Big Language Models in Banking: Value-Creating Practice Examples

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Major Language Models (LLMs) are revolutionising various industries, and the banking sector is no exception. These powerful models are able to provide innovative solutions to complex problems by processing, understanding and producing human-like texts. In this blog post, we'll look at how LLMs are reshaping banking, their practices, and the value they bring to financial institutions.

Customer Loyalty

Personalized Marketing Communication

In today's banking environment, personalization is the key to customer loyalty and acquisition. LLMs can create personalized marketing communications by analyzing customer data — including transactions, preferences, and behavior patterns. For example, an LLM:

• Can identify valuable customers and estimate their financial needs.
• It can create custom messages promoting products, such as loans, investment options or savings plans.
• Automate and personalize email campaigns to ensure that messages are tailored to individual customers.

Banks can develop customer analytics and create marketing strategies by integrating LLMs into data streams.

Chatbots and Virtual Assistants

Demand for instant customer service has increased, and LLM-powered chatbots and virtual assistants play an important role in meeting this need. Unlike traditional rules-based systems, these advanced bots:

• Understands and answers complex customer questions expressed in natural language.
• It can cope with different languages and dialects, appeal to a diverse audience of customers.
• It supports transactions such as balance inquiry, transaction details, loan applications and fraud alerts.

A bank's virtual assistant can reduce wait times, increase customer satisfaction, and reduce operational costs.

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Operational Efficiency

Document Processing

Manual document processing is time consuming and prone to error. LLMs offer a more effective alternative by automating the process of extracting and analysing critical information from documents:

• Loan applications and loan agreements.
• Legal compliance forms.
• Insurance claims and financial reports.

Through LLMs, banks can speed up workflows, improve accuracy and ensure compliance with legal standards. For example, Dataikuhelps banks build scalable document processing solutions tailored to their needs by enabling LLMs to combine with machine learning pipelines.

Internal and External Information Management

Effective information management is vital for modern banking operations. LLMs enable institutions to:

• Collecting and editing internal documents, policies, and reports.
• Providing employees with immediate access to accurate and contextual information.
• Improving the quality of self-service portals by developing customer-oriented information bases.

These capabilities reduce information search times and provide consistent and reliable responses to both employees and customers.

Risk Assessment and Fraud Detection

Fraud detection and risk management are critical in banking. LLMs develop these functions:

• Analyzes large datasets and identifies unusual patterns and suspicious activity.
• Identifies potential risks in loan applications and investment portfolios.
• Generates detailed reports to help decision makers reduce risks.

LLMs today are viable resources for business and operations teams to improve productivity without the need for complex data science studies or cutting-edge technology. All of these viable use cases have one thing in common: a human in the cycle. This constraint means that the current generation of use scenarios has added significant improvements to speed, accuracy and accessibility without changing the underlying structures, rather than processes becoming unrecognizable.

By integrating LLMs into their systems, banks can automate workflows, reduce costs, and improve customer experiences. Komtaş's innovative technologies enable efficient and scalable implementation of LLMs, accelerating banks' digital transformation processes.