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AI in Banking: 5 Impacts Artificial Intelligence Will Have on the Industry by 2025

Leslie Loges, Content Marketing Manager
March 18, 2024

The potential impact of AI in banking appears boundless. A 2023 McKinsey report found that effectively incorporating generative AI tools into business operations could lead to annual operational savings ranging from $200 billion to $340 billion for the global financial services industry. 

These cutting-edge technologies can also enhance customer satisfaction, attract more potential customers, and improve employee experience. Beyond revenue growth, generative AI has the potential to enable financial institutions to derive additional value from increased productivity, amounting to between 2.8% and 4.7% of the industry's annual revenues, according to the McKinsey report. 

Nervous about the risks that come with implementing AI? We wrote the AI Handbook for Financial Services Leaders just for you.

The current state of artificial intelligence in banking.

AI has been utilized in the financial sector for quite some time, primarily for tasks such as fraud detection, analytics, and forecasting. Predictive AI can analyze market data to produce recommendations for investors or manage investments autonomously using market projections. AI that is focused on anomaly detection can find irregularities to act as an alert system, while classification AI can identify and categorize information to streamline operations. 

More recently, generative AI has emerged as a compelling subject of discussion. While AI encompasses a wide range of technologies that mimic human intelligence, generative AI stands out for its capacity to create something novel based on the data it receives. 

Below are five ways AI will continue to shape the financial landscape this year.


1. Machine learning insights from synthetic credit data.

AI and machine learning techniques provide banks with the opportunity to make better credit and lending decisions by using synthetic credit data. Even though banks have vast amounts of real credit data, it’s difficult for them to use it for tasks like making a credit decision due to privacy and legal reasons.

By using generative AI to create synthetic credit data that mimics actual data, banks can use it to train their AI models and algorithms more effectively. This data can represent reality without the need to use real customer information and allow banks to create scenarios that can help them not only determine creditworthiness, but also what types of products and services they should consider offering.

2. Preventing fraud and detecting emergent threats.

For decades, financial institutions have depended on conventional, rule-based, and error-prone systems to monitor and screen for fraudulent transactions. However, with the rise in financial crimes and evolving fraud patterns, the landscape of fraud prevention has become increasingly intricate and demands more sophisticated technology, like AI.

KYC (Know Your Customer), collateral management, anti-money laundering, credit assessment, and fraud detection processes are complex stages of the lending lifecycle that are ideal for automation. Leveraging a data-agnostic approach like a data fabric enables financial institutions to bolster their existing systems by connecting data across the organization, creating a managed data pipeline to feed AI models. In addition to being able to review vast amounts of information quickly and reduce operational costs, AI can more accurately detect fraud and decrease the number of false positives.

8 experts weigh in on the future of artificial intelligence. Download their insights in the 2024 AI Outlook.

3. Personalizing market insights with generative AI.

Creating personalized market insights reports for clients that are tailored to their specific investment paths and risk preferences could be highly beneficial for many financial institutions. But it would also be a time-consuming process with the potential to deliver inconsistent information if it had to be done manually.

AI can be used to create and deliver such reports to clients using their transaction history and market dynamics combined with predictive analytics to craft customized commentary and insights. Bank employees can use the time savings to further personalize reports before sending.

By combining AI with human insights, banks can give their clients regular valuable market data to help them make better investment decisions and have a better overall banking experience.

4. Creating efficiencies in AML compliance and reporting.

New regulations continue to influence the way banks manage risk throughout their organizations. Changes due to DORA, Basel III, and others will increase the scrutiny banks face in their risk management processes and credit decisioning. Explainability and auditability will be crucial for banks, and AI can play a role in assisting these processes.

While traditional methods of determining credit risk are prone to errors like typos, copy-paste errors, and a misunderstanding of the data, AI can avoid such mistakes. Using AI, banks can get real-time insights into emerging risks while reducing the amount of routine tasks employees need to do, optimizing their risk management processes more efficiently and precisely.

Learn how to prevent fallout from AI hallucination in critical tasks such as risk management and fraud detection.

5. Making product recommendations for bank customers.

Mobile banking has changed profoundly in the last decade, with more and more customers using online tools to complete transactions. An October 2023 survey by the American Bankers Association found that 71% of customers prefer to bank on their phones or computer, while only 9% visit a branch, 8% use ATMs, and 5% call on the phone. Online banking has changed the way clients interact with their financial institutions, and banks are seeking new ways to create connections with customers.

While basic information can be accessed using chatbots today, many banks are working to create more powerful versions that make users feel they are talking to a human. The goal for many is to create a chatbot that can hold a conversation that’s informative and offers useful insights, like product recommendations made from a customer’s account data and history. AI can help banks bring back the type of personalized service the banking customer used to experience in person, with even better recommendations.

Navigating AI risks in financial services with private AI.

The surge in AI technology is driving digital transformation at financial services companies with its potential for growth and operational efficiency. But banks also need to be aware of the potential for risk. AI-based systems that use public models and publicly trained data can lead to informational leaks with costly impacts like regulatory fines and reputational damage.

Private AI is an option that financial institutions should consider when planning how to implement the technology at their organization. Private AI tools use models that are proprietary to your organization and exclusively train on your organization’s data. This helps control your data and information to avoid regulatory compliance issues and protects you from unintentionally giving competitors an advantage.

Can private AI help your organization do more? Learn how you can avoid public AI pitfalls and rapidly integrate private AI.