Internal Automation using LLMs

LLMs (Large Language Models) are at the forefront of emerging technologies, captivating minds and bringing significant shifts in the automation landscape. With GPT-4 as a prime example, internal automation using LLMs is revolutionizing everyday life, providing both generic and complex solutions. This new era will witness the integration of physical and cognitive automation, driven by advancements in physical automation technologies and the practical application of LLMs in operational enterprise scenarios.

On top of that, leading organizations are recognizing the potential of transformative automation initiatives while riding the wave of renewed AI enthusiasm. According to a report by McKinsey & Company on the actions undertaken by top CEOs in 2023, 45% of C-executives have embraced an automation mindset. In this context, LLMs play a pivotal role across industries, including the vast and extensive banking sector. Now, the question arises: How will LLMs shape the future of the banking industry?

What are LLMs?

Large Language Models (LLMs) are advanced artificial intelligence (AI) systems created to process, comprehend, and generate text that resembles human language. These models utilize deep learning techniques and undergo extensive training on vast datasets, often comprising billions of words from diverse sources like websites, books, and articles. Through this training, LLMs can understand language intricacies, grammar, context, and even some aspects of general knowledge. LLMs can undertake a wide range of functions, including answering questions, summarizing text, translating languages, generating content, and even engaging in interactive conversations with users.

The ongoing development of LLMs holds enormous potential for enhancing and automating various applications across industries, encompassing customer service, content creation, education, and research. However, their usage also raises valid concerns pertaining to ethical and societal issues, such as biased behaviour and misuse. It is crucial to address these concerns as technology advances to ensure responsible and beneficial internal automation using LLMs.

Internal automation using LLMs in the Banking Industry.

Integrating Language Models (LLMs) in the banking industry is revolutionizing internal automation processes. From transforming customer interactions to optimizing regulatory compliance, internal automation using LLMs offer enhanced personalization, efficiency, and accuracy, making them invaluable tools in streamlining operations for banks; here are some of the tasks that can be done by using LLMs:?

1. Transforming Customer Interaction: It brings a new level of personalization and efficiency to customer interactions, surpassing traditional chatbots:

• Natural Language Chatbots: LLM-powered chatbots engage customers in natural conversations, addressing queries, providing account information, and handling complex financial inquiries.

• Virtual Financial Advisors: It analyses customers' financial situations and preferences to offer tailored advice on investments, savings, and financial planning. Paired with document understanding, LLMs automation access extensive banks' knowledge bases to provide insights that human advisors may overlook.

1. Streamlining Documentation and Communication: LLMs revolutionize documentation and communication processes, which is crucial for a paperwork-intensive industry:

• Automated Reports: LLMs generate reports, summaries, and analyses from raw data, saving time and reducing the risk of errors.

• Client Communications: LLMs assist in drafting emails, official letters, and other correspondence, ensuring consistent and professional communication.

1. Semantic Analytics: Empowering decision-makers and citizen data scientists with accessible data results, removing the need for direct access to BI tools:

• Report Generation: It bridges the gap between the BI layer and requesters, interpreting requirements and dynamically building necessary reports (e.g., breakdown of defaulted loans by age, city, and car type).

• Augmented Reporting: It can analyze complex reports, explain their implications and offer suggestions based on the data.

1. Enhancing Fraud Detection: Implementing LLMs for automation significantly contribute to detecting and preventing fraudulent activities in banking operations:

• Text Analysis: It analyzes unstructured text data, including transaction descriptions and account notes, to identify suspicious patterns indicative of potential fraud.

• Risk Assessment: By examining customer interactions and communication patterns, LLMs assist in assessing the likelihood of fraudulent activity.

1. Optimizing Regulatory Compliance: It plays a vital role in ensuring banks' compliance with heavy regulatory requirements:

• Automated Compliance Monitoring: It tracks regulatory changes and assesses their impact on the bank's operations, maintaining compliance across processes.

• Policy Documentation: LLMs aid in creating and updating compliance policies and documents, ensuring an accurate and up-to-date repository.

Real applications of Internal Automation using LLMs

As generative AI gains recognition for its disruptive impact, financial institutions actively run proofs-of-concept (POCs) and experiments with language models.

1. 摩根士丹利 Wealth Management has launched an advanced chatbot powered by OpenAI's latest technology to provide its team of financial advisors with better customer service. The chatbot is designed to assist the team in leveraging the bank's massive research and data library. It will allow advisors and their teams to ask questions and analyse large amounts of data, with answers derived entirely from MSWM content and source material.

2. 荷兰銀行 Amro is also piloting generative AI in its processes to summarise conversations between bank staff and customers. ChatGPT generates summaries, enabling agents to focus on clients without taking notes.

3. 高盛 is experimenting to optimize automation with LLMs and help its software engineers automatically generate lines of code. Developers have been able to automate 40% of their code using this technology, which also aids in testing.

4. SouthState Bank Bank has adopted a language solution trained on bank documents and data to allow employees to query the system, interpret internal records, and perform various tasks, boosting productivity.

5. Westpac has deployed a language model trained on conversations and data in the banking industry for internal automation using LLMs to streamline the mortgage application process for lending staff and customers.

Other banks like JPMorgan Chase and Wells Fargo also experimented with ChatGPT-style technology for various applications. The boom of experiments with LLM automation solutions using generative AI in finance raises the question of its impact and potential uses in the industry.?

Conclusion

Effective LLMs implementation in the banking industry holds immense potential for streamlining internal automation processes. LLMs offer enhanced personalization, efficiency, and accuracy, transforming customer interactions, optimizing regulatory compliance, and revolutionizing bank documentation and communication processes. However, as with any emerging technology, addressing ethical and societal concerns is crucial to ensure responsible and beneficial deployment of LLMs in the banking industry. Overall, internal automation using LLMs shape the future of banking by bringing advanced automation capabilities that enhance operational efficiency and customer experience, paving the way for a more efficient and personalized banking sector.