May 21, 2023
By Acuity Trading
Today we understand that Generative AI is going to change the way we work. Generative AI, is the evolution of text in to numbers and back into text. How did NLP and NLG help us arrive at ChatGPT and Generative AI? Which companies have helped us to evolve to where we are today and how this will affect financial services and trading? Where can we take advantage of these evolutions and how can we avoid the pitfalls?
Generative AI is a broader field that encompasses Natural Language Processing (NLP) and Natural Language Generation (NLG) as specific areas of focus. Generative AI refers to the use of artificial intelligence techniques to generate new content, such as text, images, music, and more.
NLP specifically deals with the processing, understanding, and manipulation of human language by computers. NLP techniques enable computers to analyze and interpret text, speech, and other forms of natural language data. NLP encompasses tasks such as text classification, sentiment analysis, named entity recognition, machine translation, and more.
NLG, on the other hand, is a subfield of NLP that specifically focuses on the generation of human-like text. NLG techniques use AI algorithms to automatically generate text that is coherent, fluent, and contextually relevant. NLG finds applications in areas such as content creation, data reporting, personalized recommendations, and more.
Generative AI can utilize NLP techniques to understand and analyze existing text data, and then use NLG techniques to generate new text based on the learned patterns. For example, a generative AI system may use NLP to analyze a large dataset of customer reviews and then use NLG to generate summaries or responses based on the analysis. Similarly, a chatbot or virtual assistant may use NLP to understand user queries and NLG to generate responses in natural language.
In summary, generative AI is a broader field that includes NLP and NLG as specific areas of focus. NLP enables computers to process and understand human language, while NLG specifically focuses on generating human-like text. Both NLP and NLG are important components of generative AI, enabling systems to understand and generate text in a wide range of applications.
Generative AI is a type of artificial intelligence that can create new content, such as text, images, or music. It is often used in conjunction with NLP and NLG to create more natural and engaging interactions between humans and machines.
NLP (Natural Language Processing) is a field of computer science that deals with the interaction between computers and human (natural) languages. NLP systems are used to understand and process human language, and they can be used for a variety of tasks, such as machine translation, text summarization, and question answering.
NLG (Natural Language Generation) is a field of computer science that deals with the generation of human language. NLG systems are used to create text, speech, and other forms of human language output. They can be used for a variety of tasks, such as writing news articles, generating marketing copy, and creating chatbots.
Generative AI can be used to improve the performance of NLP and NLG systems in a number of ways. For example, generative AI can be used to create more realistic training data for NLP systems. This can help NLP systems to learn to better understand and process human language. Generative AI can also be used to generate more creative and engaging output for NLG systems. This can help NLG systems to create content that is more appealing to humans.
Here are some specific examples of how generative AI is being used to improve NLP and NLG systems:
Generative AI is a promising new technology that has the potential to revolutionize the way we interact with computers. By combining generative AI with NLP and NLG, we can create systems that can understand and process human language more effectively, and that can generate more creative and engaging content.
Today, NLG is used in a wide variety of applications, including:
Content Creation: Generative AI can be used to automatically generate content for journalism, advertising, and marketing
Image Generation: Generative AI can generate images from scratch or modify existing images. Think Dalle 2
Music Generation: Generative AI can create new music compositions or assist musicians in generating melodies, harmonies, and rhythms.
Video Game Design: Generative AI can generate game levels, characters, and game assets,.
Personalized Recommendations: Generative AI can analyze user data, preferences, and behavior to generate personalized recommendations for products, services, movies, music, and more. This can be used in recommendation systems for e-commerce, streaming platforms, and online advertising.
Virtual Assistants and Chatbots: Generative AI can power virtual assistants and chatbots to generate human-like responses to user queries, engage in conversations, and provide information or assistance in various domains, such as customer support, healthcare, and education.
Data Augmentation: Generative AI can generate synthetic data to augment limited or insufficient data for training machine learning models. This can help improve the performance and robustness of AI models, especially in scenarios where obtaining large amounts of real-world data is challenging.
Drug Discovery: Generative AI can assist in designing and generating novel drug molecules with desired properties, potentially speeding up the drug discovery process and aiding in the development of new medicines.
Anomaly Detection: Generative AI models can be trained on normal or legitimate transaction data to learn the patterns of normal behavior. Once trained, these models can identify anomalies or deviations from the learned patterns, which may indicate fraudulent activities.
For example, a generative AI model can learn the patterns of legitimate credit card transactions and flag transactions that deviate significantly from those patterns, such as unusually large transactions, transactions from unknown or suspicious locations, or transactions that occur at abnormal times.
Fraud Alert Generation: Generative AI can be used to generate fraud alerts or notifications that provide real-time feedback to users or fraud detection teams. These alerts can be generated based on the analysis of multiple data sources, such as transaction data, user behavior data, device information, and more, to identify suspicious activities and trigger alerts for further investigation.
Adaptive Fraud Detection: Generative AI can continuously learn and adapt to changing fraud patterns and techniques by analyzing new data and updating the fraud detection models in real-time. This allows for proactive detection and mitigation of emerging fraud patterns, making fraud detection systems more resilient and effective.
