outreach and when to have a personal follow-up from a private banker. The results were impressive: they earned six times higher response rates, a 140% increase in client engagement and a 60% reduction in offers.
AI has and always will stand on the shoulders of human intelligence. It may be superior in predicting our preferences or financial habits, but customers will often want or need in-person interaction and advice to get to the right outcome. Ultimately, if AI technology is used for humans — and as long as there are new applications for AI to learn and new tasks for it to master — AI will continue to need human involvement to extract all the value there is to offer.
Steve Morgan is global banking industry lead at Pegasystems.
While financial services firms may lag other industries in AI deployment, they are better than average at data collection and verification. In this report, learn how the industry has potential to lead by further investing in AI infrastructure and computing...
Persistent inflation and higher interest rates will challenge banks’ ability to meet capital needs and cash flow. That means treasury departments need digital solutions that are timely, capture data from across the institution and anticipate changing economic trends.