What you'll learn:
- What are the common financial models, modeling processes and use cases
- How generative AI can be used at every step of the modeling process (gathering data, establishing assumptions, building the model, and validating it)
- The various types of models in various activities, such as for valuation, trading, insurance, lending/banking, and others
- How generative AI changes the modeling process and accelerates/augments certain tasks
GENERATIVEAIISCHANGINGTHEMODELING GAME
Generative AI has revolutionized several industries. And financial modeling is no different.
With the capability to summarize data, transform them and process them, validate assumptions, generate scenarios, apply formulas and templates instantly, and more, text generative AIs such as ChatGPT are changing the modeling landscape.
This course will cover how to incorporate generative AI into your financial modeling pipeline, improving it for this new era.
LETMETELL YOU... EVERYTHING.
Some people - including me - love to know what they're getting in a package.
And by this, Imean, EVERYTHING that is in the package.
So, here is a list of everything that this course covers:
You'll learn about the basics of generative AI, including its capabilities, limitations, common models and technology used, and how it accelerates various tasks;
You'll learn about the basics of financial modeling, including the general modeling process with four steps (gathering data, establishing assumptions/constraints, building the model, and validating it/using it);
You'll learn about some common modeling use cases in finance, such as the Discounted Cash Flows analysis for valuation, regression for credit scoring, time series and machine learning for security price prediction, and actuarial/catastrophe models for insurance risk pricing, as well as the usual inputs and assumptions in general;
You'll learn about the main types of financial models:mathematical (where we apply operations to the inputs given), statistical (where we calculate results based on causality, correlation, or other relationships among variables), simulations (where we stochastically simulate various scenarios and gauge variations in outputs due to these), and algorithmic/computational (where we execute a set of steps, in a programmatic manner), as well as how these are used for common use cases such as banking/lending, trading, fraud detection or insurance;
You'll learn about the steps of the modeling process in depth, including what to take into account at each step (when gathering and preparing data, when establishing assumptions and constraints, when building the model itself, and when validating or using the model);
You'll learn about ways in which gen AI can accelerate or augment each of the four main steps of the modeling process (extracting or transforming data when gathering data, double-checking and generating assumptions when establishing assumptions, applying formulas or making calculations when building the model, and validating outputs or generating various scenarios when validating or using the model);
