The Future of Artificial Intelligence in Banking

Artificial intelligence (AI) is more than what you see in science fiction movies. Virtually every industry, from manufacturing and retail to health care and education, is considering using, investing in or leveraging AI due to its wide range of benefits and applications.

One industry expected to harness the power of AI is banking. While humans run most banks, it’s possible that AI will become ubiquitous throughout banking operations around the world. 

Banking, but Make it Digital

AI will play an increasingly important role in all aspects of life and how we manage our finances through banking is no exception. Consider just how much shopping has changed in the last decade. It’s more common to ship essential items to your doorstep than go to a brick-and-mortar store to browse and make purchases.

By the same token, the banking landscape is rapidly evolving. It’s easier than ever for banking customers to make transfers between accounts, deposit checks and send money to other people using modern banking apps. For example, the Bank of America app is among the highest-rated apps. In July 2022, the bank announced it had almost one billion digital logins from customers. 

Banking is becoming more digitally driven than ever, so it’s no surprise that AI will help pave the way for next-generation banking. 

The Role of AI in Banking

As AI has grown more advanced in recent years, many industries are exploring potential opportunities to deploy AI. 

Banks want to get in on the action, too — Allied Market Research predicts that AI in the banking market will reach over $64 billion by 2030. In 2020, the market was valued at only $3.88 billion. These predictions highlight how revolutionary AI will be for banks and their loyal customers.

Within the field of AI, other technologies will benefit the banking sector. For example, computer vision (CV), machine learning (ML) and natural language processing (NLP) will undoubtedly become a staple in future banking. 

In 2020, Chase’s chief data and analytics officer Sandra Nudelman, spoke at Transform 2020, a leading AI event for business and technology executives. During her talk, Nudelman outlined how the company is using AI and ML for:

• Improved marketing efforts
• PPP loan management
• Increasing credit lines
• Fraud prevention

Considering Nudelman delivered her speech two years ago, it’s safe to say that Chase and other leaders in banking will continue using AI, ML, CV and NLP for various purposes.

Use Cases for AI in the Banking Sector

One reason AI is so transformative in the banking and financial services industry is its seemingly limitless use cases. In other words, AI can be used in so many different ways, which helps banks meet their bottom line, get a high return on investment (ROI) and improve customer experiences and satisfaction.

Here are some of the top AI use cases in the banking industry, according to office type:

Front Office

• Conversational banking
• AI biometrics technology
• Personalized insights for customers

Middle Office

• Fraud detection
• Risk management
• Anti-money laundering
• Know-your-customer (KYC)

Back Office

• Underwriting
• Smart contracts infrastructure (relies on blockchain technology)

Two mature use cases for AI are AI-powered chatbots and anti-payment fraud in the middle office. As AI becomes more widespread in banking and financial services, more use cases will emerge. AI-based solutions will become increasingly affordable for the institutions looking to invest in and deploy them.

Benefits of AI in Banking

There are several benefits banks will reap by leveraging the power of AI. Other industries leading the way in AI adoption can attest to this — those who adopt and deploy AI often outperform their competitors. 

For example, well-known business consulting company Accenture reported that most companies are considered AI Experimenters, meaning they have yet to deploy AI fully. In other words, these organizations are only scratching the surface of the potential AI can offer.

Here are some of the benefits of AI in banking:

• Significant cost savings in the form of reduced operational costs and risks
• Boosts revenue
• Highlights new and previously untapped opportunities for banks
• Improves employee productivity and customer experience
• Improves loan and credit decisioning
• Improves fraud detection and regulatory compliance
• Automation of the investment process

According to one study by Accenture, 12% of companies surveyed already use AI to outperform their competitors. AI gives organizations a competitive advantage across several areas of operation. 

Compared to their less technologically advanced counterparts, these companies earn higher scores on Accenture’s AI maturity scale (almost doubling that of others) and report 50% higher revenue growth.

How Banks Can Successfully Deploy AI

While using AI seems like a no-brainer for banks and financial institutions, adopting and deploying AI is no walk in the park. In a 2021 report called “Building the AI bank of the future,” McKinsey & Company shared some of the common challenges organizations face in deploying AI at scale. 

Here’s a list of those challenges:

• Lack of a clear AI strategy
• A weak core and technology and data backbone
• An outmoded operating model and talent strategy
• Siloed working models

How can banks overcome these challenges to implement AI solutions successfully?

Six Critical Areas of Focus

According to Deloitte, there are six critical areas companies must focus on to deploy AI successfully. Learn about each area and why they’re important in AI deployment.

1. Develop a Strategy

Banks must move beyond the initial hype of the potential of AI. Instead, they must identify practical AI applications to implement into their operations. Rather than using AI capabilities, banks can transform into AI firms and, as Deloitte so eloquently puts it, “address the ‘how’ of execution.”

2. Define Use Cases

In this step, companies can focus on two things: they must define business value-driven use cases for their strategy and invest in diverse AI capabilities. Instead of leveraging a single, limited AI solution, they should focus on building a set of practical solutions that accomplish a wide range of business objectives.

3. Experiment With AI

Once a strategy is prepared and a company’s use cases are clearly defined, executives should bring that concept to life and experiment with AI models. Organizations might want to consider partnering with multiple AI vendors to explore the potential of AI and which solutions will prove the most valuable.

4. Build an AI Solution

After thorough research, development and experimentation, companies can build unique AI models to suit their specific business needs. Companies should use a proactive approach during the building phase, which involves managing risk and ethics, creating partnerships, balancing convergence and taking essential cybersecurity factors into account.

5. Scale for Deployment

In this stage, Deloitte suggests that companies should transition from a “nice-to-have” mindset to a “must-have” mindset regarding AI deployment. Essentially, the goal should be for the organization to shift from rigid, traditional operating models to a more adaptive, data-driven nimbleness across departments.

6. Drive Outcomes

Deloitte’s final stage of AI deployment for banks is driving sustainable growth and outcomes. After successfully deploying AI, banks should continuously monitor their solutions and discover new opportunities to enhance their AI capabilities. Deployed applications can evolve and banks need to be ready to adapt to these changes to generate business value.

AI: Shaping the Future of Intelligent Banking

There’s no doubt that AI will shape the future of banking. Intelligent banking is no longer a far-fetched idea. In fact, it’s becoming a reality for many banks and financial institutions nationwide and across the globe. We might not be able to pinpoint what banking will look like in the next decade, but one thing is certain: AI will play an essential role in the banking industry.