Transforming Finance: The Impact of Generative AI on Financial Data in the Past Year

March 28, 2024
Douglas Hopkins
Tech4Fin

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Executive Summary

The emergence of Generative AI (Gen AI) within the financial industry has sparked transformative changes in just over a year. Although raising concerns about privacy and data veracity, Gen AI has revolutionized financial data analysis, enabling rapid predictions, enhancing customer interactions, aiding risk management, and simplifying regulatory compliance.

Surprisingly rapid adoption by firms, driven by prior awareness, access to vast data, and support from tech giants, has led to cost reductions, competitive advantages, and talent optimization. Moving forward, firms with robust data expertise and versatile access are poised for innovation, while vendors offering diverse operational capabilities are set to lead, shaping a more efficient and responsive future for finance.

Introduction

ChatGPT was released to the public just over a year ago on November 30, 2022. Over that year, the conversation in Finance has broadened, evolving from ChatGPT to Large Language Models (LLMs) to Generative AI (Gen AI). It’s been a transformational year to say the least.

In this article, we’ll explore the initial concerns over Gen AI appearing in the industry, the revolutionary changes these technologies have brought to the financial landscape, and how quickly and in what ways the market has adopted and adapted to Gen AI.

We published The Evolution of Alternative Data in Finance is Being Driven by LLMs in June 2023. The thesis was that the three most valuable types of AltData – Alternative, Fundamental, and Proprietary Data Lakes – had been disrupted by a monumental Gen AI shift. The financial market moved from a data mining model to having any query answered instantly in a clear and concise manner in a search engine style interaction. Initially firms raised two major concerns: the security of their IP, especially for private data, and the traceability and veracity of Gen AI’s output.

Initial Concerns

Private vs. Public Data

Publicly available data refers to information that is accessible to the general public without restrictions or limitations. This type of data is typically made available by government agencies, research institutions, businesses, or other organizations for public use and dissemination. Publicly available data can take various forms and may include: government data, research data, websites and online platforms, published reports, open data initiatives, and others.

It is important to note that even while publicly available data is accessible to the general public, its use may still be subject to certain terms of use, licenses, or restrictions imposed by the data provider.

In contrast, non-public data or private data is information that is not openly accessible to the public and may be subject to confidentiality, privacy, or security restrictions. Access to non-public data is typically restricted to authorized individuals or entities, and its use is often governed by legal and ethical considerations.

Private Companies have been concerned that their proprietary data lakes can be used in the processes to train Large Language Model (LLM) or other types of Gen AI models. The result is that many firms working with vendors like Symphony’s Amenity Analytics division deploy their models on-Prem

Traceability and Veracity

Traceability refers to the ability to trace the origins and development of data and processes within an AI system. It involves documenting and understanding how the model was trained, what data was used, and how decisions are made.

Traceability is crucial for accountability, transparency, and ethical AI practices. Understanding the lineage of data and the training process helps in identifying biases, ensuring compliance with regulations, and addressing potential issues related to the model’s behavior. It also aids in debugging and improving models over time.

Veracity in AI refers to the reliability and accuracy of the data used to train and operate the model. It’s about ensuring that the information in the training process is truthful and reflects the real-world scenarios the model is meant to address.

Veracity is essential for the credibility and effectiveness of AI models. If the training data contains inaccuracies or biases, it can lead to unreliable predictions or outputs. Ensuring the veracity of data is critical for making informed decisions and maintaining the trust of users and stakeholders.

In summary, both traceability and veracity are vital for financial firms to ensure ethical user trust, and effective decision-making. Without understanding the source of the data it in essence becomes a risk and cannot be trusted to incorporate into output processes such as equity research.

Enhanced Data Analysis and Insights

Gen AI has revolutionized data analysis in the financial sector. These models are being used to ingest financial data, providing valuable insights and noting trends that might have been challenging or too time consuming to identify using traditional methods. Financial analysts now have a powerful ally in deciphering complex datasets and making informed decisions.

The speed at which financial markets operate demands real-time insights. Gen AI has played a pivotal role in enabling financial institutions to make more accurate predictions. By analyzing historical data and market trends, these AI models can generate real-time predictions, empowering traders and investors to respond swiftly to market changes.

Gen AI models have also been integrated into customer service platforms, enhancing the way financial institutions interact with their clients. AI powered chatbots are able to provide personalized financial advice, answer queries, and guide users through various financial processes. This not only improves customer satisfaction but also streamlines operations for financial institutions.

With their demonstrated ability to identify patterns and anomalies, these models have also become a crucial tool in risk management and fraud detection. By analyzing transactional data and identifying irregularities, these AI models contribute to the development of robust security measures. Financial organizations can now proactively detect and prevent fraudulent activities, safeguarding the interests of both businesses and consumers.

Regulatory compliance is a critical aspect of the financial industry. Gen AI has simplified compliance processes by automating the extraction of relevant information and generating comprehensive reports. This not only ensures adherence to regulatory requirements but also reduces the burden on financial professionals, allowing them to focus on more strategic aspects of their roles.

Fast time to Market

One of the biggest surprises so far is how fast firms have come to market with Gen AI powered tools and in some cases, their own custom models. We see this in examples ranging from early movers, like Bloomberg’s BloombergGPT, to even more recent tools, such as JPMorgan’s DocLLM, announced on January 4th. And there are plenty more examples of early adoption. There are a few reasons for the quick adoption. First, many of the largest firms were not surprised by the November 2022 ChatGPT release. Both Silicon Valley and Finance were aware of the large PE activity. The second reason is that many firms already had both large public and private data within their data lakes. And the most important factor was that existing partners and big players, such as Microsoft/OpenAI, Amazon, and Google were already providing the LLM technologies and capabilities to custom tailor them to specific needs.

In addition to the reasons already described above, we see rapid adoption based on a few more critical aspect:

Cost reduction, mainly in the form of new opportunities for automation, both saving time and decreasing potential error
Competitive and innovative advantages, which is critical for firms in the financial industry where maintaining a competitive edge can make or break a firm
Talent/human capital optimization, including some of the brightest minds we have today being put towards real challenges that can be solved only by human judgment and creativity
Keeping pace with technological advancements, just like we see today with the early movers to mobile, it pays to stay ahead of the game when it comes to paradigm shifting technologies

Conclusion

The emergence of Gen AI has ushered in a new era for financial data processing and analysis.

Firms with data teams, experience, and access to both large public and private data lakes will have the advantage to develop their own models.

Vendors with the ability to operate on behalf of clients both on-Prem and off-Prem will emerge as the leaders in the race.

As we move forward, it’s exciting to anticipate further innovations and applications that will continue to shape the future of finance, making it more efficient, secure, and responsive to the dynamic nature of global markets.

It’s important to note that the responsibility for data protection is shared between the companies developing these models and the organizations implementing them. Users of generative AI models should also be aware of the potential risks and take appropriate measures to protect their data when interacting with these systems. As the field of AI and privacy evolves, ongoing efforts are made to enhance the security mechanisms surrounding generative AI and large language models.

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Transforming Finance: The Impact of Generative AI on Financial Data in the Past Year

Interested in learning more?

Executive Summary

Introduction

Initial Concerns

Private vs. Public Data

Traceability and Veracity

Enhanced Data Analysis and Insights

Fast time to Market

Conclusion

Interested in learning more?

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