How To Use ML for Credit Scoring & Decisioning

Credit scoring and decisioning models have been used by financial institutions for many years to predict the risk associated with lending to individuals or entities. However, these models are evolving, with machine learning now playing an essential role in refining and improving the accuracy and efficiency of credit scoring and decisioning. 

This article will explore credit scoring and decision models in greater detail, with an emphasis on the machine learning aspects and how data platforms like the Snowflake Data Cloud can be utilized to maximize effectiveness.

What are Credit Scoring and Decisioning Models?

Credit Scoring

Credit scoring is a statistical analysis performed by financial lenders to determine the creditworthiness of an individual or a business. This creditworthiness is influenced by several key factors:

  1. Credit History: The primary source of information is usually the applicant’s credit history, which is a detailed record of all past borrowing and repayment, including late payments and defaults. Credit bureaus compile this data and generate credit reports.
  2. Personal Information: Personal data, such as income level, employment status, and length of credit history, are vital pieces of information. This information is often self-reported by the applicants on their credit application.
  3. Other Data Sources: In recent years, alternative data has become increasingly important. This could include utility bills, rent payments, bank account information, and even social media activity. All these sources provide a more holistic view of the applicant’s financial behavior.


Based on these data points, models generate a credit score. This credit score is then used to decide whether to extend credit to a borrower and at what interest rate. Credit scores typically range from 300 to 850, with higher scores indicating less risk to the lender.

Credit Decisioning

Credit decisioning, on the other hand, is the process by which financial institutions make decisions on credit applications. It involves evaluating the borrower’s creditworthiness and the likely profitability of the loan to the lender. This margin may differ based on the firm’s risk appetite and lending strategy.

For example, a lender specializing in subprime loans might accept higher default probabilities than a traditional bank. Additionally, firms must comply with federal and state financial regulations.

In another example, lenders cannot discriminate based on age, race, religion, nationality, or marital status, and such factors must be handled appropriately by the model. Ultimately, the decisioning process may result in acceptance, rejection, or a request for additional information.

How do These Models Add Business Value?

Accelerating Loan Approvals

In a competitive market, speed matters. Customers are increasingly seeking instant decisions on their credit applications, and the status quo of manual underwriting is simply too slow for today’s consumer.

Automated credit-decisioning models meet this demand and can drastically reduce the time required to make decisions, allowing lenders to provide instant loan approvals. This enhances customer satisfaction and allows lenders to process a larger volume of applications, boosting their market share.

Enhancing Risk Management

Effective risk management is at the heart of any lending business. Lenders need to be able to accurately assess the risk associated with each credit application.

This is where credit-decisioning models are essential. With a modeled estimation of the applicant’s credit risk, lenders can make more informed decisions and reduce the occurrence of bad loans, thereby protecting their bottom line.

Figure 1: A simplified example of a decision tree model used for risk assessment

Expanding Credit Access

Traditionally, the primary data used for credit decisions was limited to credit history and income level. However, this approach often excluded individuals with limited credit history or unconventional income sources.

Advanced credit-decisioning models can use alternative data sources, like utility bill payments or rent payment history, to enable lenders to assess the creditworthiness of these ‘thin-file’ or ‘no-file’ individuals. This allows lenders to tap into new customer segments and expand their business.

Optimizing Loan Pricing

Credit-decisioning models can help lenders set loan prices that align with the associated risk. By determining the probability of default for each applicant, lenders can adjust the loan interest rates and terms accordingly. This risk-based pricing strategy can optimize the lender’s revenue and also ensure that customers are offered prices commensurate with their credit risk.

How Does Machine Learning Impact These Models?

The above business cases are made possible today by utilizing machine learning within the models themselves. Below are just some of the advantages provided by incorporating machine learning in credit models.

Greater Accuracy

Machine learning models can handle high-dimensional, nonlinear, and interactive relationships between variables. They can leverage a large number of factors to improve the prediction of risk. These nuanced algorithms can lead to more accurate and reliable credit scores and decisions.

