Artificial Intelligence: The importance of temporal validity for financial AI
Understanding time series data, such as price feeds of the capital markets, would be a huge competitive advantage for market participants.
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Fintech’s Future is AI: Building Better AI Models with Temporal Validity
CURATED UPDATES: LLMs and other Machine Models; AI Applications in Finance; Infrastructure & Middleware
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Fintech’s Future is AI: Building Better AI Models with Temporal Validity
While it may seem that generative AI is already capable of reasoning, the current generation of AI models lacks a degree of nuance to properly understand temporal validity, according to recent research by Wenzel and Jatowt from the University of Innsbruck – missing the timeliness of data and statements and their context. This shortfall is particularly relevant in the fintech sector, where the timing of information is critical for decision-making such as investment or money movement.
In human communication and data transmission, understanding the time involved in what someone says or writes is important, because it allows us to deduce whether or not the information is still true or relevant. Early information is valuable, whereas old or out-of-date information is not fit for purpose.
For example, if a person says, "I am driving home from work," and it has already been five hours, the recipient of that information will likely conclude that this person is likely no longer driving, if such trips usually take less than five hourse. The context of statements impacts their temporal validity duration. If “I am driving home from work” is followed by “There is a massive traffic jam,” the temporal validity duration of the target statement becomes longer.
In their paper, the researchers created a data set from X in which they tested temporal validity duration prediction (TVDP) in several leading AI models using a novel natural language processing (NLP) task format called Temporal Validity Change Prediction (TVCP). This task required the AI model to reason over whether or not a context statement changes the temporal validity duration of a target statement. Their results showed that ChatGPT and other models perform poorly in this regard. However, they also demonstrated that performance could be improved by explicitly including the TVD during model training, to improve embeddings.
This research indicates a promising direction for developing AI systems capable of handling temporal data more effectively. Such systems would significantly improve performance in tasks requiring nuanced time understanding.
As AI continues to evolve, its ability to comprehend temporal validity will be a key factor in its effectiveness, particularly in sectors like finance, where the timing of information is a critical component of decision-making processes and their profitability. Wenzel and Jatowt's research provides a foundational step towards realizing AI models that are not just data processors, but contextually aware interpreters of time-sensitive information.
The time dimension has previously been a challenge for machine learning in financial services as well. Whereas machine learning can be successfully used to underwrite particular risks for a lending or insurance data set, it struggles with time series data, such as the price feeds of the capital markets.
Therefore, AI in capital markets is often focused on very short-duration future predictions for high-frequency trading, rather than longer-term projections of market activity. Figuring out a way around this limitation by extending the predictive power of models – even if just by a bit – is a huge competitive advantage for market participants.
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Curated Updates
Here are the rest of the updates hitting our radar.
Machine Models
⭐ How AI and machine learning solutions drive value for financial institutions - IT World Canada
AI Applications in Finance
⭐ Scienaptic AI and DigiFi revolutionise credit lending with AI integration - Fintech Global
AI Will Transform the Credit Industry by Putting the Customer First - The Fintech Times
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks - Queen’s University and J.P. Morgan AI Research
Infrastructure & Middleware
⭐ Amazon turns to AI to help customers find clothes that fit when shopping online - Yahoo Finance
AI-powered search engine Perplexity AI, now valued at $520M, raises $73.6M - TechCrunch
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