We helped a leading US-based credit analytics company implement a robust, flexible, and modern credit scoring system, which helped them make the best credit decisions with a high level of accuracy. The solution we built helped them generate major revenue.Get In Touch
Determining the creditworthiness of a person is the primary objective of a credit scoring system. Financial industries rely on credit scores to determine a consumer’s eligibility to use their services. Relying on accurate, complete, and relevant data is very critical in minimizing risk while taking high-stakes decisions such as qualifying for a loan.
Determining the credit risk of potential customers using a credit score is one of the major costs incurred by financial industries. Minimizing the costs in the credit scoring system will have a positive impact on the revenue.
The existing scoring systems are costly. They rely on archaic and legacy systems that are slow and rigid. They don’t have the capability to predict the future risk of default. Our client approached us to explore the possibility of solving this problem by building a modern and cost-effective credit scoring solution, taking advantage of modern technologies.
The requirement was to build a new credit scoring system that is modern, fast, cost-effective, flexible, and more accurate in predicting creditworthiness. We worked with our clients in brainstorming, building prototypes, and doing multiple proofs of concepts.
There are many factors and parameters that come into play when generating a credit score. We used the credit data available from any of the three national credit bureaus – Experian, TransUnion, and Equifax – to analyze various factors in a person’s financial records.
We developed unique methods to gain insight into consumer credit behavior and borrowing and payment patterns, which can be used across all credit industries. We architected a robust system by combining the credit data with machine learning modeling techniques to produce an accurate and reliable credit score. We leveraged ML technologies like hyper-parameterizing to optimize the credit score. Our system could take reporting data in different formats and produce outputs in different formats using different protocols such as Webservice, RESTful API, XML over HTTP, and FTP.
Along with the credit scoring system, we also developed mortgage-related products around credit scoring for our client:
The infrastructure to host these systems were approved by security auditing companies, which follow strict security guidelines. Also, we made sure to comply with all regulatory requirements.
Our easy-to-use credit scoring system helped the customers make accurate credit decisions cost-effectively. The solution has been used to generate credit scores and other related products millions of times. Our system recorded a high performance by generating credit scores in milliseconds, which was an improvement by a factor of about 30. It was estimated that our solution helped to bring in a significant reduction in the defaults.
The products and applications around these credit scoring system generated major revenue for our client and their customers. Also, the overall operation cost was reduced by 50%.
The following are some of the benefits of our credit scoring system: