Get your Free Credit Score Report Online Instantly.

Artificial intelligence is the term used to describe the intelligence that machines are built with in order to automate tasks and solve problems. It is used in a variety of fields, including banking, to complete many tasks that were earlier carried out manually. One of the key roles of AI in banking pertains to assessing the track record of loan applicants who have no credit score.

Credit score is a three-digit score provided by credit information bureaus such as CIBIL to assess the credit history of a loan seeker. However, not everyone may have a credit history or score in the first place, because they may be unbanked or have never sought a loan before. According to a recent report, 50% * of the earning population in India have no or very little participation in the credit space.

It is in such a scenario that credit scoring artificial intelligence assumes importance.

How Is Artificial Intelligence Helping the Banking Sector

Banks and financial institutions have begun to use AI for customer service, by way of chatbots that resolve everyday banking issues pertaining to cards and account details, among others. Bots that converse with customers cut down many customer-facing tasks so that the staff can focus on more important core banking tasks.

 

Another big area that AI has been helping the banking sector is in the collection of data and analysis of the same. This data has been found to help banks and financial institutions boost customer experience and fraud detection.

Role of Artificial Intelligence in Credit Score

One of the biggest uses of AI in banking is in the domain of loans. Extending credit to borrowers can be daunting when there is no credit history or score to keep track of. In such a scenario, the lender is taking a huge risk by approving a loan. If a borrower with no credit score or a history of defaults yet again, it can lead to issues for banks. In such a scenario, having a credit scoring artificial intelligence system can be useful for lenders to assess the risk involved in approving a loan to a borrower.

 

Credit scoring artificial intelligence can help a lender by providing alternative data in place of a traditional credit score. So, if you are borrowing a loan for the first time with no credit score, artificial intelligence and machine learning tap into data pertaining to your financial transactions including shopping habits and history, the phone you use, the places you visit for shopping, entertainment or eating and even your activity on social media.

 

By tapping into and analysing this data, the AI system creates an alternative credit score for you. This data helps lenders assess whether the borrower has a genuine need for a loan and whether they have the capability to repay and not default.

How AI Minimises the Risk for Lenders by Assessing Loan Seekers with No Credit Score

Consider a situation where a lender has not applied for a loan before and has no credit score or history. The individual approaches a lender online and begins the application process.

 

The app or the website visited by the lender poses questions to the applicant that pertain to where the individual works, their shopping habits, for instance, or the kind of phone they use. This is then matched with live data collected by the lender.

 

When a fintech, bank or financial institution uses a credit scoring artificial intelligence model, hundreds of data points are gathered on the basis of permissions granted to the apps/websites. The data includes digital transactions, phone call logs etc.

 

The AI model creates a user’s risk profile and earnings track record by gathering their smartphone behaviour and other human behavioural traits. The lender uses this profile to either approve or reject a loan application, thereby minimising the risk of defaults.

Advantages of Artificial Intelligence in Credit Score

Credit scoring artificial intelligence has many advantages. They are as follows:

  • Traditional credit scores are evolved on the basis of historical data. A borrower’s past borrowings are used to predict the future. However, AI-based credit scores are evolved on the basis of live data.

  • AI-based credit scores help lenders make decisions on the go and resolve issues faster. It takes more time to evolve traditional scores of creditworthiness. Using an AI-based system is a huge timesaver for lenders who can disburse credit faster.

  • Credit scoring artificial intelligence uses a wider and diverse range of data to help form a decision about approving credit to borrowers. Standard data points like age, income, gender or occupation may no longer be adequate for taking credit-related decisions. A wider range empowers more borrowers to gain credit access and build a credit history.

  • AI-based credit scoring systems are self-learning models and are constantly evolving. Such a system learns from data, analyses it and shows predictions that can be used at scale. Conservative models of credit scoring may not be able to replicate the same as they are based on fixed data points.

  • Fraud can be detected remotely and in an accurate manner when credit scoring artificial intelligence systems are used. In a traditional model, detecting fraud is rules-based. With artificial intelligence and machine learning, the rules-based approach is replaced with a real-time/live approach.

  • Credit scoring artificial intelligence models help banks, fintechs, and financial institutions penetrate deeper into the lending space. It helps lenders reach those who have thus far found it difficult to avail of loans or other forms of credit. The AI-based credit scoring model taps into unexplored demographic groups.

Conclusion

If borrowers don’t have any credit score because they haven’t sought any loan or form of credit, traditional models may find it difficult to approve loans. On the other hand, credit scoring artificial intelligence models use alternative data points such as a borrower’s shopping habits, phone use, social media activity etc., to assess their behavioural traits and create a risk and earning profile. This diverse range of data helps lenders make a risk assessment of loan applicants who have no credit score.