What is 'Predictive Modeling'

Predictive modeling is the process of using known results to create, process and validate a model that can be used to forecast future outcomes. It is a tool used in predictive analytics, a data mining technique that attempts to answer the question “what might possibly happen in the future?”

BREAKING DOWN 'Predictive Modeling'

The rapid migration to digital products has created a sea of data that is easily available and accessible for businesses. Big data is utilized by companies to improve the dynamics of the customer-to-business relationship. This vast amount of real-time data is gotten from sources like social media, internet browsing history, cell phone data, and cloud computing platforms. By analyzing historical events, there is a probability that a business might be able to predict what would happen in the future and plan accordingly. However, this data is usually unstructured and too complex for humans to analyze in a short period of time. Due to the complexity that enormous amounts of data present, companies are increasingly using predictive analytics tools to forecast the outcome of an event likely to happen in the near future.

How Predictive Analytics Works

Predictive analytics collects and processes historical data in huge amounts and uses powerful computers to assess what happened in the past, and then provides an assessment of what will happen in the future. Predictive analytics uses predictors or known features to create predictive models that will be used in obtaining an output. A predictive model is able to learn how different points of data connect with each other. Two of the most widely used predictive modeling techniques are regression and neural networks.

Predictive Modeling: Regression

In the field of statistics, regression refers to a linear relationship between the input and output variables. A predictive model with a linear function requires one predictor or feature in order to predict the output/outcome. For example, a bank that hopes to detect money laundering in its early stages might incorporate a linear predictive model. The bank specifically wants to know which of its customers are likely to engage in money laundering activities at some point in time. All the bank’s customers’ data are presented, and a predictive model is built around the dollar value of transfers each customer made during a period of time. The model is taught to recognize the difference between a money laundering transaction and a normal transaction. The optimal outcome from the model should be a pattern that signals which customer laundered money and which didn’t. If the model perceives that a pattern of fraud is emerging for a particular customer, it will create a signal for action which will be attended to by the bank’s fraud analysts.

Predictive Modeling: Neural Networks

Predictive models are also used in neural networks such as machine learning and deep learning, which are fields in artificial intelligence (AI). The neural networks are inspired by the human brain and are created with a web of interconnected nodes in hierarchical levels which represents the foundation for AI. The power of neural networks lies in their ability to handle non-linear data relationships. They are able to create relationships and patterns between variables that would prove impossible or too time consuming for human analysts. So while a bank can input known variables such as value of transfers initiated by its customers into its model in order to obtain a desired outcome of who is likely to engage in money laundering, a neural network can create a more powerful pattern if it can successfully create a relationship between input variables like time logged in, geographic location of the user, IP address of the user’s device, recipient or sender of the funds, and any other feature that is likely to make up a laundering activity.

Other Types of Predictive Modeling

Other predictive modeling techniques used by financial companies include decision trees, time series data mining, and Bayesian analysis. Companies that take advantage of big data through predictive modeling measures are better able to understand how their customers engage with their products and can identify potential risks and opportunities for a company.

  1. Neural Network

    Neural network is a series of algorithms that seek to identify ...
  2. Fed Model

    The Fed model is a tool used to determine whether the U.S. stock ...
  3. Financial Modeling

    The process by which a firm constructs a financial representation ...
  4. Machine Learning

    Machine learning is the idea that a computer program can adapt ...
  5. Model Risk

    Model risk occurs when a financial model used to measure a firm's ...
  6. Regression

    A statistical measure that attempts to determine the strength ...
Related Articles
  1. Trading

    Technical Analysis Works In Forex Markets

    Technical analysis is a hotly debated topic. Discover evidence showing that it works in forex markets.
  2. Investing

    Google: AI Beats Hospitals at Patient Predictions

    Google claims that its artificial intelligence can better assess risks regarding patient outcomes.
  3. Trading

    Build a Profitable Trading Model In 7 Easy Steps

    Trading models can provide a powerful tool for building profit. Traders can use and customize existing trading models or build an original model. This article provides seven steps to building ...
  4. Investing

    Financial Models You Can Create With Excel

    The relatively modest amount of time it takes to build these models can pay for itself by leading you to better investment decisions.
  5. Investing

    Regression Basics For Business Analysis

    Regression analysis is a quantitative tool that is easy to use and can provide valuable information on financial analysis and forecasting. Find out how.
  6. Personal Finance

    Discounted cash flows or comparables: Which to use

    DCF and comparables models are widely used in equity valuation, and here we'll explain the pros and cons of each method.
  7. Investing

    What's the Gordon Growth Model?

    The Gordon growth model is used to calculate the intrinsic value of a stock today, based on the stock’s expected future dividends. It is widely used by investors and analysts to compare the predicted ...
  8. Investing

    How Google, Facebook, Amazon Will Ride The AI Wave

    No Brainer: These 9 major stocks are poised to outperform over the next decade as they lead the AI revolution
  9. Investing

    What's Anti-Money Laundering?

    Anti-money laundering involves the laws and regulations designed to prevent criminals from generating income through illegal activities.
  1. What methods are used to launder money?

    Learn about the methods that criminals use when they are looking to launder money. Many different methods are used, and they ... Read Answer >>
  2. How can I create a linear regression in Excel?

    Learn the steps involved in creating a linear regression chart in Microsoft Excel. A linear regression is a data plot that ... Read Answer >>
  3. How is sensitivity analysis used?

    Sensitivity analysis is used to identify how much variations in the input values for a given variable will impact the results ... Read Answer >>
  4. If caught, what implications does money laundering have on a business?

    Understand the damaging effects of money-laundering on businesses as well as anti-laundering measures businesses can use ... Read Answer >>
  5. Is there an easy way to do financial forecasting in Excel?

    Find out how to use Excel to conduct financial forecasting. Learn how to build a complex financial model, and discover functions ... Read Answer >>
  6. Why does fighting money laundering reduce overall crime?

    Fighting money laundering reduces overall crime by helping identify perpetrators, restoring stolen money to victims and disrupting ... Read Answer >>
Trading Center