Credit Scoring – How AI can personalize and optimize credit decisions

Credit scores have become synonymous with a person’s financial health and indicate their apparent creditworthiness. Financial institutions increasingly rely on this information to determine underwriting decisions and predict the possibility of events like defaults on outstanding credits.

The credit-scoring market typically consists of Consumer Rating Agencies (CRA) and other companies that develop and license automated scoring tools using proprietary methodologies. An individual’s credit report is generated by obtaining data from banks, credit card companies, mortgage lenders, and other potential sources. The CRAs have estimated that the Consumer Financial Protection Bureau (CFPB) receives around 1.3 billion updates from over 200 million consumer files each month. This credit report is then used to score the individual using the proprietary scoring models.

Automated credit scoring systems, such as those created by FICO, have become one of the prime indicators of Americans’ financial success over the past 30 years. But these automated scoring tools did not find widespread adoption until the early 1990s. However, now they are viewed as solutions that could significantly improve efficiency by eliminating biases in underwriting and credit scoring and other forms of discrimination that arise primarily due to a lack of good credit history and adequate analysis models. The CFPB, in 2010, stated that more than 90% of lenders relied on FICO scores for their underwriting decisions.


Before the modern credit scoring processes came into being, loan specialists and officers would screen the applicants. This human intervention brought a slew of personal biases into the decisions, and the approvals relied heavily on manual labor. Furthermore, the relatively newer scoring systems like those developed by FICO were unable to consider all aspects of a borrower’s financial health. Based primarily on the individual’s payment history, debt, length of the credit history, new credit, and the different types of credits used, it failed to factor in employment history, salary, and other factors that could contribute to creditworthiness. Hence, depriving some of a fair analysis while working well for others who checked all the boxes.  Due to this lack of well-rounded data and analysis, an Experian report tagged 64 million customers as “unscorable” denying them the traditional credit forms. The data typically used in these conventional credit scoring models lacked diversity and depth, rendering these models considerably myopic in predicting an individual’s creditworthiness. The direct result is lost opportunities for individuals and businesses.

Since every CRA’s credit-scoring process and models differ, an added risk of inconsistency gets added to the already short-sighted scoring system. With this considerable variance in the score across reports, consumers are left confused and tend to exclude them entirely when considering opportunities. ‘Behavioral analysis’ or ‘behavioral scoring’ is another form of credit assessment. It defines an individual’s creditworthiness based on their social associations like friends, neighbors, and others with similar interests, income, and background. Such an assessment disregards the individual’s merit, employment history, and other rational factors that could otherwise have given a more realistic analysis of their creditworthiness.

The processes and models constitute only one aspect of the erroneous scores. The inaccuracy of data used while determining the credit-score is another contributor. As per a study conducted by a 2013 Federal Trade Commission (FTC), 26% of the individuals studied had errors in their credit reports, and 13% had score errors. Because of the erroneous scores, these individuals were subjected to loan denials, higher interest rates, and other terms and conditions that were disadvantageous compared to those offered to individuals with error-free scores. Also, the error correction process is convoluted and time-consuming due to the long turnaround times and the number of the process involved in re-calculation. This seriously impacts the individual’s credit health and their ability to maintain it in the long run.

Solution Approach

Machine learning can help predict creditworthiness in an unbiased manner, considering all aspects of an individual’s financial status. Financial institutions have a tremendous opportunity to leverage these insights to arrive at the right decisions backed with data. In this attempt to create a complete solution, just analysis and creating a model does not suffice as the data is continuously growing and changing. To have a solution that is reliable, it is imperative to employ an end-to-end solution that can keep the model in sync with the data and all other artifacts that contribute to its accuracy. This is to ensure that the predictions are always in real-time. is an AI/MLOps lifecycle management platform that enables a comprehensive approach towards building enterprise Artificial Intelligence (AI) solutions. platform has an integrated set of frameworks and accelerators – the goal of which is to help data scientists build production ready cognitive AI solutions. The frameworks provided by the platform employ the latest machine learning and deep learning tools that are required to develop these complex credit scoring models.

The traditional approach to model development is tedious, time-consuming, and often prone to human biases. Additionally, inefficient data collection, lack of data models, and data cleansing activities can lead to inaccuracy in the data used to build the models. With automation embedded in the roots of’s platform, steps required to create a production-ready AI model are automated, accelerating the model creation process. These activities include data operations such as data collection, data analysis, data preparation, etc. Model operations such as continuous CI/CD/CT, model versioning, model monitoring, etc. And finally, deployment activities are where the model is deployed on production environment using containerization. 

Data Collection and Analysis

A large and significant part of the data transformation journey involves establishing the required infrastructure and collecting raw data (unformatted and unparsed) continuously from various sources like social media channels (LinkedIn, Facebook, etc.), scoring agencies financial institutions. platform provides connectors that are used to collect data from different sources as well as store and analyze this extensive data repository. Details such as the individual’s name, current employment details, as well as the history, work experience, frequency of job change, skill endorsements, group memberships, connections, online behavior, purchase patterns, to name a few, are collected. The data is then explored and analyzed to understand it and determine its quality and suitability to the model that needs to be created. platform has many accelerators in the form of python libraries and pre-built components that can help you do this in very few steps.

Data Cleansing and Preparation

The collected data may not be clean and may have outdated, incorrect, redundant, or incomplete information. platform provides the necessary libraries that assist in data correction activities such as data cleansing, standardization, and the removal of stale and extraneous data.

Model Training & Evaluation

Once the data is prepared, multiple experiments are done with varying algorithms, data, and parameters. platform makes it very easy to run multiple experiments, and every artifact of each experiment is versioned and stored for better traceability. Each model and its artifacts can be tracked in real-time and compared to arrive at the optimum model that serves the business needs best.

Model Deployment

Using the platform, trained models can be deployed in just a few simple steps. They can also be scaled and managed in a highly available multi-cloud system rendering a credit score model ready for end-users.


Adopting a clear and consistent credit scoring policy allows credit managers to evaluate the individual’s performance and history without exceptions or biases that may otherwise arise due to human biases, behavioral analysis, and/or lack of sufficient and accurate data. The individual credit scores can then be used to create segments, place each segment in the proper workflow and create the best actions for each individual/customer.

How can help Finance Organizations transform their journey to cognitive AI solutions is an AI/ML Application Lifecycle Management Platform. enables complete lifecycle management of AI/ML solutions, addressing the AI transformation journey of enterprises on any cloud platform of choice. offers functionality essential for building AI/ML solutions – primarily enabling data scientists to rapidly build predictive and prescriptive models. The platform provides a user-friendly interface to develop, deploy, and manage AI/ML solutions at scale. In addition, supports the incorporation of these solutions into business processes, surrounding infrastructure, products and applications.

Key benefits of include:

  • Empowers data scientists to transform AI/ML research into solutions 
  • Improves the productivity of data scientists by enabling them to focus on the business problem, developing algorithms and rapid experimentation of models 
  • Addresses the shortage of skilled data science resources with automated workflows, toolkits and frameworks 
  • Manages AI transformation journey costs without any wastage of R&D efforts  
  • Provides an enterprise-ready and secure environment for complete lifecycle management of AI/ML applications
  • Enables at-scale deployment of enterprise AI/ML applications on-premise, cloud (AWS, GCP, Azure), or hybrid environments

Additional details on can be found at: We can schedule a demo of the platform for anyone interested in learning more.

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