Over the years, an individual’s or even an organization’s creditworthiness has come to be defined by their credit score. A loanee’s traditional data (e.g. credit history, types of credit, credit utilization, etc.) is usually the only factor considered by credit scoring systems to evaluate their creditworthiness. The problem with this system is that a significant part of the population has an insufficient or nonexistent credit history – making them credit-invisible. Indeed, the most common barrier many loanees in India face is the lack of a credit score.
To provide credit access to a wider audience and achieve financial inclusion, loaning institutions must consider a different approach to confirm a loanee’s creditworthiness. This is where alternative credit data comes in.
What is Alternative Credit Data?
Alternative credit data, sometimes categorized as big data, is any data that’s not directly related to a client’s credit conduct. Alternative data regarding a client can be obtained from a number of non-traditional data sources- e.g. digital platforms that can provide information on consumer activities for credit risk assessment. Before lending out to a customer, lenders can have credit risk management models leverage alternative data to develop credit scores and ensure their customers’ creditworthiness.
Alternative data for credit scoring can be a combination of the information collected from multiple sources, including a consumer’s utility, rental, insurance, and other bills payments history, social media usage, employment history, travel history, e-commerce, government transactions, and property records.
However, when collecting alternative data for credit risk analysis, it’s important to remember that the gathered data must consist of data points that show the loanee’s habits, preferences, behavior, and character- which is one of the five C’s of credit risk (the others being capacity, condition, capital, and collateral).
It’s also important to make sure the borrower cannot directly or indirectly manipulate any of the given data. This ensures a thorough evaluation of the potential loanee’s financial abilities and credit risk profile.
How Reliable is Alternative Data When Predicting Credit Risks?
When it comes to using alternative data in credit risk analysis, there’s no specific set of guidelines to follow. Since this approach to credit risk analysis is also fairly new, it’s still in its tentative stages – there’s no extensive historical evidence available to guarantee alternative data’s effectiveness or reliability when it comes to credit risk predictions.
However, it’s undeniable that even with the traditional way of risk assessment, there’s always going to be risks in the lending business. The alternative data system, keeping up with the digital age, has certainly proven to be more efficient in credit risk analysis, since it focuses on a loanee’s behavior and can bring up data points that the traditional methods might have glossed over. An added benefit of using alternative data in risk appraisal is the increased levels of accuracy, compared to the traditional way of credit scoring.
In recent years, the general market practices have slowly evolved, with more and more lenders using additional information related to the user along with the traditional credit report to make better lending choices. According to Experian, 65% of lenders in 2019 used some information beyond the traditional credit scores to make lending decisions.
Whether combined with traditional credit scores or not, alternative data provides a detailed picture of a potential loanee’s creditworthiness. It allows creditors to expand their reaches and recognize new, profitable lending opportunities. Plus, with advanced ML implementation (more on that later), alternative metadata can be translated into reliable credit scores.
Where to Get Alternative Credit Data?
A wide range of non-traditional data can attest to a loanee’s creditworthiness – the sources and sorts of data used in the credit risk analysis depend entirely upon the creditor organization.
As per this research conducted by the management consulting firm Oliver Wyman, a meticulous alternative credit data source should have these features:
- Coverage: A data source will ideally have broad and consistent coverage (for instance, the mobile phone market is more concentrated than most others, so data collection is easier there).
- Specificity: The data source should contain detailed information about the individual/organization applying for a loan. (e.g., timely/late payments over a particular time period, income data, etc.).
- Accuracy and Timeliness: The data considered must be accurate and updated frequently.
- Predictive Power: The information should be relevant to the specific consumer behavior being assessed.
- Orthogonality: Ideally, the data source would complement traditional bureau data, so that its use would improve the accuracy of traditional credit score.
- Regulatory compliance: Alternative credit data sources must abide by existing regulations for consumer credit.
A few types of alternative data frequently used in credit risk analysis are:
- Phone, Rent, and Utility bills: Since all of these payments have to be made periodically, they are some of the most trustworthy sources for alternative data collection. Periodic payments provide frequently updated insight into a consumer’s financial behavior.
- Social Media Accounts: A consumer’s social media pages (for example, LinkedIn, Facebook, Instagram, and Twitter) can bring forth a lot of information including their employment status, lifestyle, and spending habits, etc. However, the data displayed on social media can be inaccurate and is also directly influenced by the consumer; therefore, the credibility of alternative data procured through social media can be diluted.
ML Adoption in Extracting and Processing Alternative Data:
When it comes to processing the gathered alternative credit data, manually going through a loanee’s information would be incredibly taxing, not to mention the quite high chances of human errors or oversight. Therefore, it’d be in the best interests of lending corporations to look into advanced technologies such as machine learning and AI (artificial intelligence) that can take over the process on their behalf.
ML, or machine learning, comes with superlative analytical frameworks that could help in evaluating data accurately and recognizing the key patterns in customer behavior under different circumstances. The convergence of different ML techniques with alternative data could prove revolutionary in credit risk analysis. Some of the advantages that ML can provide are:
- Rooting out only the useful information out of sizable data sets
- Lowering data processing time
- Giving a full rundown of a customer’s creditworthiness based on the collected data
- Recognizing key patterns in consumer behavior under different circumstances
- Predicting a loanee’s ability to repay the loan in time
Interested in learning more about the whys and hows of integrating machine learning into credit risk analysis? We’re happy to share our thoughts on the convergence of machine learning and big data in credit risk management.
With the number of people looking to get loans for varying purposes, increasing with every passing day, the credit industry needs to realize the significance and benefits of financial inclusion. After all, only a small percentage of people in Asia have a formal credit history; to work towards closing the lending gap that still exists, companies need to look into other ways to evaluate a person’s creditworthiness. It’s realities like these that led to EarlySalary and all its innovations in the credit space, enabling us to achieve 1 million loan disbursals.
As the usage of smartphones grows and the financial systems worldwide gradually become internet-based, tracing a person’s digital footprints has become a lot easier. Besides, collecting alternative data has inarguably gotten simpler than accumulating traditional credit data.
Keeping pace with the advancements in individual technologies, the introduction of an alternative data-based credit risk management system in loaning organizations only seems reasonable. Taken into account, the sheer amount of still unrealized possibilities that ML incorporation into credit risk analysis brings, the future of credit risk management sure looks bright.
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