Generative AI and Decision-Making in Financial Markets

This article explores how three widely used generative AI tools – ChatGPT from OpenAI , Gemini AI from 谷歌 , and Microsoft Copilot from 微软 – can inform companies about their risk management capabilities, making them ideal decision-support tools in financial markets.

While generative AI encompasses various models, Large Language Models (LLMs) are particularly impactful in finance. Trained on massive datasets of text from various sources, LLMs capture the complexities of natural language and relationships between words to serve specific text-generation goals.

ChatGPT - OpenAI

ChatGPT , the most popular LLM tool today, boasts over 180 million monthly users according to the latest data. Launched by OpenAI in November 2022, it was trained as a chatbot to process natural language and generate human-like responses to a wide range of prompts and questions.

OpenAI offers two versions for companies: Teams and Enterprise. Both provide to various departments like engineering, marketing, sales, finance, and HR. Teams helps smaller groups explore AI with features like code generation and basic data analysis. Enterprise tackles complex workflows, big data, and stricter security with advanced functionalities and customization options. More information can be found here: https://openai.com/pricing

At Orka, innovation and automation are our core values, and as a FinTech company, we believe in the power of technology to drive innovation, efficiency, and profitability.

We also believe in the power of AI to augment human capabilities, not replace them. Tools like the ones developed by OpenAI serve as a valuable assistant to our engineers, by simplifying coding and development tasks, allowing them to focus on more creative and strategic work.

Gemini: Google's Generation Model

In 2022, 谷歌 AI launched Bard, its first-generation LLM, capable of generating text formats, answering questions informatively, and engaging in basic conversations. In March 2024, Google AI, in collaboration with Google DeepMind , introduced Gemini AI , a next-generation model building upon Bard's capabilities, offering a more powerful and versatile AI experience.

A key distinction between Gemini and ChatGPT lies in their architecture.

ChatGPT uses a traditional Transformer model where a single large neural network handles all information processing. Gemini leverages a Mixture-of-Experts (MoE) architecture, splitting the workload amongst a pool of smaller, specialized "expert" models, each focusing on specific subtasks. During operation, the MoE system routes incoming tasks to the most appropriate expert model(s).

Gemini offers business and enterprise versions. More information can be found here: https://workspace.google.com/solutions/ai/.

Copilot: Microsoft's LLM Integration

Microsoft Copilot , an LLM developed by 微软 and launched in 2023, is the primary replacement for the discontinued Cortana. Introduced as Bing Chat, it was initially a built-in feature for Microsoft Bing and Edge. Copilot utilizes the Microsoft Prometheus model, built upon OpenAI's GPT-4 foundational large language model, which is further fine-tuned using supervised and reinforcement learning techniques.

Copilot integrates with Microsoft 365 applications (Word, Excel, PowerPoint, Outlook, Teams, etc.) and Business Chat. Business Chat works across the LLM, Microsoft 365 apps, data like calendars, emails, chats, documents, meetings, and contacts.

Copilot offers personal, business, and enterprise versions. More information can be found here: https://www.microsoft.com/en-us/microsoft-365/microsoft-copilot#tabs-pill-bar-ocb9d49_tab2

Generative AI's Impact on Business and Finance

As generative AI provides a deeper understanding of natural language, businesses will increasingly use them to capture, analyze, and leverage data. The following sections explore how these tools can be used in business and finance for customized services, risk management, and decision support.

Benefits Across Sectors

Generative AI can provide automated customer service solutions, increasing efficiency, reducing costs, and enhancing customer experience. Companies can use generative AI to understand customer needs, interact directly with customers, and customize marketing strategies.

For example, e-commerce companies can use automated chatbots to respond to customer inquiries quickly, improving user experience while saving costs. Generative AI can also enhance digital marketing campaigns, improving customer engagement and data collection.

Financial institutions like banks can use it for customer logins, account checks, and personalized services without requiring branch visits. Insurance companies can use it to expedite claim evaluations. In the FinTech sector, generative AI can play a crucial role in digital advisory and provide portfolio management services with minimal human involvement.

For financial institutions lacking data to train models or conduct stress tests, generative AI can generate synthetic data compliant with privacy regulations. This is achieved by learning patterns and relationships from actual data. Such synthetic datasets can achieve a certain level of similarity to the original data without compromising customer privacy.

Risk Management Applications

During the lending decision process in credit risk management, a significant amount of textual information is generated. Lenders can leverage generative AI to analyze loan textual assessments and provide extended information on borrower eligibility. Future research could compare generative AI default predictions for crowdfunding with actual default risk based on disclosed information. Additionally, generative AI could be used to assess the quality culture of crowdfunding projects.

Furthermore, generative AI can produce detailed, loan applicant-friendly explanations for rejections, maintain positive customer relationships, and improve future application processes.

In the accounting and auditing domains, generative AI can automatically generate coherent, informative, and well-structured financial reports based on historical data, including balance sheets, income statements, and taxation documents. This process could significantly reduce the operational risks of manual errors.

Additionally, generative AI can produce synthetic cases of fraudulent transactions or activities for fraud detection. This can help augment algorithms and more efficiently differentiate between legitimate and fraudulent patterns.

Conclusion

Generative AI offers a powerful set of tools with the potential to transform financial markets. By leveraging its capabilities for data analysis, risk management, and decision support, financial institutions can gain a significant competitive advantage. However, it is crucial to address the potential limitations and ethical considerations surrounding generative AI to ensure its responsible and sustainable use in the financial sector.