Using account level credit data from, Terrene can apply machine learning models to combined consumer-tradeline, credit-bureau, and macroeconomic variables to predict delinquency. On top predicting delinquency, Terrene models can be used to analyze risk managements practices and identify drivers of delinquency.

Initial Risk Profile Calculated by Terrene

After training a machine learning model, Terrene will then use the trained model to construct a probability distribution for every applicant predicting how likely they are in not defaulting on the credit issued to them. This is calculated by looking at characteristics of the applicant's existing accounts such as the current balance, utilization rate, and purchase volume; individual-borrower characteristics from a large credit bureau such as the number of accounts an individual has outstanding, the number of other accounts that are delinquent, and the credit score; and macroeconomic variables including home prices, income, and unemployment statistics.

For instance, in the example above Terrene has calculated a hypothetical customer will have a 90% to 98% of successfully repaying their loan without defaulting based on all the data on-hand about them.

Terrene Can Help You Detect "Run-Up" Accounts More Accurately Using Machine Learning

Compared to other retail loans such as mortgages, lenders and investors have more options to actively monitor and manage credit-card accounts because they are revolving credit lines. Consequently, managing credit-card portfolios is a potential source of significant value. Better risk management could provide you with savings on the order of hundreds of millions of dollars annually. For example, you can cut or freeze credit lines on accounts that are likely to go into default, thereby reducing your exposure.

By doing so, you can potentially avoid an increase in the balances of accounts destined to default, known in the industry as “run-up.” However, by cutting these credit lines to reduce run-up, you also run the risk of cutting the credit limits of accounts that will not default, thereby alienating customers and potentially forgoing profitable lending opportunities. More accurate forecasts of delinquencies and defaults reduce the likelihood of such false positives.

Terrene can automatically update risk profiles based on real-time events

Terrene will automatically update its risk assessment on every account when certain events such as mortgage payments, credit card payments, etc. happen. Doing so will allow you to quickly and more accurately find accounts with a high risk of defaulting.

Terrene models flagged an account with high probability of defaulting after a missed credit card payment at another institution

Terrene can also automatically flag accounts that their risk of defaulting has decreased in order to potentially offer them more credit.

Terrene models flagged an account with decreasing probability of defaulting after a series of on-time payments


What is Machine Learning?

Machine learning is a technique of getting computers to learn and act as humans do. Instead of specifically implementing algorithms to perform certain tasks, machine learning takes in a set of inputs and the desired output then learns how to produce the desired output without explicitly being given instructions.

Machine learning algorithms are used every day to in medical diagnosis, stock trading, energy load forecasting, and more. For example, media sites such as Netflix use it to give you movie recommendations and e-commerce websites such as Amazon use it to display products to you that you are more likely to purchase.

When Should You Use Machine Learning?

Machine learning works best when you have a complex task or problem involving a large amount of data and lots of variables, but no existing formula or equation. For example, machine learning is a good option if you need to calculate risk of credit applicants to your institution defaulting on their credit.

How Machine Learning Works?

Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.

In the case of financial risk assessment, supervised machine learning would work the best because we would know the outcome (i.e. wether or not borrowers defaulted). A supervised learning algorithm takes a known set of input data and known outcomes to the data (output) and trains a model to generate reasonable predictions for the response to new data.

How Do You Decide Which Machine Learning Algorithm to Use?

There are many variables that go into selecting the best machine learning algorithm for the task on hand. Some of these variables are the size and type of data you're working with, the insights you want to get from the data, and how those insights will be used.

There is no best method or one size fits all and choosing the right algorithm can be overwhelming. There are dozens of supervised and unsupervised options to choose from, and each takes a different approach to learning.

This is where Terrene comes in. Terrene can quickly and automatically train multiple machine learning models without any data science knowledge to fit your data and the outcome you are looking for. Terrene will then automatically pick the model that performs the best after running some tests on them.

How Does Machine Learning Work With Terrene?

Terrene makes machine learning easy. Terrene offer tools for reading and combining data sets from nearly any source, automatically training machine learning models, and visualizations tools to display the results. Terrene allows your organizations to better forecast demand with minimal change to your current process.

Terrene lets you:

  • Read and combine data from multiple sources
  • Build optimal machine learning models with the click of a button
  • Integrate machine learning models into your workflows and generate predictions in real-time