The ground rule for any lender is to check whether a borrower is creditworthy enough to receive a loan. Banks and financial institutions have long been successfully using various credit scoring models for that. However, the financial sector faced significant challenges during the COVID-19 pandemic, including declining ROE and reduced loan investments. These disruptions accelerated the adoption of more flexible lending approaches and encouraged financial institutions to invest in advanced credit scoring software.
Credit scoring software allows banks and financial institutions to effectively expand their client bases while keeping lending risks low.
Therefore, more and more companies are considering utilizing an alternative to conventional scoring models – AI-based credit scoring software.
In this article, you’ll find out what is a credit scoring system, what are the main benefits of this type of software, and how to implement it in your FinTech solutions.
Traditional Credit Scoring vs Alternative Scoring
Before we get to the differences between traditional and alternative scoring, let’s find out what credit scoring is and why it is important.
Credit scoring is the process of evaluating a borrower’s creditworthiness to determine whether they qualify for a loan. When scoring is performed, an applicant receives a three-digit number that stands for a score. If the score is high, then financial institutions provide the borrower with a loan, though if it’s low then lending is highly likely to be rejected, or interest rate will be higher.
Any customer interested in their score can check it as well as banks and various financial organizations once they gain their client’s permission.
Traditional Credit Scoring
There are a number of conventional credit scoring models around the world. Let’s have a look at the most popular ones in the US as an example. They are FICO and VantageScore. Both have much in common though they pay attention to different factors for determining a credit score.
FICO
FICO is the most popular scoring model in the US developed in 1989. It’s used by more than 90% of top lenders in the US. FICO offers diverse types of scoring. For example, if a customer wants to get a loan for a car, they should check their FICO AutoScore whereas an application for a credit card will require checking FICO Bankcard Score.
When determining a score, the major factor FICO relies on is payment history. The score range in FICO is usually as follows:
VantageScore
VantageScore is a competing model with FICO created in 2006. Just like FICO, VantageScore also offers its users several suites depending on the loan purpose. Along with this, different suites include various factors for tracking credit behavior, e.g. VantageScore 4.0 model includes trended data in its scoring decisions.
When determining a customer’s score, VantageScore focuses mainly on the customers’ credit card balances and credit utilization ratio. The score ranges include:
The credit scoring models aren’t limited to only FICO and VantageScore which provide their own scores, though they are heavily based on these two most popular models.
Alternative Credit Scoring
An alternative scoring model derives from conventional scoring methods and statistical techniques that are effectively enhanced by digital innovations. It means that lending organizations don’t have to request scoring data reports from credit bureaus. Instead, they can use the latest digital technologies to gather and evaluate customers’ digital footprint.
Utilization of credit scoring software solutions is effective when conventional bureau data is unavailable, there is little access to diverse data sources, or when borrowers fall into the group of underbanked consumers. When these situations occur, lenders experience difficulties in the proper evaluation of loan risks.
How FinTech Companies Utilize Alternative Credit Scoring
Lending companies are becoming more interested in credit scoring software today. It allows lenders to expand their customer bases by shifting their focus from credit-worthy clients to potential customers who would gain low scores in conventional scoring systems. This way, companies can offer loans to individuals with limited or no credit history, e.g. students, freelancers, households with low income, unbanked people, and others while being assured that they will pay back their loans on time.
According to the FDIC National Survey of Unbanked and Underbanked Households, 4.2% of U.S. households (about 5.6 million) were unbanked in 2023, meaning that no one in the household had a checking or savings account at a bank or credit union. The US stats look encouraging when comparing them to the percentage of the unbanked population worldwide by country.
The COVID-19 pandemic in 2020 also accelerated the shift toward alternative credit scoring models. Economic disruptions and rising unemployment exposed the limitations of traditional credit assessment methods, prompting lenders to explore more flexible and data-driven approaches.
Regulatory Considerations in Credit Scoring
Modern credit scoring systems must comply with various regulatory requirements aimed at protecting consumers and ensuring fair lending practices. Regulations such as GDPR, the EU AI Act, and other consumer protection laws impose strict requirements on how personal data can be collected, processed, and used in automated decision-making systems.
Financial institutions must ensure that their credit scoring models are transparent, explainable, and free from discriminatory bias. This means lenders should be able to justify automated lending decisions and demonstrate that their models treat applicants fairly.
As a result, modern credit scoring software increasingly incorporates explainable AI techniques, audit trails, and compliance monitoring tools to meet regulatory expectations while maintaining accurate risk assessment.
New Technologies in the Lending Industry
While data from credit bureaus can be insufficient as it usually takes bureaus some time to receive customers’ data, credit scoring software processes data practically in real-time. This becomes possible due to the latest digital innovations that software developers use to create credit scoring applications. They include Big Data processing, Artificial Intelligence (AI), Machine Learning (ML), and others.
- Big Data tools process massive amounts of raw customer data and find the pieces of information suitable for scoring;
- Artificial Intelligence and Machine Learning identify patterns in various data types, analyze the data, predict outcomes, and determine borrowers’ credit scores.
