How to Build a Credit Scoring Software Solution in 2023?

Blog Summary: Credit scoring software solutions can analyze large amounts of data, including non-traditional credit data, such as social media activity, to make more informed credit decisions. In this blog, we will walk you through -

• What is traditional credit scoring?
• What is a credit scoring software?
• Benefits of credit scoring software solutions
• How to build a credit scoring software?
• Features of credit scoring software solutions

Credit scoring has become an integral part of lending and financial institutions. It enables lenders to assess the creditworthiness of borrowers, thereby reducing credit risk. With advancements in technology, the traditional manual process of credit scoring has been replaced by credit scoring software.

Credit scoring software solutions or credit scoring platforms use algorithms and artificial intelligence (AI) to automate the process of credit scoring, providing faster and more accurate credit decisions. In this blog, we will discuss how to build a credit scoring software solution or credit scoring platform in 2023 in detail. So, let’s start from the basics before we delve deeper!

What Is Traditional Credit Scoring?

Traditional credit scoring platform refers to the process of evaluating the creditworthiness of a borrower based on their credit history. Credit history includes factors such as credit utilization, payment history, length of credit history, types of credit accounts, and recent credit inquiries. Lenders use this information to calculate a credit score, which helps them make informed decisions about lending money.

The most common credit scoring model is the FICO score, developed by the Fair Isaac Corporation. It uses five main factors to calculate a credit score, with different weights assigned to each factor. These factors include payment history (35%), amounts owed (30%), length of credit history (15%), credit mix (10%), and new credit (10%).

While traditional credit scoring methods are effective, they can be time-consuming and require a significant amount of resources to evaluate each borrower’s credit history manually. This is where credit scoring software comes into play.

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What Is a Credit Scoring Software?

Credit scoring software is a platform that uses algorithms and AI to automate the credit scoring process. It enables lenders to evaluate a borrower’s creditworthiness in a more efficient and accurate way.

Credit scoring software solutions can analyze large amounts of data, including non-traditional credit data, such as social media activity, to make more informed credit decisions. Also, credit scoring software solutions are customizable, and lenders can set their own criteria for evaluating creditworthiness. This allows lenders to tailor the credit scoring process to their specific needs and risk tolerance.

Additionally, credit scoring software solutions can be integrated with other financial systems, making it easier to manage and track credit decisions.

Benefits of Credit Scoring Software Solutions

There are several benefits of using credit scoring software solutions, including:

• Accuracy: Credit scoring software solutions use AI credit scoring models that can make accurate credit risk assessments, reducing the risk of default and loan losses.
• Speed: Credit scoring software solutions automate the credit scoring process, making it faster and more efficient than traditional credit scoring.
• Customization: Credit scoring software solutions can be customized to meet the specific needs of lenders and financial institutions, improving the accuracy of credit risk assessments.
• Efficiency: Credit scoring software solutions reduce the time and effort required for manual credit scoring, making lending processes more efficient.

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How to Build a Credit Scoring Software?

Building a credit scoring software solution requires a comprehensive approach that includes data acquisition, feature engineering, model selection, and evaluation. In this section, we will discuss each of these steps in detail.

1. Data Acquisition

The first step in building a credit scoring software solution is to acquire relevant data. This may include historical data on loan repayments, credit card balances, and other financial information that can be used to assess credit risk.

Data can be acquired from various sources, including credit bureaus, financial institutions, and other third-party providers. It is important to ensure that the data is accurate, up-to-date, and relevant to the specific needs of the credit scoring software solution.

2. Feature Engineering

Once the data has been acquired, the next step is to identify and engineer relevant features. Feature engineering involves selecting the most relevant variables that can be used to predict credit risk.

Features can include variables such as income, age, employment status, credit history, and other relevant financial factors. It is important to ensure that the features are representative of the borrower's creditworthiness and are not biased or discriminatory.

3. Model Selection

After identifying and engineering the relevant features, the next step is to select an appropriate credit scoring model. Credit scoring models are statistical models that are used to assess the creditworthiness of an individual or a business.

These models use a variety of data points, such as credit history, income, and employment status, to calculate a credit score, which is a numerical representation of a person's or business's creditworthiness. Here are the top 5 credit scoring models in detail.

a. FICO Score

The FICO score is the most widely used credit scoring model in the United States. It was developed by the Fair Isaac Corporation and is used by many lenders, credit card companies, and other financial institutions.

The FICO score ranges from 300 to 850, with higher scores indicating better creditworthiness. The model considers a variety of factors, including payment history, amounts owed, length of credit history, and new credit, to calculate the score.

b. VantageScore

The VantageScore is a credit scoring model developed by the three major credit reporting agencies (Equifax, Experian, and TransUnion). Like the FICO score, it ranges from 300 to 850, with higher scores indicating better creditworthiness.

The model considers factors such as payment history, credit utilization, length of credit history, and types of credit used. VantageScore 4.0 also incorporates machine learning techniques to improve accuracy.

c. CreditXpert

CreditXpert is a credit scoring model that is used primarily in mortgage lending. It uses a combination of credit bureau data and statistical analysis to predict the likelihood of a borrower defaulting on a mortgage loan.

The model takes into account factors such as payment history, credit utilization, length of credit history, and recent credit inquiries. CreditXpert scores range from 350 to 850.

d. TransUnion CreditVision

TransUnion CreditVision is a credit scoring model that uses trended credit data to assess creditworthiness. Unlike other models, which only consider a borrower's current credit standing, CreditVision looks at how a borrower's credit behavior has changed over time.

