Many times, many of us have either been elated or sad looking at our credit score. This happened to us when we were looking for a loan or business funding or just wanted to understand our financial credibility. Credit scoring is an integral aspect of today’s financial world. As defined by Investopedia, credit scoring is a statistical analysis performed by lenders and financial institutions to access a person’s creditworthiness. Credit scoring is used by lenders to help decide on whether to extend or deny credit. A person’s credit score is a number between 300 and 850, 850 being the highest credit rating possible. A credit score can impact many financial transactions, including mortgages, auto loans, credit cards, and private loans. 

If we recall how it used to work traditionally, was that our fathers or grandfathers had good terms with then bankers or maybe knew someone who could put a recommendation for them or in other scenarios present their case to the bank. With time, banking eventually became an indispensable part of our lives; however, credit scoring became more of a mystery or an algorithm that most of us were unable to decode. 

Today this process has transformed into an all-new stream which is FinTech. The emergence of financial technology has illuminated the financial sector just like colour TVs lit up the entertainment world and our visual experience. Today service industries are more focused on providing an unmatched experience to their clients or customers. Additionally, with rising consumer awareness and regulatory framework, the customer today wants an explanation to everything and even for a rejection, be it a loan, an account, or a credit card.

Inline to that even FinTech has gone a notch up with bringing artificial intelligence (AI) to augment the whole effort. Things are finally changing for better as with AI, the shroud covering the credit scoring will be off. Moreover, there have been many instances in the past, where financial institutions that were using conventional ways of credit scoring had bad experiences or losses of huge magnitudes. The errors of judgment or an error of data entry might lead to irreversible damage. So this had to be resolved and changed without a doubt. The traditional scorecards used factors such as payment history, debts, credit history, types of credit, and others. This methodology completely relied on the history of the applicant and not on the present or the future prospects which made many people miss on the financial associations just due to a small old batch on the financial history sheet. Sounds unfair right!

AI in this specific area is expected to help banks and creditors in servicing borrowers based on their creditworthiness. Here alternative data comes to action with digital footprint being the key. Digital footprint brings out details around the online behaviour of a person based on what all sites he visits, what searches he made in the past, his purchase patterns, his wishlists, and not to forget his posts on Instagram, Facebook, Pinterest, LinkedIn or any media post for that matter. In this process, we are constantly being watched as a part of the assessment for the likeliness of our capability of paying back our loans or any financial obligation.

SalaryDost, a startup established in 2018, in Mumbai, has a very innovative credit scoring system that helps its customers to get a loan in no time. Their system is designed to fill in the gaps between the financial sector and customers by promoting open banking lending options to even those who do not have a relationship with the bank. 

FICO, (Fair Isaac Corporation), is a data analytics company based in San Jose, California focused on credit scoring services. As per the company, AI-based services are going to surpass the traditional methodologies in providing credit approvals to around 79 million Americans who are still out of the mainstream credit cycle. The new scoring mechanism using AI is all set to now approve and boost the loan approvals. As per them, the humongous numbers of pending files will be benefitted in terms of improved scores. The company finds it impossible to ignore so many hopeful applicants and that too in such a competitive environment. 

A big advantage of using AI-based credit scoring systems is that they are capable of digging out the aspects and information around an entity that looked meritless to the conventional credit scoring systems. These new softwares allow financial institutions to unearth details about potential customers from the web and then use predictive analysis in deciding their creditworthiness.

CreditVidya, a Hyderabad-based data underwriting start-up, provides credit score to first-time loan seekers and borrowers using more than 10,000 data points. The company works with several big players such as ICICI Bank and HDFC Life, among others, to provide alternate data to people who do not have much credit history or those who are unbanked and do not possess any credit data. 

Cashe, another Mumbai-based FinTech company, is working towards making it easier for salaried professionals to fetch a loan. They use live data and then with AI and ML, they run a verification process for the details provided by the applicant. In case of any discrepancies in the basic identification or professional information, the request is right away rejected. Additionally, this helps them understand the repayment capacity of the person. They create a Social Loan Quotient for each applicant which Makes it pretty easier for a future loan or an upgrade. Some third-party credit information companies are Equifax, Experian, TransUnion CIBIL and FICO are also transitioning towards making it an AI and robotics-based working.

IT giant Infosys believes that “robots can take over the credit scoring functions”. One such product is GiniMachine which offers a bot that can predict credit behaviour based on existing customer data. The company states that due to the unavailability of sufficient data for credit scoring; nearly 20% of US customers do not have enough data to generate a credit score, hence without AI pitching in this domain surviving the rising competition is difficult.

Data mining works alongside machine learning in specifying what indicators to look for as the decisive factors for differentiating between a responsible and a risky client. There are many software in this category with all of them working differently in terms of adopting various decisive indicators and weighing them differently. Several use predictive analysis or natural language processing in doing the same task, then the data produced by them is churned in the machine learning algorithms of the software. These software are trained by feeding them huge historic data of borrowers which includes their full-fledged profiles and all other relevant information or data points. This algorithm decides on what indicates a risky profile or a safe one. 

Owing to the changes bought by AI there is much more transparency and a wider spectrum of clientele. For the fact that these systems keenly look for the current status of the individual ie current income, the stream of work, profession, business prospects, and recent credit details there are more and more dreams being fulfilled. People dare to dream, to work on them, bring changes in society try new things. It’s fairer now as with AI enabling banks to better explain its customers about the details in cases of rejections of their requests or other credit decisions. Sounds like we are a bit (though a tiny one) closer to a more inclusive and fair world.