16 February 2023
Over the years, an individual’s or even an organisation’s creditworthiness has come to be defined by their credit score. A loanee’s traditional data (e.g. credit history, types of credit, credit utilisation, 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 their 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.
Alternative credit data, sometimes categorised 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 bill 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, behaviour 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.
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 behaviour 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 reach and recognise new, profitable lending opportunities. Plus, with advanced ML implementation (more on that later), alternative metadata can be translated into reliable credit scores.
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 organisation.
As per this research conducted by the management consulting firm Oliver Wyman, a meticulous alternative credit data source should have these features:
A few types of alternative data frequently used in credit risk analysis are:
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 recognising the key patterns in customer behaviour 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:
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 realise 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 Fibe and all its innovations in the credit space.
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 organisations only seems reasonable. Taken into account the sheer amount of still unrealised possibilities that ML incorporation into credit risk analysis brings, the future of credit risk management sure looks bright.