Understanding the Difference: Generative AI vs. Predictive Analytics in Corporate Finance

At ChatFin, we have been working to combine the worlds of Generative AI & Predictive analytics.? The combination is going to be ChatFin's superpower. But as we work in trenches, we felt we need to clarify the basic differences and their applications.

While they might sound similar, their applications are totally different from one another Let’s break down what each of these technologies are? and how they differ in their use in corporate finance.

What is Predictive Analytics?

Predictive analytics uses historical data to forecast future outcomes. It involves various statistical techniques, including data mining, machine learning, and big data analytics, to predict future events based on past trends. In? finance, predictive analytics can help forecast sales revenues, understand market trends, and anticipate risk. For example, a financial analyst might use predictive analytics to determine the credit risk of potential borrowers or to predict cash flow fluctuations.

What is Generative AI?

Generative AI, on the other hand, steps into the realm of generation. It uses machine learning models to generate new content —be it text, images, or even code—that has never existed before.? But the greatest power of these models is not just generation but reasoning. In finance, generative AI can be a AI assistant , simulate financial scenarios, create realistic financial models, or generate reports and presentations based on the input data it receives. Its ability to? reason? makes it a powerful tool for innovation and problem-solving.

The greatest power of Gen AI models is not just generation but reasoning.

Key Differences in Application

1. Creation vs. Prediction: The fundamental difference lies in their core functions. Predictive analytics predicts the future based on past data, whereas generative AI creates new data and scenarios based on learned patterns. For instance, while predictive analytics might forecast the next quarter's revenue based on past performance, generative AI could simulate how changing a business model or market conditions could create different revenue outcomes.
2. Scope of Use: Predictive analytics is often more narrowly focused on specific forecasting tasks such as risk assessment or demand forecasting. Generative AI has a broader scope, capable of assisting in creative problem-solving or generating data required for complex decision-making processes.
3. Data Handling: Predictive analytics typically deals with structured data in a linear, straightforward manner. Generative AI, however, can handle both structured and unstructured data, learn from it, and generate outputs that can be remarkably creative and multi-dimensional.

Which Should Your Business Use?

The choice between generative AI and predictive analytics isn’t necessarily an either/or proposition. In fact, they can complement each other beautifully. Predictive analytics is indispensable for businesses that need solid forecasts based on quantitative data. It’s about precision and reliability. On the other hand, generative AI is perfect for scenarios where innovation or comprehensive scenario modeling is required.

In practice, a financial planning team might use predictive analytics to estimate future revenue streams and then employ generative AI to explore strategic changes and their potential impacts under various scenarios. This combination allows for both data-driven forecasting and creative, informed decision-making.