Granting loans and giving bank credit is impossible to do without first checking the potential borrower’s financial history. Do they have any active loans? What about those they’ve already repaid? Were they ever late with payments? The credit score rating system was created to simplify the research process. It helps banks and lending organizations understand if a person can be trusted with loans.
It is complicated to calculate this manually, so the easiest way to determine creditworthiness is to have a software program that automates it, saving you and your clients a lot of time during the application process.
In this article, we will talk about the purpose and benefits of credit scoring software, list its necessary features, and possible challenges you may face during development.
To start the discussion, let’s define in more detail what this software is. Credit scoring software is a program used by lenders and financial institutions to evaluate the creditworthiness of a future borrower. It uses a variety of data, including credit history, employment history, and other financial information to generate the borrower’s score.
A credit scoring system can serve various purposes. It can aid lenders in making swift and precise decisions regarding the acceptance or denial of loan applications, and helps determine the interest rates that should be charged. This makes it a crucial tool for lenders to evaluate any potential risks of a loan application and allows them to make well-informed decisions.
To estimate a particular credit score, the software uses a complex algorithm that considers several different financial factors. It then uses those results to calculate and present a final number, the credit score. This number will determine whether or not the loan will be granted.
The software assigns a numerical score to each individual based on the data analysis of their credit history. It also compares a person's profile to others with similar credit histories to see how they stack up. These processes help lenders understand whether a person who applies for a loan stands in terms of creditworthiness compared to their peers.
We already mentioned that to evaluate a credit score, the software needs to consider some initial data inputs. These variables may differ depending on the credit scoring model, but most models take into account the following:
Payment history: This includes all types of loan payments made by the applicant: on-time payments, late payments, missed payments, and more.
Credit utilization: This is the amount of money being used compared to what amount of money is available. If the ratio is too high, it can damage the credit score.
Length of credit history: The time the borrower has been using credit. A longer history generally means a higher score.
Credit mix: This category refers to the types of credit the borrower has, for example, student loans, credit cards, and/or mortgages. A diversified mix can help boost the credit score.
New inquiries: The number of times the borrower has applied for a loan in recent times. If there are many inquiries in a short time span, the credit score will be lower.
Credit scoring models take these factors into account and generate a specific number, ranging between 300 and 850. The higher the score is, the more creditworthy the person is considered to be.
In order to collect all the metrics and calculate the necessary number, the credit score software needs to get them from somewhere. To do so, it accesses a wide range of data sources that provide information about an individual's financial behavior. Here are some of the types of these sources:
A credit bureau is an organization that maintains information on people's credit histories and uses the data to generate reports. These reports typically include information about an individual's accounts, their payment history, any outstanding balances, credit limits, and recent inquiries.
Financial public records include bankruptcy filings, tax liens, legal judgments, UCC filings, and foreclosures. These records are maintained by various government agencies and can be accessed by anyone who requests them.
Collections agencies are hired by creditors or lenders to collect unpaid debts from those who have fallen behind on their payments. While such organizations are legally allowed to contact debtors, they follow strict regulations like the Fair Debt Collection Practices Act (FDCPA). This law prohibits collections agencies from using abusive or deceptive practices to collect debts (harassment, threats, false statements, or contacting debtors at unreasonable times or places).
This metric doesn’t typically contribute to calculations, however, some lenders consider employment history as part of their overall evaluation of creditworthiness. For example, if an individual has a stable employment history with a consistent income, it may positively influence the evaluation.
Address and identity verification
Address and identity verification data can also be used to help ensure that the credit report is accurate.
Machine learning algorithms can analyze large amounts of data and identify patterns that help predict an individual's creditworthiness. Here are some ways that machine learning algorithms are used in the industry:
Risk assessment: Machine learning can help identify risk factors that are associated with defaulting on loans.
Predictive modeling: Predictive modeling can generate predictions about an individual's creditworthiness based on their credit history and other factors.
Fraud detection: Algorithms can analyze data from various sources to identify unusual behavior that may be indicative of fraud.
If you’re looking to empower your business with artificial intelligence, check out this guide for tips on how to achieve this successfully.Learn more
Machine learning allows lenders to apply automated decision-making, which can help reduce the risk of default and improve the overall financial health of borrowers.
One of the main advantages of this type of software is streamlining the loan approval process. It gives lenders a standardized method for evaluating creditworthiness and reduces the time for reviewing loan applications.
Also, such software can improve the accuracy of evaluations. By using a unified scoring model, the software can provide a more reliable method for assessing an individual's creditworthiness, reducing the risk of errors in evaluations, and improving lending practices.
Traditional credit assessment methods can be influenced by unconscious biases that can lead to unfair lending practices. By reducing the impact of subjective factors, lenders are able to make decisions based on merit and risk rather than on factors like gender or socioeconomic status. This can lead to more equitable lending and help with promoting financial inclusion for underserved populations.
A credit scoring system can analyze vast amounts of data from common and alternative data sources like social media activity and payment history for utilities and rent. Processing all the available data can also help to identify creditworthy individuals who may have been overlooked by traditional models, leading to greater financial inclusion.
Here are some of the features that any credit scoring software should have in order to be easy to use:
Lenders can adjust the importance of factors that will influence the credit score to create a model that reflects their preferences and risk tolerance. This way, the process is fine-tuned to match their business objectives. Customization can also help lenders address business-specific challenges like assessing people with a limited credit history or identifying suitable applicants in high-risk industries.
Another important feature of credit score software solutions is the ability to provide real-time updates and monitoring of credit scores. Having the assurance that they are receiving the most up-to-date information allows lenders to quickly assess changes in an individual's creditworthiness and adjust lending decisions accordingly.
