In financial services, what differentiates mediocre lenders from great lenders is usually the strength of their underwriting. In the long run, better underwriting translates to better loan performance, resulting in sustainable growth and more profitability for financial institutions. 

By harnessing the power of data-driven underwriting methodologies, such as credit scoring and automated decision-making systems, lending organisations such as banks and NBFCs can streamline their processes and make more informed credit decisions.

In this piece, we will dive deep into the process of underwriting, showing you leveraging the right technology can  improve your lending operations. We will also explore fundamental concepts like credit risk assessment, benefits of alternate data underwriting, and regulatory compliance.

What Is Underwriting?

Underwriting is the process by which financial institutions assess risk and determine the terms of a loan or insurance policy. Underwriters analyse data like income, debt, assets, and credit history to determine the likelihood of default or claims. If the level of risk is deemed acceptable, the underwriter approves the application and sets the interest rates, loan terms, EMI, and/or premiums accordingly. 

For example, someone with a high credit score and stable income may qualify for a lower mortgage interest rate. By accurately assessing risk, underwriting helps ensure that loans & insurance are priced correctly and that financial institutions remain solvent.

How Does Underwriting Work?

Underwriting is an essential step in the lending process to evaluate a borrower's financial capacity to repay a loan. It usually involves assessing four significant elements: income, appraisal, credit score, and assets.

Here is an in-depth look at  each element:

1. Income

Here, the borrower’s income is evaluated to estimate whether they can cover the monthly payments for a loan. Analysing income and expenses also gives lenders a look into their debt-to-income ratio or their income-to-expenses ratio. This ratio is instrumental in deciding whether a borrower can afford to repay the loan or not. In addition, underwriting may include the stability of the borrower's job, length of employment, and other sources of income, including rental properties and investments.

2. Appraisal

In the case of secured loans, underwriters must ensure that the collateral guarantee is worth more than the requested amount. An appraiser assesses the property (or other assets) or evaluates the purpose of the loan to collect determining information like the viability or quality of the investment. The appraisal process ensures that a borrower can only secure a loan worth the property's actual value. For mortgage applications, loan underwriting appraisals assess the home's value.

3. Credit score

A credit score provides insight into a borrower's current financial standing. Credit scores also provide a borrower's debt-to-income ratio, assessed to estimate whether they can repay the loan & any existing debts. A good credit score may help a borrower benefit from a lower interest rate.

4. Assets

Assets refer to all the items borrowers can sell if they cannot repay their loan. Asset evaluation may include assessing the borrower's savings accounts, stocks, commodities, & real estate. These items act as a security net for lenders, as they are guaranteed reimbursement if the borrower cannot repay the loan.

What can Lenders do with Dynamic, Real-time Underwriting?

80% of the data underwriters receive is unstructured, in emails, PDFs, forms, & images. This makes extracting meaningful information a challenging task, reducing the efficiency of the underwriting team and  increasing the time required for risk assessment. Adopting dynamic, real-time underwriting using AI and  data analytics can help streamline the process, improve accuracy, and  enable faster policy decisions. 

Let's look at some of the critical benefits of dynamic, real-time underwriting:

1. Customise Product Experience 

By using dynamic, real-time underwriting, lenders can obtain detailed information about a customer's risk profile. It helps lenders customise product experiences, allowing them  to offer personalised credit products based on customer needs & the underlying risk factors.

This leads to improved customer experience as well as better closure rates for lenders. 

2.  Improved Risk Assessment 

AI-driven underwriting solutions can quickly convert unstructured data into structured information, helping lenders understand borrowers' risk profiles. This helps them make a calculated decision and prevent any potential losses. ML-based underwriting also automates the data analysis process and  reduces manual errors..

3. Easy & Automated Documentation 

Dynamic, real-time underwriting solutions can automatically extract essential information from documents like contracts and forms. This eliminates the manual effort required to do the same task, improving accuracy while reducing processing time. AI-driven underwriting solutions also offer document management solutions, enabling lenders to store all the documents required for risk assessment in a secure cloud-based repository.

