Deep Learning and AI

AI & LLMs in Finance & Payment

June 6, 2024 • 8 min read


Large Language Models and Their Disruptive Potential

The finance industry is often overwhelmed with data — comprised of filings, reports, and contracts, not to mention the need for intense and careful observation/scrutiny. When dealing with money, the finance industry can’t afford to make mistakes.

The recent adoption of utilizing Large Language Models (LLMs), is a game-changer. LLMs can ingest and write large amounts of text extremely quickly, automate tedious tasks, and summarize information into easy-to-digest formats. Tasks like detecting fraud, running anomaly analysis, and forecasting can be accomplished utilizing AI and machine learning methods.

Applications of AI in the Financial Sector

Let's dive into the possible applications of AI in finance, from combating fraud to providing advanced financial guidance. We’ll explore how these technologies drive innovation and efficiency in the financial industry. While AI and LLMs are trained on real data, take necessary safety precautions when trusting an AI output. Quality assurance, skepticism, and fact-checking are encouraged when utilizing any LLM or AI tool.

1. Fraud Detection and Prevention

By analyzing large volumes of data in real time, machine-learning AI models can assist in quickly detecting potential fraudulent activities that might otherwise go unnoticed. These AI models are trained on historic fraudulent data and authentic examples and through pattern recognition learn to classify data accordingly certain transaction patterns into categories.

Claim insurance processing for property and casualty involves complex assessments of damage. This consists of deciding whether insurance is valid and assigning a cost, this can be time-consuming and prone to errors. While the job is quite subjective and should be done by the insurance claims professional, LLMs can help by processing the reports with descriptions of the damage, writing comprehensive.

Combining LLMs with other AI systems that can also process images of the incidents, the insurance claim process can be further automated or at least sped up with automated cost assessment. This reduces costs, saves time, and can even improve customer satisfaction but also helps identify potential fraud more easily, ensuring the integrity of claims and payments.

2. Risk Assessment and Management

LLMs can play a crucial role in risk assessment and management for financial institutions. By analyzing diverse data sources and incorporating advanced machine learning algorithms, LLMs enable more informed decision-making, minimizing potential risks.

For instance, real-time risk management is an opportunity. Sometimes an organization needs to evaluate risk before making a decision based on customer data, industry data, and real-time updates - all of which are constantly changing. An AI model could be trained to assess certain decisions based on the given parameters and expected outcomes, thus, conducting better risk assessments with greater accuracy and impact. An LLM can help deliver those findings in digestible language.

3. Advanced Financial Advice

AI and LLMs are transforming how financial advisors provide personalized financial guidance to their clients. By leveraging the capabilities of complex AI algorithms, advisors can offer tailored recommendations for investments, retirement planning, and other financial decisions.

Predictive trading algorithms perfectly demonstrate this application. In trading, identifying valuable trades requires analyzing vast amounts of data in real-time. LLMs can assist in that by accelerating market analysis and machine learning models can provide accurate predictions. By enhancing analyst tools, organizations can accomplish more profitable trades, drive revenue and mitigate risks, enable financial advisors to offer high-quality and customized advice on demand, and enable clients to make informed decisions about their finances.

LLMs are also commonly used in various other applications not related to financial companies. For example, in chatbots that help enhance customer service or new customer onboarding by guiding them through account setup, check balances, or being a personal bank teller. Since LLMs can understand language, their capabilities span farther than a decision tree-type chatbot.

LLMs in the Finance Industry

All previously discussed applications mention the need for a well-trained model. This is where open-source LLMs prove to be the accessible, adaptable solution. 

Released by Bloomberg on March 30th, 2023, BloombergGPT addresses the escalating complexity and volume of financial data, a groundbreaking development in financial AI technology. It is trained on domain-specific knowledge specializing in financial-related tasks and the ability to generate BQL queries through a prompt. It also improves tasks like sentiment analysis, entity recognition, data searching, analysis, report creation, and insight generation.

FinGPT is also an open-source LLM tailored specifically for the finance industry released in June 2023, with a focus on promoting data accessibility and open finance practices. FinGPT aims to democratize access to financial data and LLMs, sparking innovation within the finance sector. FinGPT is capable of performing financial sentiment analysis, being a robo-financial advisor, quantitative trading, and more. FinGPT continually adds new

Companies Using AI for Payment Fraud Detection


PayPal, with its colossal volume of transactions—$235 billion in payments last year from four billion transactions. With a large presence, Paypal, and other payment companies face perpetual cybersecurity challenges. To combat this, PayPal has turned to deep learning and machine learning to bolster its security measures. By analyzing vast amounts of data, including customer purchasing history and fraud patterns, PayPal's AI algorithms can identify potential fraud with remarkable accuracy. 

For example, suppose a U.S. user's account is accessed from multiple countries within a short time frame. In that case, PayPal's system can discern whether the transactions are legitimate or fraudulent, considering factors such as the user's travel history and purchasing behavior. By continually mining data and generating thousands of 'features' or rules in real time, PayPal can detect suspicious patterns.

This intensive, real-time analysis enables PayPal to maintain a remarkably low transaction-to-revenue of 0.17% of revenue versus the industry average of approximately 1.82%. PayPal's success highlights the critical role of integrating deep learning algorithms in conjunction with rule-based algorithms in combating cybersecurity threats, with similar approaches being adopted by other companies like startup Deep Instinct and security firm FireEye.


Mastercard, one of the world's leading payment giants, has developed its own proprietary generative AI model to combat fraudulent transactions within its extensive network of banks. This new AI model, known as Decision Intelligence, enhances Mastercard’s real-time fraud detection capabilities across its network. 

Built from scratch by the company's cybersecurity and anti-fraud teams, Decision Intelligence utilizes a recurrent neural network trained on data from the 125 billion transactions processed annually enabling it to understand transactional relationships and predict fraudulent activities. Similar to Paypal, Mastercard uses the history of transaction patterns to determine legitimate transactions as well as the likelihood of fraud within just 50 milliseconds. 

This innovative technology has led to significant improvements in fraud detection rates of about 20% increase, with some financial institutions seeing up to a 300% increase. Mastercard is leading the way in using AI to enhance security and efficiency in the payments industry investing over 7 billion dollars in cybersecurity and AI. By harnessing the power of its vast transaction data, Mastercard's AI algorithm detects fraud and predicts future fraud trends, offering unparalleled protection across its global ecosystem.


In conclusion, large language models (LLMs) are poised to revolutionize the finance industry, offering transformative solutions in fraud detection, risk assessment, financial advice, and more. From the groundbreaking capabilities of models like BloombergGPT and FinGPT to the real-world applications showcased by companies like PayPal and Mastercard, it's evident that LLMs are reshaping how financial institutions operate and innovate.


deep learning and ai



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