For example, suppose a borrower's score drops due to a missed payment or increased credit utilization. In cases such as these, the lender can take appropriate action such as increasing interest rates or adjusting the repayment terms. Real-time monitoring can also help to identify potential fraud or identity theft so all parties of a lending deal stay protected.
To streamline the lending process the software can also integrate with other financial systems like loan origination software and customer relationship management systems. This can help to reduce manual errors and improve the efficiency of the lending process, enabling lenders to process applications more quickly. Integration with other software can also provide a more seamless and convenient experience for borrowers and improve customer satisfaction and loyalty.
Credit scoring software development starts with a detailed understanding of the problem you are trying to solve. To define it, you will need to identify the purpose of the credit scoring software, understand the data requirements, determine technical requirements, and describe success metrics. Defining the problem is crucial to building credit scoring software that meets its intended purpose, performs accurately, and meets the requirements of its users.
After defining the problem, the next step is to collect the data. Some of the data sources for credit scoring software include credit reports, loan repayment history, income statements, and employment history. Once the data is collected, it must be preprocessed to clean and prepare for analysis. Preprocessing involves removing duplicates, handling missing values, encoding categorical variables, and scaling numerical variables.
Several machine learning algorithms can be used for credit scoring, including logistic regression, decision trees, random forests, gradient boosting, and neural networks. The choice of algorithm will depend on various factors, such as the size of the dataset, the complexity of the problem, and the computational resources available. The chosen algorithm must be able to accurately predict creditworthiness based on the data provided, and it must be able to do so in a timely and efficient manner.
This involves splitting the data into a training set and a testing set. The training set is used to teach the algorithm how to predict creditworthiness based on the input data, while the testing set is used to evaluate the accuracy of the algorithm's predictions. The training process involves adjusting the algorithm's parameters to minimize the difference between its predicted values and the actual credit scores. This is typically done using a cost function that measures the error between the predicted values and the actual values.
Once the algorithm has been trained, the next step in building credit scoring software is to evaluate its performance. You will need to measure the accuracy of the algorithm's predictions with a variety of metrics, such as precision, recall, F1 score, and ROC AUC. Additionally, it's important to perform regular testing and validation to ensure that the algorithm remains accurate and effective over time, and to identify and address any issues that may arise.
The validation dataset is a separate set of data that was not used in the training process and is used to evaluate the algorithm's performance further and to ensure that it's not overfitting to the training data. Overfitting occurs when the algorithm becomes too specialized to the training data and may result in poor performance on new, unseen data. Testing the algorithm on a validation dataset allows for a more accurate assessment of its performance on new data.
Deployment involves making the software available for use by end-users, such as lenders or credit bureaus. The deployment process may involve integrating the software with existing systems or applications, and ensuring that the software is compatible with the end user's hardware and software environments. It's important to ensure that the software is secure and reliable and that it meets all regulatory requirements and industry standards.
Monitoring may involve analyzing the performance of the software regularly, identifying any issues or trends that may be affecting its accuracy, and making appropriate changes or updates to address these issues. Updating the software may involve incorporating new data or variables, improving the accuracy or speed of the algorithm, or incorporating new regulatory or industry requirements.
There are several options available in the market today. Here are some examples:
FICO: FICO is a leading provider of credit scoring software used by many lenders and financial institutions around the world. FICO's software uses advanced analytics and machine learning algorithms to evaluate credit risk and generate credit scores for individuals.
Experian: Experian is a credit reporting agency that also provides credit scoring software. Experian's software uses a variety of data sources to evaluate creditworthiness, including credit reports, public records, and alternative data.
Equifax: Equifax is an agency that offers credit scoring software to lenders and financial institutions. Equifax's software uses a combination of credit reports, public records, and alternative data to evaluate creditworthiness.
Building any financial software is a complex task to complete, so it’s natural you may encounter some challenges along the way. Knowing about them beforehand will help you manage them or even avoid them completely.
Data privacy is one of the most common challenges in the fintech industry, which includes lending. Credit scoring software can access a number of data sources that contain personal and sensitive information about people. This data is highly valuable and can be a target for cyberattacks and other security breaches. All financial institutions must take appropriate measures to protect this data.
The software’s algorithms are designed to evaluate creditworthiness based on a wide range of factors. However, these algorithms may also incorporate variables that are correlated with protected characteristics, such as race, gender, and age, which can lead to discriminatory outcomes.
To address this challenge, providers must ensure that their algorithms are designed to be fair, transparent, and unbiased. This may involve incorporating safeguards and being transparent about the variables used in the model.
Lenders and financial institutions must comply with a range of laws about lending and data security:
Fair Credit Reporting Act (FCRA)
Equal Credit Opportunity Act (ECOA)
Truth in Lending Act (TILA)
General Data Protection Regulation (GDPR)
California Consumer Privacy Act (CCPA)
Besides legal liability, failure to follow these regulations can result in financial and reputational damage. It’s important to stay up-to-date on the latest regulations and to implement robust compliance programs to ensure that they stay in compliance with everything needed.
Yellow specializes in creating custom credit scoring software solutions for financial organizations. With our expertise in fintech development, we can help you design and implement a system that meets your needs and requirements.
Our team of engineers can work with you to customize a model that works for your business, ensuring that it takes into account the variables and factors that are most relevant to your lending criteria. We also prioritize the importance of data privacy and security in our development process, ensuring that your customer data is protected at all times.
If you want to build your credit scoring solution and/or integrate it into existing software, feel free to contact us and get a detailed estimate.
Despite the challenges and complexities of credit scoring software development, they remain a valuable solution, and, with the continued advancements in artificial intelligence and machine learning, its future looks promising. As the technology continues to evolve, it’s important for providers to prioritize fairness, transparency, and compliance, ensuring that their algorithms and models do not violate any laws or regulations and that they provide unbiased and objective assessments of creditworthiness.
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