How AI is unlocking new possibilities for data processing in fintech

Undoubtedly, AI has reshaped our modern day, with the fintech sector already investing heavily in the technology. However, AI is still in its infancy in all areas, with a myriad of possibilities still to be tapped into, particularly for the markets that deal with large amounts of data daily.

Multimodal AI

Multimodal AI is set to redefine how the fintech space interacts with data. Unlike unimodal AI, these models can process various types of data, such as text, visual, and audio, enabling applications to better understand real-world scenarios.

Traditional AI models, trained for a single data modality, perform well within their scope but struggle with new or mixed data. Similarly, large language models (LLMs), regarded as some of the most capable systems closest to true artificial intelligence, are generally trained exclusively on text data, limiting their understanding of real-world issues.

Multimodal models, on the other hand, can process information from multiple sources beyond just written words. For example, a model trained to interpret different data formats could not only analyse spoken words from a news report but also visual cues like facial expressions, tone, and body language—all of which could influence its assessment of whether the news has a positive or negative sentiment.

These models are more efficient at understanding complex, multidimensional data, making them ideal for detecting suspicious market behaviour by integrating insights from multiple sources. This makes them particularly suited to assessing market impacts and identifying illicit activities.

Customisation of large language models (LLMs)

There is a growing trend among large providers to reduce the costs associated with hosting and running models. This reduction, coupled with the rise of companies specialising in helping data-rich businesses fine-tune their models, is making model customisation more accessible and effective. Companies can now focus on creating highly tailored generative models that are often better suited to domain-specific tasks than general-purpose models. For example, developing a ‘customer support assistant for a forex broker‘ is becoming both more feasible and efficient, highlighting the advantages of specialised solutions over one-size-fits-all approaches.

These advancements are particularly beneficial to companies with access to proprietary data. Trading platforms with unique market data will gain a significant competitive advantage by providing personalised services for their users.

Retrieval Augmented Generation (RAG)

RAG combines traditional information retrieval systems with the capabilities of generative large language models (LLMs), offering greater accuracy and relevance in outputs. Timely and accurate information is crucial, especially in the finance industry where markets and regulations frequently change.

Due to its capabilities, we anticipate seeing self-managed knowledge bases for different teams (e.g., compliance, risk management, HR), with systems that implement role-based access controls and fine-tune assistants’ responses and capabilities.

Generative AI with structured data

Currently, generative AI is closely monitored by engineers to prevent errors, limiting its application primarily to internal productivity tools. However, future advancements will expand its potential, creating more opportunities for end customers. While models capable of handling different data modalities have been discussed, structured data specifically deserves attention. Machine learning models have long excelled in managing structured data, such as numerical inputs, but recent advancements in generative AI have enhanced these capabilities. AI can now manage less guided data analysis and generate comprehensive reports.

In fintech, for instance, generative AI can process and interpret structured data, making complex financial information more accessible and actionable. This could involve a single model interacting directly with data storage or a multi-model system where a ‘decision-making’ model generates a detailed report based on the outputs of other models. This approach is particularly useful for broker-dealers, saving them significant time that would otherwise be spent analysing reports. The technology simplifies data interaction, enhances decision-making, and broadens market participation.

Conclusion

AI is set to become even more integral to the fintech industry, with significant investment in the technology still to come. The innovations discussed will undoubtedly transform the sector. These advancements promise to streamline domain-specific tasks, including risk management, fraud detection, and investment research, by enabling more dynamic and adaptive systems that can handle the complexities of financial data.

By leveraging these technologies, companies can improve decision-making, detect suspicious behaviour, and personalise their services, all while freeing up resources and enhancing customer experience. In this way, AI-driven innovation is poised to become the norm in financial services, offering a competitive advantage to those who embrace it.

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Ivan Kunyankin has over five years of experience in data science, during which he has contributed to a variety of projects. For the last three years, he has focused on applying machine learning and AI in the fintech industry. At Devexperts, he currently leads a team dedicated to developing intelligent assistants for brokers and funded trading firms, with a focus on trading data analysis and risk management technologies.

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