The financial services industry has long been criticized as insular, elitist and discriminatory. Will artificial intelligence finally open up and “democratize” the industry? This can and will happen in a number of ways—by empowering customers, by opening up services to underserved communities, and by increasing the breadth of capabilities companies can deliver.
While still a minority, a growing number of financial services executives are implementing AI as part of their customer experience and operations. About half (48%) of the 500 executives surveyed by The Economist Impact and SAS in March 2022 considered advanced data analytics to be one of the most Tube specifically mentioned artificial intelligence and machine learning as their path to the future.
Similarly, a study by the Deloitte AI Institute confirmed that 32% of financial services executives said their organizations use AI. “There is no denying that AI is the future of financial services,” said the study’s authors, adding that while “many fintech companies have already adopted AI, the financial services industry is largely in the early stages of AI adoption.” .”
Artificial intelligence and machine learning introduce enormous complexity, and many financial services firms are still evaluating where and how to invest in these approaches. “AI and machine learning have a lot of moving parts,” said Michael Upton, chief digital officer at First Tech Federal Credit Union, which caters specifically to employees at Microsoft, Amazon, Intel, HP and other tech companies. However, once in place, these technologies will play a key role in emerging digital businesses.”Covid has really accelerated digitization, and from a tactical and transactional perspective, the industry has done a good job of meeting customer demand. But I think across the industry as a whole, we have a lack of engagement, a lack of enthusiasm, a lack of relevance, especially through digital channels. We need to bring the human back into the digital, and AI is a tool that can help us do that. Combined with face-to-face encounters, AI can help provide more personalized and relevant services to meet the needs of customers at specific moments. “
First Tech Federal sees providing customers with highly personalized interactions and services as a primary goal of its own expanded AI efforts. “Using AI and ML, we believe we’ll put ourselves in the best position to help each member meet their needs at any point in time,” Upton said. “When members need us to be relevant, we want to be relevant, no matter which touchpoint they choose. We want to use that for personalized and relevant engagement, whether it’s sales engagement, service engagement or retention engagement.”
While AI promises to improve the level of service financial institutions can provide, there are still challenges to overcome, including incorrect expectations, skills issues, and implementation issues. “Talent scarcity is a key limiting factor,” says Bjorn Austraat, senior vice president and head of AI acceleration at Truist. “This includes both technical and business stakeholders,” he explained. This includes people who are “well versed in data science and executive speaking. . An over-reliance on purely technical skills can lead to disjointed scientific experiments without a clear business return and an excessive focus on business outcomes—especially early in the sometimes long data science and model operations lifecycle—can suppress disruptive innovation. “
Barriers to success with AI are common across all industries, according to Charlene Coleman, senior managing partner and head of the Modern Finance practice at Launch Consulting Group. But financial services presented it with its own set of problems. “Deploying AI to democratize the financial system requires bold, human-centered leadership willing to invest in technology and talent. Going forward, institutions lacking an AI strategy will not move beyond the experimental stage. Most do not have centralized data backbone. Finally, they must embrace a new operating model that breaks down functional silos to achieve speed and agility.”
AI “can help redefine and restore personalized experiences to build trust for consumers and small business owners,” Coleman said. “Assuming informed consent, one example is AI-driven personalized conversational interfaces and biometric profiles, which show promise in helping vulnerable consumers avoid the debt traps of late fees and inflexible payment schedules.”
This doesn’t just mean building models to support algorithms, no matter how well designed. “However, the model is only five percent of the solution. Integration, instrumentation, validation, ongoing monitoring and eventual dollarization is the other 95 percent.” The key is to “think of the model as a racing engine,” he added. “To win a race you need a lot of other things: petrol, shock absorbers, tyres, pit crew and drivers.”
The key to the success of AI in financial services is selling or promoting AI adoption to businesses. “I accelerate this alignment with a simple phrase: ‘Whose life would be better off, how much and how do we know?'” Austraat said. “If you can really answer that question, you’ve covered all the bases from framework, deployment, value proposition and value perception and realization, to political air cover. Interpretability trumps model performance in financial services. In credit underwriting, etc. A particularly sensitive area where banks and other institutions must balance the desire to innovate and use cutting-edge AI with reasonable regulatory expectations around explainability, robustness, and fairness. The hottest solution does not always win, Especially when it’s too black-box.”
This requires a more holistic view of AI outside of the lab or data science team. “You can’t just let data scientists do their thing,” Austraat said. “A holistic team approach centered on cross-functional teams is critical to engaging legal, risk, data engineering, implementation engineering, operations, support and business leaders early and often to create sustainable success.”
In the end, technologies like AI and ML are “just tools,” Upton said. ‘You need to have a very clear business strategy, a very good go-to-market strategy, and a very good operational plan to leverage these tools to create experiences and drive business value. People tend to be enamored of a tool or technology, but they don’t know the use case for which their investment is worth. You can buy all the coolest tools in the world, but if you don’t think about change management, adoption, helping organizations understand why and how to use these tools to drive important things, you’re going to own a lot of expensive tools yourself. “