Synopsis: This article discusses the need for elevating the traditional credit underwriting process by deploying a human-machine collaborative approach to envelop the credit unserved and underserved in the formal lending ecosystem. 

The Indian lending market has a credit gap problem—a colossal $250-300 billion at that. Even the share of medium enterprises in bank credit has fallen to 4.3% in 2020; it was 13.5% in 2007.  

This trend persists in spite of the government’s massive push to increase the penetration of bank accounts among the masses. As per EY’s research, the primary impediment to credit growth is the limited ability of financial lenders to determine a borrower’s creditworthiness. 

The over-reliance of traditional lenders, including banks and NBFCs, on credit scores to inform their lending decisions has restricted access to credit for many, increasing the credit gap and plaguing the growth of the economy.  

So, how are lenders evolving their credit underwriting processes to lend better?  

The Rise of Alternative Scoring Models 

Due to the proliferation of smartphone penetration and plummeting data rates, consumers and businesses alike are increasingly coming online, creating their own digital footprint. Consequently, it has opened access to alternate sources of data, such as eCommerce payments, asset ownership, utility payments, public records, social media metrics, and much more. 

Moreover, with the rapid transformation in the technology space, particularly a surge in artificial intelligence (AI) and machine learning (ML) based models, the lending ecosystem has been undergoing a paradigm shift. 

Now, lenders have been innovating with their credit scoring models by employing AI/ML models that use alternative data to better gauge a prospective borrower’s creditworthiness, broadening their risk assessment frameworks. These proprietary models have vastly improved the credit assessment process by automating data collection and evaluation, improving loan accessibility and availability to new-to-formal-credit (NTFC) customers. 

Additionally, with a rise in blockchain-based lending, borrowers are assured of the privacy and safety of their personal data while reaping the benefits of a faster and more seamless lending experience. 

How are Alternative Scoring Models Improving Credit Penetration? 

Unlike traditional lending models, where banks manually parsed through documentation to understand a potential borrower’s repayment ability by scanning for past payment records and income history, AI/ML-driven models have significantly improved the quality of the data. 

These models go beyond the typical financial figures by analyzing the spending patterns of customers, current cash flows, smartphone GPS data, and even psychological factors to gain a superior understanding of a borrower’s creditworthiness. Resultantly, customers can access loan products that are tailored to their requirements despite a lack of thick credit history. 

Besides, as these ML-driven models are trained on alternative credit data, they undergo constant upgradation and retrofitting as more data becomes available based on customers’ performance. This further helps reduce loan defaults and provides lenders with actionable insights to improve their credit processes. 

Indeed, these models have deepened the Indian credit market, with the digital lending market already valued at $270 billion in 2022. It is expected to rise by almost 5x to a massive $1.3 trillion by 2030. And Protium, with its over Rs. 5,000 crore loan book, has been trailblazing this stupendous credit growth. 

How is Protium Revolutionizing Credit Underwriting and Loan Disbursals? 

Our engineering finance philosophy, which is a cohesive collaboration of risk, tech, and data analytics, has been creating ripples in the lending landscape in multiple ways. 

Transformative Proprietary Tech Models 

Protium has developed a proprietary engineering platform called Turiya, which serves as a multi-product, omnichannel LOS supporting a wide range of loan products catering to different risk types, from personal loans for digital-only consumers to business loans and equipment finance for MSMEs. Turiya’s decision-making process works via two broad approaches: algorithms implemented via a proprietary decision engine and automation with human intervention at the appropriate steps of underwriting. 

Our product mix demands both algorithm-based and highly automated assessment of underwriting. To comply, we have managed to create and operate separate customer journeys by customer segments or loan types without increasing the system complexity. 

To illustrate, the loan disbursal journey for customer “A” applying for an INR 3 lakh business loan will be very different from customer “B” looking for INR 50 lakh worth of equipment financing. Similarly, the evaluation metrics and loan terms will vastly vary across customers in Tier 1 and Tier 3 cities. 

Such continuous customizations to provide bespoke loans to customers generally result in increasing system complexities, which may lead to unforeseen system failures, also known as software entropy. However, as a risk-focussed lender, Protium has managed to nip these systemic issues in the bud with its superior design choices and intuitive algorithms. 

The Human + Machine Blend Advantage 

Even though the lending industry has increasingly become digital, with several financial lenders walking down the 100% digital-only lending approach, this is far from optimum. After all, processes involving property valuation, loan servicing, and business inspection require their fair share of customer interactions for a better determination of a borrower’s creditworthiness. 

This is precisely why we, at Protium, have maintained a solid offline presence by establishing 85+ branches in over 69 cities to complement our digital processes. This enables our customers to seamlessly alternate between physical and digital processes in our lending stack as per their requirements. Best of all, our local branches help localize content and loan products to the borrower’s native tongue, making onboarding and credit evaluation easier. 

Bottom Line 

New-age lenders and financial institutions are automating their credit underwriting processes by deploying AI and ML-driven models to increase credit off-take, minimize default risk, and improve their risk assessment processes. 

But Protium has taken this credit revolution a step further by basing its lending decisions on its engineering finance-based proprietary tech models. You can experience this transformative journey firsthand through the Protium app. Drawing on best-in-class risk models, our Protium app shares exclusive pre-qualified loan offers customized to your profile. It aids you in your financial decisions by offering extensive banking insights and smart financial tips. You can even check your credit scores for free without any negative impact for taking your loan decisions. So, wait no longer, download the Protium app, and begin your quest for long-term growth and success.