Understanding Opportunities for AI in Banking

Over the past decade, the world evolved from basic chatbots to a few niche users of ChatGPT to a widespread use of artificial intelligence across organizations. Primary examples of AI usage today range across nearly every industry. 

The retail industry leverages AI to deliver personalized ads and shopping recommendations to customers based on browsing and purchase histories. In healthcare, AI is leveraged to help analyze medical images and detect conditions like cancer, heart disease, and more. In the automotive industry, AI is used to plot travel routes and now, even drive the car with advances in autonomous vehicles. The list of examples goes on — agriculture, manufacturing, entertainment, utilities, etc. 

Within financial services, AI can power better money experiences, faster credit and loan decisions, financial advice and investments, customer service, and more. As financial services companies embrace AI for a multitude of use cases, the key lies in the data. Companies should focus on leveraging data in a way that maximizes integrated, inclusive, and intelligent use cases. Here’s a closer look at 5 opportunities for AI in banking: 

1. Intelligence Amplified

Artificial intelligence can augment and enhance data analysis processes and tools to enable smarter, faster insights based on large sets of data. It can also help uncover hidden patterns and previously unrealized opportunities that can drive significant value for organizations. McKinsey & Company calls out that AI’s potential to create value for financial services organizations is one of the largest across industries, potentially unlocking $1 trillion of incremental value for banks annually.

2. Operational Efficiencies

According to IBM, 54% of organizations are seeing cost savings and efficiencies from using AI in IT, business, or network processes. These operational efficiencies can take the form of everything from automating repetitive tasks and reducing manual errors to filling labor and skills shortages and enabling chatbots to handle less complex customer service inquiries. 

3. Financial Inclusion

While the risk of AI-generated bias in financial advice is real, AI can also break down barriers to financial access for unbanked and underbanked communities in a number of ways: 

• Democratizing access to financial products and advice 
• Empowering better money management with personalized insights and tools
• Expanding credit access to those with little or no credit history

4. Fraud Detection

A survey from Mastercard and Fintech Nexus reveals that “increased fraud detection” ranks as the primary driver (63%) behind AI investment among financial institutions. AI can improve fraud detection and prevention by analyzing patterns and anomalies in transaction data more quickly and efficiently. In fact, a study by IBM found that AI-powered fraud detection systems will reduce false positives by up to 70%, resulting in significant cost savings and improved customer experiences. 

5. Personalization at Scale

Eighty percent of consumers say customer experiences should be better considering all the data companies collect, according to Salesforce’s State of the Connected Customer report. However, it’s difficult to truly understand customers and deliver personalization at scale when most financial providers only see a small fraction of a consumer’s financial life. AI can help financial providers deliver personalized experiences at scale by helping to automate and personalize support, as well as translate financial data points into actionable intelligence. 

Want to learn more? Check out the full report on the top risks and rewards for AI in banking: https://www.mx.com/guides/risks-rewards-ai/