Enhanced Efficiency

Machine learning algorithms can automate the credit scoring and decisioning processes, making models faster and more efficient. They can process large amounts of data in real time, providing instant credit scores and decisions. This can significantly reduce the time taken for loan approvals and increase customer satisfaction.

Reduction in Bias

Machine Learning can help to reduce bias in credit scoring and decisioning. They can be trained to ignore variables that might lead to discriminatory practices, such as race, gender, and age. This can lead to fairer and more equitable credit decisions.

What Does a Credit Score or Decisioning ML Pipeline Look Like?

Now that we have a firm grasp on the underlying business case, we will now define a machine learning pipeline in the context of credit models. Machine learning in credit scoring and decisioning typically involves supervised learning, a type of machine learning where the model learns from labeled data. 

In the context of credit scoring, the label could be whether a borrower defaulted on a loan or did not default. The model learns from these labels to predict the outcome of new, unseen data.

Various machine learning algorithms can be used for credit scoring and decisioning, including logistic regression, decision trees, random forests, support vector machines, and neural networks. More recently, ensemble methods and deep learning models are being explored for their ability to handle high-dimensional data and capture complex patterns.

Data Preparation

The first step in the process is data collection and preparation. This involves gathering data from various sources such as credit bureaus, financial statements, and loan applications. The data then needs to be cleaned, normalized and transformed into a format that can be used by the machine learning algorithm.

Feature Selection

Next, relevant features (or variables) need to be selected for the model. These could include credit history, debt ratio, income, and employment status. Feature selection is crucial as it directly impacts the model’s performance. Using irrelevant features can lead to overfitting, where the model performs well on the training data but poorly on new data.

Model Training and Validation

The prepared data is then split into a training set and a validation set. The machine learning algorithm uses the training set to learn the relationship between the features and the outcome (i.e., loan default or not). The validation set is used to test the model’s performance and tune its parameters.

Model Deployment and Monitoring

Once the model has been trained and validated, it can be deployed in the credit scoring and decisioning system. It’s important to continually monitor the model’s performance to ensure it’s making accurate predictions. If the model’s performance starts to degrade, it may need to be retrained with new data.

Snowflake for Credit Scoring and Decisioning Systems

When building a tool like a credit model, we need to select a cloud platform that meets the needs of a machine learning solution. One such provider is Snowflake

Snowflake is a cloud-based data warehousing platform that provides secure and easy access to any data with near-infinite scalability. It can be a powerful tool for machine learning in credit scoring and decisioning.

With Snowflake, you can store and manage all your data in one place, making it easier to collect, clean, and prepare data for machine learning. You can also easily scale up or down based on your data volume and computational needs, making it a cost-effective solution.

Furthermore, Snowflake provides native support for machine learning algorithms and tools. You can build, train, and validate machine learning models directly in Snowflake using the Snowpark SDK for Java, Python, or Scala with incredible performance

Let’s take a brief look at the below image to see how Snowpark can be used for an end-to-end machine learning solution.

Figure 2: Standard ML Pipeline on the Snowflake Platform

First, we utilize Dataframes to shape our data in preparation for training. Then we take that conformed data and utilize a Stored Procedure to train our model. Last, we create a User Defined Function with which to use that trained model for drawing inferences. 

Naturally, this is an all-Snowflake solution, but if you have another tooling in mind, Snowflake partners with other popular cloud platforms for seamless integration.

Snowpark Credit Decisioning Tutorial

The engineering team at Snowflake has provided a practical example for us to look at: credit card approvals. Say we are part of an ML team working on a decisioning model. We want our model to approve or reject the issuing of a credit card as a fast and accurate service for our customers.

We will be using Snowpark to ingest, analyze, and transform our data on customer demographics and credit history to train a model that will then be deployed inside Snowflake to evaluate new credit card requests.

This link will redirect you to the Snowflake quickstart tutorials website. Please expect the tutorial to take roughly 1.52 hours to completion.

Closing Thoughts

Congrats! You now understand the core workings of credit decision models and built your very own PoC in Snowflake! 

Want to learn more? Reach out to phData today and see how we can help your business grow with our machine learning and Snowflake consulting services.

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