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Home»Finance»AI in banking | IBM
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AI in banking | IBM

July 11, 2025No Comments
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There are several key benefits for banks that embrace and deploy AI.

Enhanced APIs: Banking operations increasingly depend on the use of application programming interfaces (APIs) to enable customers to track their money on various applications. For example, banks must give API permission to third-party budgeting apps so customers can monitor multiple bank accounts. AI enhances API usage by enabling more security measures and automating repetitive tasks, making them more powerful.

More intelligent customer tools: The rise of generative AI and agentic AI powered by deep learning means that the investment and banking industries can deploy more sophisticated tools to streamline customer service. AI-powered chatbots and virtual assistants can enhance customer support, helping customers solve small problems on their own. AI can also power budgeting apps that help customers better manage their finances and save more money.

Smarter credit scoring: Determining creditworthiness is a critical banking service activity. Banks need to crunch significant amounts of customer data to make important credit decisions, such as whether they accept a credit card application or approve a credit increase. AI algorithms and machine learning techniques can help financial institutions approve or deny credit cards, credit increases and other customer requests at fast speeds.

Enhanced cybersecurity and fraud detection: Cyberattackers increasingly use AI to create more sophisticated ways to defraud financial institutions. They can use AI-created audio3 to imitate customers, confusing customer service agents. They can use AI to make phishing emails look increasingly legitimate. As a result, those financial institutions can use AI algorithms to protect their employees from cybersecurity threats in real-time, while creating tools to help customers avoid the same tricks. Financial institutions and governmental agencies can also use AI systems to thwart other financial crimes like money laundering or impersonation.

Embeddable banking: This is the introduction of banking into nontraditional experiences, such as when Starbucks started its own payments app.4 Embeddable banking is expected to grow as a service, especially as AI helps retailers and other companies collect and analyze data about potential market opportunities, predict creditworthiness and better personalize services to customers.

New markets and opportunities:  AI-driven predictive analytics and forecasting tools can identify new areas of growth, improve underwriting processes and better estimate which customers are a churn risk. For example, banks can analyze their customers’ habits, such as how often they log in or deposit money and compare it to other data points to determine whether individual customers can be on the verge of canceling their accounts.

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