What is Generative AI in Finance?

Lotta Lundaas

Lotta Lundaas

VP of Marketing

We look at what generative AI is and how it is revolutionizing finance.

June 14, 2023

min read

What is Generative AI in Finance?

In today's rapidly evolving financial landscape, staying ahead of the curve is crucial, and generative AI is a game-changer that brings innovative solutions to complex financial challenges. From predictive analytics to risk management, finance and accounting teams increasingly turn to generative AI to unlock new opportunities, streamline processes, and gain a competitive edge. But what exactly is generative AI, and how does it revolutionize finance?

Introduction to generative AI

Generative artificial intelligence (AI) refers to a subset of machine learning techniques where algorithms learn to generate new data based on the input data they are trained on. Unlike traditional AI models that are trained to classify or predict based on existing data, generative AI models can create entirely new data samples. These models are trained on large datasets and learn the underlying patterns and structures within the data to generate new, original outputs.

Video: What is generative AI in the context of finance?

In this “What is Generative AI in the Context of Finance?” episode of’s Intellibytes video series, we cover the basic concepts and principles of generative AI, including how generative AI systems are designed to mimic and generate human-like outputs and how it can assist in tasks such as financial modeling, risk analysis, fraud detection, and investment strategies.

How is generative AI used in finance?

In finance and accounting, generative AI holds immense promise for the following business functions and use cases.

Risk assessment and management: Generative AI models can analyze historical market data to simulate various scenarios and assess potential risks. These models can augment existing datasets by generating synthetic data, enabling more accurate risk assessment and proactive risk management strategies.

Portfolio optimization: For banks and traditional financial institutions, generative AI can optimize investment portfolios by generating diverse asset allocations based on historical performance data and market trends. This helps in minimizing risk while maximizing returns.

Algorithmic trading: Generative AI algorithms can analyze market data and generate trading strategies based on historical patterns and market conditions. These strategies can be continuously refined and adapted to changing market dynamics, leading to more profitable trading decisions.

Fraud detection: Generative AI can generate synthetic datasets that mimic fraudulent activities, allowing finance and accounting teams to train more robust fraud detection models. These models can better identify and prevent fraudulent transactions in real-time by simulating various fraud scenarios.

Credit scoring: Generative AI models can analyze vast amounts of customer data to generate synthetic profiles, enabling more accurate credit scoring and risk assessment for loan approvals.

Customer segmentation: Financial institutions use generative AI to segment customers based on their financial behavior and preferences, allowing targeted marketing campaigns and personalized services.

Market forecasting: Generative AI algorithms can generate synthetic market data to forecast trends, predict asset prices, and inform investment decisions.

Regulatory compliance: Generative AI helps financial institutions comply with regulatory requirements by generating synthetic datasets for testing and validating compliance systems without exposing sensitive customer information.

Enhancing financial decision-making: Generative AI empowers financial professionals to make more informed decisions by surfacing critical data and insights.

Automated reporting and analysis: Generative AI automates tedious reporting and analysis tasks, reducing time-consuming data analysis and modeling and allowing team members to focus on future forecasting and strategic decision-making based on available reports and analysis.

How to evaluate generative AI solutions for finance

For financial institutions looking to harness the power of generative AI, here are some steps to get started:

1. Define objectives: Identify specific use cases and business objectives where generative AI can add value, such as risk management, fraud detection, or customer segmentation.

2. Data preparation: Gather and preprocess relevant datasets, ensuring they are clean, labeled, and representative of the problem domain.

3. Software selection: Explore different generative AI solution providers designed for your business and work. Many AI-first technology solutions for accounting have been explicitly designed to address traditional pain points and challenges. is one of those technology providers. uses proprietary AI technology to power its autonomous finance platform for end-to-end invoice processing and bill pay.

4. Pilot and integrate: Pilot new software or AI algorithms to see how they adapt and work for your business. This can mean starting with one task or function, evaluating performance against predefined metrics and benchmarks, and then expanding to other existing workflows, processes, and systems, ensuring seamless deployment and scalability.

Overall, generative AI represents a paradigm shift in how finance and accounting teams analyze data and make strategic decisions. By harnessing the power of generative AI, financial institutions can unlock new opportunities, mitigate risks, and deliver more personalized services to their customers, ultimately driving business growth and competitive advantage in an increasingly complex and dynamic market landscape.

This article originally published June 2023, and was updated April 2024.

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