When we look at Fraud detection in Trading we may imply Trading Surveillance, where data led models are already employed by nearly all major banks and exchanges
Data Augmentation: Generative AI can generate synthetic data to augment limited or incomplete credit data for training credit scoring models. This can help banks overcome the challenges of limited data availability, especially for borrowers with thin credit files or in emerging markets, and improve the accuracy and robustness of credit scoring models.
Feature Engineering: Generative AI can automatically generate relevant features or variables that can be used in credit scoring models. For example, generative AI can generate synthetic features that capture borrower behavior, transaction patterns, or other relevant information that can enhance the predictive power of credit scoring models.
Fraud Detection: Generative AI can be used to detect fraud or misrepresentation in credit applications. By generating synthetic data that represents potential fraud patterns, generative AI can help banks identify suspicious applications that may have been crafted to deceive credit scoring models and mitigate the risk of granting credit to fraudulent borrowers.
Credit Risk Assessment: Generative AI can be used to simulate different scenarios and assess credit risk. For example, generative AI can generate synthetic scenarios that represent economic downturns, changes in interest rates, or other macroeconomic factors to assess the resilience of credit portfolios to different risk scenarios and make informed credit decisions.
Personalized Credit Offers: Generative AI can be used to generate personalized credit offers for individual borrowers. By analyzing borrower data, financial history, and credit preferences, generative AI can generate tailored credit offers that align with borrowers’ needs, risk profiles, and creditworthiness, improving customer experience and increasing credit approval rates.
Explainable Credit Decisions: Generative AI can generate explanations or justifications for credit decisions, making credit scoring models more transparent and interpretable. This can help banks comply with regulatory requirements and provide borrowers with understandable explanations for credit approval or denial, increasing transparency and trust in the credit scoring process.
Credit Portfolio Optimization: Generative AI can be used to optimize credit portfolio management by generating synthetic scenarios and simulating portfolio performance under different conditions. This can help banks optimize credit risk, diversify their portfolios, and make data-driven decisions for credit risk mitigation and portfolio management strategies.
Generative AI can enhance the accuracy, transparency, and efficiency of credit scoring processes for banks, enabling them to make more informed credit decisions, mitigate credit risk, and provide personalized credit offers to borrowers. However, it’s important to note that the use of generative AI in credit scoring should comply with regulatory requirements, ensure data privacy and security, and be thoroughly validated to ensure the reliability and fairness of credit decisions.
Market Data Generation: Generative AI can generate synthetic market data, such as synthetic price and volume data, order book data, or trade data.
Trading Signal Generation: Generative AI can analyze vast amounts of market data, news, social media, and other relevant information to generate trading signals.
Algorithmic Trading: Generative AI can be used to develop and optimize algorithmic trading strategies. By analyzing historical market data, generative AI models can learn patterns and relationships in the data to create trading algorithms that automatically execute trades based on predefined rules or conditions.
Risk Management: Generative AI can be used to assess and mitigate trading risks. For example, generative AI models can simulate various risk scenarios, such as market crashes, extreme price movements, or liquidity shocks.
Portfolio Optimization: Generative AI can be used to optimize trading portfolios by generating synthetic scenarios and simulating portfolio performance under different market conditions.
High-Frequency Trading: Generative AI can be used in high-frequency trading (HFT) to analyze vast amounts of market data in real-time and generate trading signals or execute trades with low latency
Sentiment Analysis: Generative AI can be used to analyze market sentiment from various sources, such as news, social media, or financial reports, to generate trading signals based on the sentiment analysis.
The risks are also there such as over fitting, models need to be retrained on new market dynamics, potential for herd mentality and super crashes. In the end all automated models need human oversight.
Bloomberg has created a new finance chatbot called BloombergGPT, which utilizes its vast proprietary and curated financial datasets to provide unprecedented financial research and analysis capabilities.
It’s the next step in Corporate AI, where you take generic Generative AI and layer in your own proprietary data. Think all of the data Pharmaceuticals, Governments, Armed forces etc
The training of the BloombergGPT model required approximately 53 days of computations run on 64 servers with NVIDIA GPUs, and the cost estimation to produce the model alone is estimated to be over $2.7 million.
The source datasets used for training include financial news, company financial filings, press releases, Bloomberg News content, as well as general and common datasets like The Pile, The Colossal Clean Crawled Corpus, and Wikipedia.
BloombergGPT has led to create some unique outputs, such as generating initial drafts of Securities and Exchange Commission filings, summarizing financial content into headlines, and reducing the cost of filing.
In conclusion, while Generative AI has the potential to bring significant benefits to the financial services industry, it also raises ethical concerns related to bias, transparency, data privacy, fraud, job displacement, regulatory compliance, and ethical decision-making. It is crucial to adopt responsible and ethical practices in the development, deployment, and use of Generative AI in finance to ensure that it is used in a fair, transparent, and accountable manner, and to mitigate potential risks and challenges.