The Rise of ChatGPT: A Pioneering Force in Financial Sector Evolution

The introduction of ChatGPT in November 2022 marked a significant milestone in the technology landscape, creating ripples not only in the professional realm but in numerous other sectors such as education. Users have been impressed by the AI-based solution, lauding its quick-witted and often accurate responses to a diverse range of queries. Silicon Valley witnessed heightened activity in the initial first few months of last year, with tech giants such as Meta and Alphabet keen on highlighting their own AI ambitions.

Despite the broad recognition of AI and large language model (LLM) technology, there has been less focused exploration of their potential applications in various sectors. However, the financial industry shows promise and merits closer scrutiny, with industry strategists advised to consider use cases, potential benefits, and limitations early on.

Front-End Service Enhancement

The integration of AI-based LLMs in direct customer interactions is evident. The technology’s dialogue capabilities have astounded users, envisioning chat solutions handling customer inquiries from a bank’s customers at a level comparable to human bank employees. This approach ensures round-the-clock availability for customers without delays, and if the bot gets stuck in individual cases, there is always the option of transferring the process to a human specialist. While the concept of “man types, machine answers” is not new, ChatGPT has heightened user interest and acceptance, whilst highlighting the sophistication of these solutions.

This application is not limited to chat variants; telephone applications are also conceivable. Voice banking, i.e. banking services via voice assistants such as Alexa or Siri which have previously been abated, could experience a resurgence in the wake of the LLM hype and rapid technological development.

Contributing to the Digital Offensive

ChatGPT garnered praise from university professors in IT and software programming in the first few months of its release. The solution has often outperformed students in terms of programming skills, now reaching the level of a junior developer. Integrating these solutions into an IT strategy becomes an obvious choice. LLM models can significantly contribute to development and programming activities, which is generally a weak point for financial institutions and addressing the shortage of specialists in digital offensives within the financial industry. If used correctly, the technology can help banks to advance their own digital presence faster and better in the future, bringing new services to market sooner.

Efficiency Boost in Process Management

Back-end processes are poised for revolution with the integration of this technology. The chatbot can serve as a knowledge carrier within an organization, providing immediate assistance with employee queries. This harbours unimagined potential today – for example in onboarding: with the help of the digital assistant, it will be much easier to quickly familiarise new employees with the bank’s processes.

Employees can expect relief as AI technology offers opportunities to better analyse customer needs, transactions, and future requirements. LLMs also present possibilities in credit decision processes, analysing creditworthiness or evaluating company information. Despite the freely available version of ChatGPT working on outdated data, it can handle detailed questions about financial reports.

AI solutions can extend to fraud detection, uncovering implausibility in annual reports and drawing the analyst’s attention to them. If the AI recognizes clear violations, a credit application can be rejected directly, and the responsible analyst can be instructed to investigate further if indications are detected.

Generated text modules and fragments from the solution can also facilitate the preparation of articles or contracts in a customized and regulatory-compliant manner for analysts or legal experts.

Great Opportunities, Dangerous Pitfalls

The diverse applications within financial institutions present significant opportunities. However, the regulatory framework imposed on banks, including strict requirements for handling, and analysing sensitive customer data, should not be underestimated. It is therefore even more important for institutions to address the regulatory potential at an early stage. Efficiency and cost issues have been prominent industry topics, and LLM and AI offer promising solutions, hence it is crucial for banks to delve into the detailed possibilities of this technology.