- Modern credit scoring platforms also rely on cloud-native architectures, which enable scalable data processing and seamless integration with banking systems, payment platforms, and third-party data providers.
- In addition, real-time data pipelines and feature stores help continuously update borrower profiles with the latest financial activity, allowing lenders to make faster and more informed lending decisions.
By using advanced analytic tools based on AI and ML, lending companies receive the most accurate and up-to-date information on their consumers’ scores.
Alternative Data Sources for Credit Scoring
When evaluating a credit score, modern systems analyze data from multiple financial and behavioral sources, including transaction history, utility and telecom payments, rental records, and other types of alternative data.
Such information helps lenders build a broader view of a borrower’s financial behavior and reliability. By analyzing these indicators, alternative credit scoring systems can identify patterns that demonstrate whether a borrower is likely to meet their financial obligations.
Credit scoring software can therefore go beyond evaluating traditional credit history and payment records. Modern systems incorporate additional data sources such as e-commerce activity, payroll information obtained through open banking APIs, and other financial indicators.
By combining these data sources with advanced analytics and machine learning models, lenders can build a more accurate and up-to-date profile of a borrower. This approach is particularly useful for assessing individuals with limited or no traditional credit history, such as freelancers, young professionals, or people new to the banking system.
In addition, credit scoring platforms may use identity verification technologies and behavioral signals to confirm that an applicant is a real person and to reduce fraud risks. This enables financial institutions to make faster and more informed lending decisions while maintaining responsible risk management.
Key Advantages of Alternative Credit Scoring
Alternative credit scoring offers a wide range of advantages both for lending organizations and their clients. Here are the key benefits they can expect from this type of software.
A comprehensive view of a customer
Credit scoring software provides a more complete picture of each borrower by analyzing their current financial data as well as behavioral indicators. As a result, the system creates a more holistic understanding of the borrower’s profile and their ability to repay loans.
Data-driven decisions
Credit scoring software gathers and processes more client data than what typically reaches credit bureaus. It means that lending companies can use more client data sources to decide whether to provide an applicant with a loan. Moreover, they can customize their scoring, ensuring that more people can be eligible for a loan.
Instant data on scores
Credit bureaus that gather consumer credit data usually obtain the information in some time intervals. This way, data can differ from bureau to bureau. Credit scoring software, in its turn, processes customer data in real-time, eliminating any human factor from the process. Apart from that, loan providers don’t have to spend time requesting customer information from bureaus, receiving all the data almost instantly.
Easier accessibility to crediting
Conventional scoring systems are rather rigid. They don’t consider many factors for lending, e.g. consumers with a thin credit file. This deprives a wide range of potential borrowers of lending services. Credit scoring software has a more flexible approach to measuring the creditworthiness of loan applicants. Therefore, a wider public can be qualified for a loan and successfully obtain it.
Credit Scoring Software Development Process
Those companies and startups that consider investing in financial software development and creating their own credit scoring software solutions, should be well-prepared for that. They need to carefully allocate their software development budget, find the needed resources, and elaborate a detailed implementation plan.
The implementation plan will ensure that all the software-development processes will go smoothly and the company won’t waste any budget or resources on unnecessary features or changes in their software.
Here are the steps companies need to include in their credit scoring software development process:
Creation of a credit scoring model
This includes gathering all the relevant external and internal data and combining it in such a way as to create a statistical credit scoring model.
Measurement of a default likelihood
At this step, the lender should identify the characteristics a borrower should possess that would indicate their inability to repay their debt.
Establishing risk classes
This involves determining the risk classes and their threshold values as well as creating measures that should be applied to each class in case a borrower won’t comply with the established rules, e.g. application refusal, modifications of payment terms, adjustment of interest rates, etc.
Backtesting and calibration
When performing software implementation it’s important to verify that the credit scoring achieves the desired target based on client data. Backtesting involves testing and adjustment of scoring systems that include classifications, credit scores, managerial advice, and others.
Application in practice
At this stage, lenders implement the scoring system as a part of their risk management in practice.
Monitoring
This includes documenting and monitoring credit and debtor data and their payment activities in order to keep technical maintenance of the scoring system and ensure effective credit risk assessment.
Conclusion
Credit scoring software is increasingly becoming a key component of modern lending systems. The growing demand for faster loan approvals, the need to assess borrowers with limited credit history, and the expansion of digital financial services are driving financial institutions to adopt more advanced credit risk assessment tools.
Traditional scoring models alone are often unable to provide a complete picture of a borrower’s financial behavior. By leveraging technologies such as Big Data, artificial intelligence, machine learning, and alternative data sources, modern credit scoring solutions enable lenders to evaluate risks more accurately and make better lending decisions.
As a result, financial institutions can improve approval rates, reduce default risks, and expand access to financial services for underserved populations.
For organizations planning to build or modernize their lending platforms, developing a reliable credit scoring system requires strong expertise in FinTech, data analytics, and secure software architecture. With over 25 years of experience in custom software development, SCAND development team helps companies design and implement scalable credit scoring solutions tailored to their specific business and regulatory requirements.
