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AI in Finance: How Generative AI and Large Language Models Can Be Applied to Financial Services Workflows

Snowflake Snowday 2021

Today’s banks, investment firms, and financial services companies have an extraordinary opportunity to improve profitability and reduce risk through the use of artificial intelligence. Faster fraud detection, more responsive customer service, and improved regulatory compliance are just a few of the benefits of using AI in finance. In this article, we’ll examine the cornerstone AI technologies powering this wave of innovation along with seven use cases that illustrate the remarkable ways in which artificial intelligence is advancing the finance industry.

Types of AI in Finance

AI is a broad category with multiple branches, each with its own unique functionalities and applications. Here are four types of artificial intelligence commonly used for finance applications.

Natural Language Processing (NLP)

NLP is a kind of artificial intelligence that allows computers to understand and process human language. NLP is used to analyze financial news, research reports, and social media sentiment at scale, enabling more informed decision-making. It’s also an essential component of automated chatbots that help customers quickly access information and solve problems.

Deep learning 

Deep learning is a subset of machine learning that uses multi-layer neural networks to mirror the way the human brain learns new information. This type of AI is useful for processing and analyzing complex financial data. Deep learning helps financial institutions predict likely outcomes such as currency exchange rates or stock market performance and extract insights from unstructured data such as documents, emails, and other text-based sources. 

Large Language Models (LLMs)

A large language model is an advanced form of AI designed to understand the intricacies of human language and produce creative, intelligent responses when queried. Successful LLMs are trained on enormous data sets typically measured in petabytes. This training data comes from a range of text-based sources. Using deep learning techniques, these models excel at understanding and generating text similar to human-produced content that can assist in financial analysis. 

Sentiment analysis

Sentiment analysis uses AI techniques to extract meaning from written text. This technology provides financial institutions with a tool for analyzing, understanding, and categorizing emotions and opinions. Sentiment analysis is commonly used to gather customer feedback, assess brand reputation, and gauge market sentiment.

AI in Finance: 7 Real-World Examples

Let’s look at seven use cases that highlight how AI-enabled technologies are enabling financial companies to thrive in an increasingly competitive industry.

Regulatory compliance 

One of AI’s most useful features is its ability to quickly and efficiently sift through massive quantities of data. Using AI systems, financial institutions can proactively identify potential compliance violations, including fraud and data security breaches.

Risk assessment

AI models are incredibly good at analyzing multi-dimensional relationships between variables, helping them spot difficult-to-detect risks with investments, loans, and insurance policies that manual methods often overlook. AI systems can rapidly analyze historical data and market trends to assess associated risks, enabling financial institutions to make more informed decisions and develop better risk mitigation strategies.

Algorithmic trading

Algorithmic trading provides financial institutions and investors the opportunity to automatically open and close trades using pre-programmed trading instructions for faster, more accurate trades that don’t require direct human intervention. AI has pioneered a new frontier for automated trading with its advanced computational models and machine learning techniques. Not only can AI algorithms process real-time market data, news, and historical trends, but they can also learn and adapt over time, adjusting trading strategies to fit market conditions. AI-powered algorithmic trading allows traders to increase efficiency and minimize human bias.

Credit risk scoring 

Credit scoring is the process financial institutions use to gauge the creditworthiness of a borrower. Using AI, lenders can quickly analyze various data sources, including credit history, financial statements, and social media activity. With its ability to make connections between data, AI algorithms can help lenders make more accurate and efficient credit decisions and offer personalized loan terms.

Advanced robo-advisors

Robo-advisors provide automated investment advice, helping clients manage their portfolios and invest in ways that align with their goals. Advanced AI algorithms can automatically complete tasks that were done by human advisors. These systems can also be used to automate other processes, including portfolio rebalancing and tax loss harvesting. Robo-advisors are predicted to also have a future impact on the wealth and asset management profession.

Fraud detection

AI plays an essential role in financial fraud detection. Using advanced algorithms and machine learning techniques, AI can identify anomalies and patterns in real time, helping financial institutions spot and stop fraudulent transactions. AI tools can also be deployed to uncover sophisticated fraud schemes and money laundering networks. AI-enabled fraud detection tools reduce the rate of false positives, freeing organizations to dedicate their full resources to safeguarding themselves and their customers.

Underwriting and claims processing 

AI models can automate large portions of the loan underwriting process, allowing banks to review documents, assess risks, and make high-quality lending decisions more quickly and efficiently. These systems are also being used by insurers to streamline policy underwriting. By automating data analysis, risk assessment, and policy pricing, insurance companies can offer customers near-instant customized quotes. Using similar technologies, AI algorithms can also expedite claims processing, automatically analyzing claims data to detect potential fraud, extract relevant information from claims documentation, and determine the appropriate payout.

Fast-Track Your AI Initiatives with Snowflake

Snowflake is leading the way in AI-driven innovation, providing financial companies with  a highly capable and scalable data platform and the tools needed to fast-track AI initiatives. With Applica, the latest addition to Snowflake, financial companies can realize the full potential of their unstructured data. 

Applica harnesses the power of deep learning to decode and automate complex documents, recognizing and interpreting information within seconds. It understands information and its context, categorizing it to help financial companies make more informed decisions, eliminate manual processing, and reduce data errors. Applica is already streamlining numerous financial services processes, including mortgage origination, debt collection, invoicing, contract management, and customer care. Unlock the full potential of AI in finance with Snowflake. 

In addition, Snowflake’s acquisition of Streamlit allows financial services app developers to rapidly turn data scripts into shareable web apps, all in Python.

Learn more: Using Snowflake and Generative AI to Rapidly Build Features