This includes factors such as payment history, credit utilization, and length of credit history. The model ranges from 300 to 850, with higher scores indicating better creditworthiness.

e. Experian PLUS Score

The Experian PLUS Score is a credit scoring model that is used primarily by lenders for risk assessment. The model ranges from 330 to 830, with higher scores indicating better creditworthiness.

The PLUS Score considers factors such as payment history, credit utilization, length of credit history, and recent credit inquiries. It also incorporates public record information, such as bankruptcies and liens, into its calculations.

4. Train the Credit Scoring Models

The credit scoring models developed in step 3 need to be trained using historical data. The historical data should be representative of the data that will be analyzed by the credit scoring software.

The training process involves feeding the historical data into the credit scoring models and adjusting the parameters of the models to improve their accuracy.

5. Evaluate the Credit Scoring Models

The credit scoring models need to be evaluated to ensure that they are accurate and reliable. The evaluation process involves testing the models on a set of data that was not used during the training process.

The evaluation process helps to identify any weaknesses in the credit scoring models and provides an opportunity to improve their accuracy.

6. Develop the Credit Scoring Software

Developing credit scoring software solutions involves a variety of technical processes and tools, including the selection of tech stacks, frameworks, and testing methods.

a. Tech Stack

The tech stack refers to the combination of programming languages, frameworks, libraries, and other tools used to develop software solutions. When developing credit scoring software solutions, it is important to select a tech stack that is reliable, efficient, and scalable. Some popular tech stacks for developing credit scoring software solutions include:

• Python: Python is a popular programming language used in data analysis and machine learning applications. It offers a wide range of libraries and frameworks, such as NumPy, Pandas, and Scikit-learn, which are ideal for developing credit scoring software solutions.
• R: R is another programming language used in data analysis and machine learning applications. It offers a wide range of libraries and frameworks, such as dplyr, tidyr, and caret, which are ideal for developing credit scoring software solutions.
• Java: Java is a widely-used programming language that is reliable, secure, and scalable. It is often used in developing large-scale enterprise applications, including credit scoring software solutions.
• C++: C++ is a programming language known for its speed and efficiency. It is often used in developing applications that require high-performance computing, such as credit scoring software solutions.
  

b. Frameworks

Frameworks are pre-built libraries of code that simplify the process of developing software applications. They provide a set of rules, guidelines, and tools that enable developers to build software applications more quickly and efficiently. Some popular frameworks for developing credit scoring software solutions include:

• TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It offers a wide range of tools and libraries for developing machine learning applications, including credit scoring software solutions.
• Keras: Keras is an open-source neural network library written in Python. It is designed to be easy to use and flexible, making it an ideal framework for developing credit scoring software solutions.
• PyTorch: PyTorch is an open-source machine learning framework developed by Facebook. It offers a wide range of tools and libraries for developing machine learning applications, including credit scoring software solutions.

7. Testing and Launching

Testing is a critical component of software development, and credit scoring software solutions are no exception. There are several types of testing that should be performed, including unit testing, integration testing, and system testing.

Additionally, it is important to perform user acceptance testing (UAT) to ensure the software meets the needs of end-users. Launching a credit scoring software solution involves deploying the software to a production environment and making it available to end-users. It is important to ensure the software is secure, reliable, and scalable, and that it meets the needs of end-users.

Features of Credit Scoring Software Solutions

Credit scoring software solutions should provide several features to lenders and financial institutions. Some of these features include:

• Automated credit scoring: Credit scoring software solutions should automate the credit scoring process, reducing the time and effort required for manual credit scoring.
• AI credit scoring models: Credit scoring software solutions should incorporate AI credit scoring models that can make accurate credit risk assessments and help lenders make informed decisions.
• Customizable credit scoring models: Credit scoring software solutions should allow lenders to customize credit scoring models to meet their specific needs and lending policies.
• User-friendly interface: Credit scoring software solutions should have a user-friendly interface that is easy to navigate and provides clear and concise information to users.Scalability: Credit scoring software solutions should be scalable and able to handle large volumes of data, as lending institutions deal with large numbers of borrowers.

Undoubtedly the rising demand for the credit scoring platforms or software indicates the high profitability of launching a credit scoring software. But to leverage the high market growth scope, just developing a mere product is not enough. You need to onboard experienced professionals for the software development.

We, at Third Rock Techkno, offer a dedicated development solution to develop a market-leading credit scoring software. Check out our portfolio and drop us a line to get started today.

FAQs

1. What is credit risk model development?

Credit risk model development is the process of creating statistical models that assess the creditworthiness of borrowers. These models use data such as credit history, debt-to-income ratio, and other financial factors to determine credit risk.

2. How does AI credit scoring differ from traditional credit scoring?

AI credit scoring uses machine learning algorithms to analyze data and make credit risk assessments, while traditional credit scoring involves manual analysis of credit history, debt-to-income ratio, and other financial factors.

3. Can credit scoring software solutions handle large volumes of data?

Yes, credit scoring software solutions are designed to handle large volumes of data, making them suitable for use in lending institutions that deal with large numbers of borrowers.

4. Can credit scoring software solutions be customized to meet the specific needs of lenders?

Yes, credit scoring software solutions can be customized to meet the specific needs of lenders and financial institutions, improving the accuracy of credit risk assessments and lending practices.