People power: How AI can make financial services more customer centric than ever

The financial services industry is ripe for profitable disruption.

There has always been an emphasis on the customer — service is in the industry name, after all — but technology has so upended the dynamic and introduced challenger brands that financial organizations must center the customer as never before. Fortunately, the technology that is disrupting the industry is the same technology that can save it, especially when combined with brands’ historic strengths.

What’s essential now is continuing to make the bold investments to drive this transformation.

AI will further change customer expectations and the way businesses choose to interact with their customers. Consumers now have access to AI assistance at their fingertips. Businesses can now power new client interactions, automate manual processes, and gain new insights from troughs of data. As AI transforms customer expectations and interactions, pioneering and disruptive new businesses will emerge.

This major AI shift is not unlike the shift to mobile that came before it. In a matter of years, mobile banking went from non-existent to the primary method customers use to interact with their banks; only 29% of Americans now say they prefer to bank in person.

With the widespread rise of AI, it’s likely that we’ll see more change in the next 12 to 18 months than we’ve seen in the last 20 years. Financial organizations need to prepare for what comes next — and the many exciting and unpredictable forms the generative AI revolution will take.

Customers demand more information from their banks and have more financial knowledge than ever before. So why does it still take more than 30 days and so much paper to apply for a mortgage?

This disconnect helps explain why many consumers typically get information from social media, internet searches, families and friends — but not their personal financial institutions. In fact, nearly 80% of young adults have gotten financial advice from social media alone, and in comparison, only 11% use their financial advisors.

Still, 45% of consumers want their financial services brands to provide support from someone who really understands their needs when it comes to their savings and checking accounts, mortgages, investments, and other financial goals. On top of that, they expect more conversational support, especially early in their journey as they look to save money for a new car, set up college funds for their children, or apply for a small business loan.

Customer intents are now different. Customers expect more. Detecting sentiment has never been more critical to serving up the right product or service at the right time. By leveraging the fast-evolving capabilities of data and AI, financial institutions can build stronger relationships and trust by enabling them to improve their offerings and deliver on customer expectations.

Changing customer expectations, alternative asset classes, regulatory requirements, the increasing cost of doing business, and the rising war for talent are placing strain on today’s financial institutions.

Despite these pressures, the industry continues to rapidly explore the opportunities to apply AI and other emerging technologies to support their entire value chain. Many financial institutions have already made significant advancements in adapting their business functions and internal operations, including regulatory and compliance, to address current issues and accelerate time to market. Much work remains to be done, especially in such a highly regulated industry, but the potential is nonetheless to great to ignore — certainly plenty of start-ups and challenger banks and lenders are diving right in.

One striking example of how AI can help is by tackling one of the most onerous and persitent challenges financial institutions face: money laundering. The estimated amount of money laundered each year is 2% to 5% of global GDP, or up to $2 trillion annually. To help tackle this problem in new ways, Google Cloud recently launched AML AI, an AI-powered anti money laundering product for financial institutions. It increases risk detection by as much as 400% while eliminating at least 60% of false positives, which helps reduce operational costs. Overall, the service can greatly improve governance and defensibility.

“HSBC is one of the world’s largest banks, we’re in more than 60 countries with more than 40 million customers,” Jennifer Calvery, the group head of financial crime risk and compliance at HSBC, said at Google Cloud’s Financial Services Leaders Forum earlier this year. “We are wanting to make sure our products and services are not exploited by individuals who would use them for crime. We review more than 1.2 million transactions every month looking for signs of financial crime. Last year, we filed 73,000 reports of suspicious activity globally.”

Using AML AI, HSBC was able toimprove detection capability, deliver more accurate results, and significantly reduce batch processing times for its large customer base.

It’s just one example of how companies need to start changing their way of thinking to fit a new way of working. Innovation is no longer a years-long journey; what took months to build can now be completed in a matter of minutes or hours. The key is to move at speed and have the underpinning of security and data to make the shift.

We believe there’s an opportunity for financial institutions to drive more revenue by placing the customer at the center, and emerging technologies like generative AI are key to unlocking those possibilities and redefining how companies serve their customers going forward.

AI has the potential to transform the finance industry with big opportunities for the front, middle and back offices. Potential cost savings for banks from AI applications is estimated at $477 billion by 2023, with the front and middle offices accounting for $416 billion of that total.

For instance, conversational AI is evolving quickly to amplify call center efficiency and cost savings by bringing together virtual agents, contact center AI, agent assistance, and insights. In the past, these capabilities were typically very complex to build. Today, a bank can build simple chat bots in minutes using content from its own data sources and websites or use pre-built flows to create custom user journeys unique to its own services and business operations.

The most important ingredient in any product — even an AI product — remains the company’s own IP, culture, and talent, which are key to enhancing the AI and differentiating it from other organizations.

New AI technologies are proving particularly effective at enhancing cross-sell and retention strategies. Instead of one-to-many-messages and interactions, institutions can now deliver “you know me” experiences through conversational language and financial product recommendations tailored to an individual.

Generative AI is also helping companies make the most of their existing workforce, especially in a sector where AI and development talent is not only scarce but expensive. These capabilities can reduce the amount of time developers spend on research, helping them create code from natural language descriptions, auto-completing code within integrated development environments, and assisting with writing and generating tests.

There are hundreds of different use cases for financial services organizations to offer up the next best action to customers at scale, manage risk, make employees more productive, and enhance internal process workflows.

Here are just a few examples of how generative AI can help:

• Research: Accelerate research and increase the quality of decision-making by searching across proprietary datasets, structured and unstructured data, and websites.
• Lending: Enhance underwriting by unlocking internal and external sources while automating the generation of loan documents and client summaries.
• Investment banking: Develop M&A theses, generate financial statement analysis, and draft earnings reports, credit memos, and pitch books.
• Treasury: Provide ad-hoc portfolio exposure information and summarize attribution for portfolios.
• Back-office operations: Improve back-office productivity by helping to locate and summarize information and improve communication between internal groups and customers.
• AML/Fraud: Enhance fraud detection and query large amounts of information at speed and scale.
• Trading: Interact with trade facilitators to better understand settlement failures and how to resolve them.
• Regulatory change: Translate changes in regulatory and business requirements into code using knowledge bots.
• Compliance: Detect anomalies, automate manual controls, and oversee new product delivery compliance.

While interacting with generative AI should feel natural and low effort, successful adoption will require enterprise scale to control proprietary data, deal with fraud and security, ensure everything is accurate and explainable, control costs, and integrate existing data and applications. Financial organizations want the ability to ground answers in their own data, integrate capabilities into their backend while respecting existing business logic, and ensure that models match the problem at hand to avoid unnecessary spending.

At Google Cloud, we believe we have the safest and most secure offering for generative AI. We’re creating distinct value for financial firms by providing these fundamental building blocks and an enterprise platform for building new customer experiences. At the same time, we give organizations full control over their data and the highest level of enterprise-grade security, including specific security and compliance controls for generative AI. Your data is your data, and it’s never used to train other models.

More than a thousand organizations are already using Google Cloud generative AI to have smarter conversations with customers, break down limitations, and increase productivity. The power of our technology and breadth of solutions are contributing to a rapidly growing list of case studies and success stories from customers around the world — and we’re just getting started.