In the rapidly evolving world of financial technology (fintech), artificial intelligence (AI) and big language models (LLMs) are revolutionizing the way we interact with financial data and investments. From improving customer experience to reducing risk, AI technologies offer a wide range of applications that fintechs can use to develop innovative products. This article highlights the use cases, the debate between open-source and closed-source LLMs and how fintechs can strategically position themselves as partners for companies and banks.

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McKinsey analyzed the potential of generative AI in fintech and banking. For banks alone, GenAI offers the potential to create between $200 billion and $340 billion in new value. It is therefore a great opportunity not only for fintechs, but also for banks. But how can they both benefit from the joint innovations? What use cases are there? Are closed or open-source models more suitable? We will shed light on this in this blog article.

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AI use cases in the fintech sector

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Bain & Company analyzed GenAI use cases in the financial sector - it stands out that financial planning, financial analysis and internal audit offer a great opportunity for GenAI - i.e. when it comes to analyzing large numbers of documents quickly, but also effectively.

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There are countless applications for fintechs, banks and other companies in the financial sector. Let's look at an overview of the most important of them:

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1. Analyzing financial documents with LLMs

LLMs are ideal for processing and summarizing complex documents. Fintechs can use this technology to analyze financial reports, balance sheets, and regulatory documents. These models can extract key information, identify risks, and produce concise summaries for decision makers. For example, extracting key figures from SEC reports or quickly understanding credit agreements can significantly increase operational efficiency. In addition, LLMs can help with compliance audits by flagging potential regulatory issues in financial documents.

2. Sentiment analysis

By analyzing news articles, social media, and market rumors, AI models can capture market sentiment and predict trends. Sentiment analysis is particularly useful for understanding investor behavior and identifying early signs of market fluctuations. Fintech platforms can integrate this technology to provide real-time investment recommendations or alerts. Advanced tools can also differentiate between short-term trends and long-term market changes, which enables more informed decisions.

3. Customer Support with LLMs

AI-powered chatbots and virtual assistants can process customer inquiries, guide users through financial products and solve problems efficiently. These systems are not only scalable, but also provide 24/7 support, reduce operating costs, and increase customer satisfaction. Beyond simple interactions, these tools can offer personalized financial advice, which improves customer retention and loyalty.

4. Fraud detection

AI models analyze transaction patterns to identify anomalies that could indicate fraud. Machine learning algorithms continuously learn from historical data, improving accuracy when identifying suspicious activity. This is particularly valuable for payment platforms, online banking, and e-commerce services. Modern fraud detection systems can even predict fraud attempts by identifying signs and risky behaviors.

5. Credit risk assessment

AI can analyze borrower data, including unconventional metrics such as social media activity or payment histories from non-traditional sources, to assess creditworthiness. This democratizes access to credit, particularly for underserved populations. In addition, machine learning models can continuously refine their evaluation criteria, providing a dynamic and accurate credit score that adapts to borrowers' changing circumstances.

6. AI-powered asset management

AI-driven robo-advisors offer personalized investment strategies based on user profiles, goals, and market conditions. These tools also make asset management accessible to people with smaller portfolios and promote financial inclusion. By continuously analyzing market data, robo-advisors can also adjust investment strategies in real time and optimize portfolio performance.

7. Tax optimization

AI algorithms can identify tax savings opportunities by analyzing financial data and investment portfolios. Fintech platforms can offer personalized tax strategies that help clients maximize their returns. AI-powered tax tools can also integrate with accounting systems to provide seamless end-to-end solutions from planning to filing tax returns.

8. ESG data analysis

Investments in environmental, social and corporate governance (ESG) policies have increased significantly. AI models can process large data sets to assess a company's ESG performance, enabling fintechs to offer products that meet sustainable investing trends. In addition, these tools can create transparency by identifying greenwashing and ensuring that the criteria of authentic ESG standards are met.

9. Integration of financial data

AI makes it easier to integrate various financial data sources and enables fintechs to offer consolidated dashboards that provide a unified overview of users' finances. This improves transparency and decision-making for both private and institutional clients. Beyond integration, AI can also derive actionable insights from this data and provide users with predictive analytics and personalized recommendations.

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LLMs: Open Source vs. Closed

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When choosing an LLM for fintech applications, the decision between open-source and closed-source models depends on the specific use cases, budget, and desired level of control.

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Open-source LLMS

Examples: GPT-3.5 Turbo (via API), Falcon, LLama

advantages:

• Cost savings: Open-source LLMs often result in lower operating costs, particularly when hosted on your own infrastructure.
• Adaptability: Fintechs can tailor these models to industry-specific requirements, such as financial jargon or compliance requirements.
• Transparency: With access to the underlying architecture, teams can review and improve models to ensure they meet ethical and regulatory standards.

challenges:

• Technical expertise: Using open-source LLMs requires an experienced team to deploy, maintain, and scale the models.
• Infrastructure costs: Hosting and operating large models can be resource-intensive.

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Closed-source LLMS

Examples: ChatGPT (via OpenAI), Google Bard

advantages:

• Ease of use: These models are ready to use and require minimal technical effort.
• Scalability: Vendors take over the infrastructure, which ensures seamless scaling and high availability.
• Regular updates: Vendors are continuously improving their models and offering cutting-edge features.

challenges:

• Dependency: Dependency on a provider can tie fintechs to a specific ecosystem.
• Data protection: Sharing sensitive financial data with third parties poses security and compliance risks.

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Which is better: open source or closed?

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For fintechs, the decision often lies in the balance between costs, adaptability and control. For startups or smaller teams, closed-source models could offer a quick start, while established players with robust technical capabilities find open source solutions more attractive in the long term. Hybrid approaches that combine open source models with closed system tools for specific tasks can provide a balanced solution.

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How fintechs can close technological gaps

Companies and banks often have to contend with legacy systems and a slow pace of innovation. Fintechs can step in as agile technology partners and offer:

• API integration: Seamless integration of new technologies with existing systems to improve payment processing, data analysis, or customer service.
• Innovative products: Development of new services, such as AI-based credit evaluation or automated investment advice, which are tailored to existing customer needs.
• Increased efficiency: Automating processes that were previously carried out manually, such as compliance checks or data analyses.
• Quick time to market: providing customized solutions that can be implemented faster than internally developed systems.

Through these partnerships, companies and banks can accelerate their digital transformation while benefiting from the innovative power of fintechs.

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Fintechs and banks: Cooperation instead of competition

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While fintechs have traditionally been seen as banks' challengers, the focus has increasingly shifted to cooperation. Banks bring an established customer base and regulatory expertise, while fintechs provide innovations and modern technologies.

A few examples of successful collaborations:

• Joint products: Partnerships to develop co-branded products, such as credit cards or digital accounts.
• Data partnerships: Banks can provide fintechs with anonymized customer data to enable better analyses and recommendations.
• Innovation programs: Banks are launching innovation hubs to attract fintechs as partners and scale their technologies.

This symbiosis enables both sides to play to their respective strengths and open up new market opportunities.

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Conclusion

The use of AI and LLMs in fintech has the potential to transform the entire industry. Fintechs have the opportunity not only to develop innovative products, but also to act as strategic partners for companies and banks. By integrating modern technologies, efficiency gains, better customer experiences and new market opportunities can be realized. The future lies in collaboration: Fintechs and banks that combine their strengths are creating a stronger and more agile financial ecosystem that is up to the challenges of the digital era.