Artificial Intelligence (AI) in Banking

AI in Banking Key Takeaways

AI can help banks differentiate their services and work more efficiently while staying ahead of advanced cybercrime.
If implemented fully, AI solutions can help banks unlock hundreds of billions of US dollars in value annually.¹
Explainable artificial intelligence (XAI) and responsible AI will become top priorities to help banks deploy AI in a compliant manner.

Artificial intelligence (AI) in banking has matured into sophisticated platforms that can analyze more data types and generate human-like responses. These new toolsets are quickly becoming a cornerstone of digital transformation in the banking sector as AI methodologies are applied to practices such as underwriting and fraud detection.

Why AI in Banking?

Banking institutions face constant pressure to increase their competitiveness, especially as customer expectations for frictionless digitized services continue to rise. Banks also need to differentiate themselves while staying ahead of regulatory requirements and increasingly sophisticated cyberattacks. Machine learning and generative AI (GenAI) can help banks adapt to and overcome these challenges. AI in banking can introduce new toolsets to help workers be more productive and enhance traditional workflows with greater automation and the ability to understand and act on higher volumes of unstructured data.

Retrieval-Augmented Generation (RAG) for Banking

As banks evaluate the potential value for large language models (LLMs) such as ChatGPT to help engage customers and empower workers with AI chatbots and personal assistants, RAG can help reduce liability resulting from inaccurate responses. RAG is an innovative approach to LLM deployment in which the AI model references an enterprise-specific knowledge base when responding to queries. As a result, AI-generated responses can be knowledgeable about specific product offerings, bank protocols, and branding in a more compliant manner.

Benefits of AI in Banking

AI has the potential to create personalized experiences and product offerings, be better at forecasting market trends based on more comprehensive data analytics, and recognize anomalous behaviors that could indicate a cyberattack or instance of fraud. With advanced AI toolsets, banks can help attract and retain more customers, make smarter decisions, and help prevent and respond to cybercrime faster and more efficiently. McKinsey estimates that GenAI could unlock an additional US$200B to 350B in value for the banking industry annually if implemented fully.¹

AI Use Cases in Banking

Machine learning and GenAI power several real and potential use cases in banking. Here are just a few examples:

Personalized customer service and marketing automation: Intelligent AI chatbots can help provide more-comprehensive and empathetic replies to customer inquiries and help boost satisfaction scores. GenAI can also help create tailored outreach plans for prospective customers, potentially improving the impact of communications by targeting the right channels, times, and frequency of contact.
Client onboarding, loan assessment, and underwriting: GenAI can help analyze unstructured data and text-heavy documents, such as industry or news reports, to help deepen know your customer (KYC) processes. Banks can also use GenAI to accelerate loan underwriting in a compliant manner.
Staff productivity: Enterprise GenAI services can help improve individual productivity by automating tedious tasks related to inbox management, drafting meeting notes and action summaries, and summarizing heavy-duty analysis into searchable, conversational content.
Anti-money laundering (AML) and fraud detection: AI-enabled cybersecurity systems can analyze transaction patterns in near-real time to help identify suspicious activity and even automate fraud detection, alert, and remediation processes for smooth, efficient operations.
Confidential computing: Hardened platforms use hardware-enabled isolation to help protect data at the memory or virtual machine level against data breaches. Confidential computing can also support federated learning for AI model training and inference, allowing multiple banks to learn from and identify patterns in the same shared data pools while preserving customer confidentiality and privacy.

The Future of AI in Banking

As AI deployments become more capable and complex, they will also need to be more transparent to help ensure they satisfy stringent regulatory requirements, customer expectations for privacy, and the elevated threat profile for sensitive financial data. More so than other industries, banks will need to follow principles of explainable AI (XAI) and responsible AI to help understand and communicate how machine learning and GenAI systems produce specific results and outputs. This is especially important for determinations that impact customers’ access to financial opportunities, such as loan eligibility.

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Frequently Asked Questions

AI in banking describes how banks are increasingly using AI to analyze more types of data, inform decision-making, and help prevent cybercrime. These new toolsets are a cornerstone to digital transformation in banking, assisting with everyday workflows such as marketing, customer service, underwriting, and fraud detection.

AI in banking can be used to personalize marketing efforts to attract and retain customers, improve loan assessment and KYC processes, streamline back office tasks such as inbox management and report generation, and enhance fraud detection by analyzing more data to identify suspicious transactions.

AI in banking will continue to become more sophisticated and capable of completing more-complex tasks. For banks to maximize their value from AI, they will need to implement rigorous explainable and responsible AI standards to help stay compliant and maintain trust with customers.