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Scope for Alternative Lending

Lending institutions traditionally carry out credit assessment on the basis of rating parameters, and in most cases, the gut feeling of the underwriter/credit evaluating officer. A typical loan origination system (LOS) would furnish the requisite insights, enable the credit evaluator with the desired set of insights, and offer a platform to optimize the workflow. While this is an effective system, it leaves a major portion of borrowers out of the credit system because they do not qualify for the criteria stipulated by a financial institute (FI) / lender.

According to recent research by Singapore-based Robocash Group (provider of robotic financial services in the field of alternative lending and marketplace funding), India has strong potential for growth in alternative lending (need stands at 0.5 on a scale of 0 to 1), along with the highest opportunities across all countries analyzed. In fact, India accounted for the largest share of the alternative lending market in South Asia, at 81.3% in 2018. The RBI has also echoed similar thoughts through its Unconventional Monetary Policy in Times of COVID-19; published during FY 21-22.

The Potential of Alternative Lending

Bringing credit facilities to individuals who are not considered credit-worthy by the traditional lending system offers huge potential for NBFCs. So much so that the total transaction value of alternative lending in India is expected to touch the $93.05 million mark in 2022 and rise to $85.34 million by 2027. Traditional lenders need to comply with stringent RBI regulations regarding pricing guidelines, which makes it difficult for them to reach borrowers that non-traditional FIs can.

The use of alternative data and unconventional credit rating mechanisms allow lenders/FIs to widen market penetration by enabling credit access economy-wide. Some of the noteworthy components of alternative credit rating systems include visionary business models, coupled with risk appetite, cutting-edge technology platforms, use of the latest technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) in the underwriting process, and strategic risk management.

Alternative FIs and lenders can penetrate deeper into the market with their fast credit approval processes, and technology innovations, such as AI & ML-powered online platforms, which streamline operations and enhance the customer experience, thereby fuelling growth.

In its Working Committee Report, published in November 2021, the RBI extensively discussed the global scenario, with examples of Person-to-Person (P2P), Person-to-Business (P2B), Business-to-Person (B2P) and Business-to-Business (B2B) lending models. This report also emphasized that following the financial crisis brought on by the COVID-19pandemic, the Indian financial market has undergone a substantial technological transformation.

Technology has fortified the underwriting and credit evaluation procedures, enablingalternative credit scoring, which in turn facilitates credit access to untapped borrowers. In particular, AI & ML-enabled algorithms, cloud-native technology, and incorporation of micro and macroeconomic parameters in statistical & behavioral models have benefited hundreds of FIs and lenders in capturing an untapped market of borrowers. These technologies are platform agnostic, while allowing incredible agility to adapt to the changing business environment.

First Payment Bank is a prominent example in India. The firm has swiftly enabled credit access to low-income groups without resorting to the brick-and-mortar mode.

The pandemic speeded up digital transformation across industries, especially in financial processes, making alternative lending more transparent, reliable and attractive to the customer, as compared to the traditional route. In addition, technological advancements have armed FIs/lenders with powerful platforms that enable quick lending decisions via algorithm-based checks. Such checks can be completed in minutes, if not seconds, allowing loan approval and disbursal almost instantaneously. Buy Now Pay Later (BNPL) is a prime example of this, offering credit at the point of sale.

The Digital Demand in Retail Banking study, conducted by Oracle on 5,200 customers across 13 countries, revealed that 40% of the customers believe that non-banking financial institutions can offer better assistance with their investment and personal money management need. It is the ability of alternative lenders to leverage technology that allows them to offer efficient credit services to the under-served populations. This helps them better penetrate the markets.

For instance, the alternative credit evaluation process requires much less paperwork, low or no credit scoring and quicker risk analysis, resulting in customer delight. Robust automation leads to instant loan processing with minimal human intervention.

Managing Risks in Alternative Lending

The boom in eCommerce and the rising popularity of BNPL offer exciting opportunities for alternative lenders, merchants and consumers. The seller not only receives instant payment, but they can also drive bigger ticket purchases. On the other hand, today’s aspirational consumer has affordable solutions that enhance their purchasing power.

However, alternative lending also presents risks for the lending institution. For instance, warning signs of financial difficulties among customers might get masked by some types of consumer behavior, such as the use of BNPL to buy household essentials. Also, how does one judge whether a customer has over-committed themselves? This is where the need for smart, data-driven decisions can prove invaluable. Risk and other data analytics can provide valuable insights regarding potential customer groups as easily as they can raise red flags that allow lenders to take pre-emptive steps.

The futuristic path opened by alternative credit lending is underlined with critical insight. At CRSPL, we offer Enterprise Risk Solutions, such as customer segmentation and AI/ML-driven application scorecards. The customer segmentation approach identifies personal details at the micro-level, allows grouping on multiple levels, uncovers leading factors, and allows for expert judgment overlays.

Application scoring, on the other hand, attempts to predict a customer’s default risk at the time of credit application, based on information such as the borrower’s demographics and credit bureau records. Historical data from past applications and their performance with the FI/lender are statistically analyzed and an “Application Scoring” equation is developed to evaluate new applicants. These solutions pave the way for FIs/lenders to offer capital to small borrowers and merchants.

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