Traditional Underwriting vs. Alternative Data Underwriting

Traditional and alternative data underwriting are different approaches to assessing a borrower's creditworthiness. Traditional methods have been the norm for decades. They involve evaluating borrowers' creditworthiness based on their past credit history, income, and other factors. However, alternative data underwriting has emerged as a game-changer in recent years, especially in emerging markets like India.

This shift in approach is driven by a need to address long-standing gaps in traditional banking that have left a large proportion of the population without access to credit. According to a report by CIBIL , it is estimated that around 480 million adult Indians, half of the total earning population, are currently 'credit unserved.'  

This is a significant issue, particularly for small businesses in the MSME sector, who have historically faced challenges in obtaining credit. A study by IFC found that SMEs represent only 6-7% of total credit in India, leaving a significant funding gap.

Coming back to underwriting, there are key differences between the traditional and alternative approaches: 

• Traditional underwriting relies heavily on factors such as credit score, income, and employment history, which can limit access to credit for individuals and businesses that do not meet these criteria. 

• In contrast, alternative data underwriting leverages new and innovative data sources to assess creditworthiness accurately. This can include digital footprints, social media activity, utility bill payments, and other non-traditional data points.

While traditional underwriting has been a reliable approach for many years, alternative data underwriting offers new financial inclusion and innovation opportunities. By leveraging new data sources and  approaches, it is possible to address long-standing gaps in credit access and provide better financial outcomes for individuals and businesses alike.

Why Should Lenders Choose Alternative Data Underwriting?

India's digital lending market recorded a 132% CAGR between 2017 and 2022 and is expected to reach $350 billion in value in 2023. Using alternative data sources in credit underwriting has become increasingly important in the Indian lending landscape, as much of the population remains either credit-underserved or unserved. Here are the benefits of alternative data underwriting:

1. Opens Up New Borrower Segments

By adopting alternative data underwriting, lenders can tap into a previously untapped pool of potential borrowers. India has a vast population of individuals with no credit bureau data, making it difficult for them to access formal credit facilities. However, they may have other data available that can indicate their creditworthiness, such as their payment history for utilities, mobile services, or credit for specific products like insurance or rent. 

Moreover, social media usage, online transactions, and online shopping behaviour provide critical data lenders can leverage. Using alternate data sources, lenders can make better credit decisions, expand their customer base, and provide access to formal credit facilities to a larger population segment.

2. Helps Improve Gini Coefficient 

The Gini Coefficient is a crucial metric for assessing an underwriting model's discriminatory power & predicting the likelihood of future borrower defaults. When enriched with alternate data, underwriting models are better equipped to differentiate between borrowers who pose a higher risk & those more likely to repay loans on time. 

In fact, an improvement in the Gini coefficient of one percentage point in a default prediction model can save a typical bank up to USD 10 million for every USD 1 billion in underwritten loans. 

3. Facilitates Tailored Products & Services

By analysing data on a borrower's financial history, payment patterns, and  other data sources, such as utility and telecom bill payments, lenders can better understand their creditworthiness and customise their offerings based on their unique needs. This means lenders can offer longer loan tenures, optimise interest rates, or adjust credit limits to suit borrowers' repayment behaviour. 

Utilising alternate data sources in underwriting facilitates a more personalised approach to lending, delivering optimal value to both borrowers & lenders.

4. Enables Risk-Based Pricing 

With alternate data, lenders can better assess the creditworthiness of the borrowers based on a complete risk profile, enabling them to price loans based on the level of risk involved. This method of pricing loans ensures that borrowers pay interest rates that match their risk level instead of a one-size-fits-all approach where all borrowers are charged the same interest rate, regardless of their creditworthiness. 

5. Aids Collections 

Besides the borrower assessment, FinTechs, NBFCs, and banks can now rely on AIML, and alternative data to automate their collection processes The latter comes from various sources, such as social media, insurance records, and device data. By leveraging a collection & prioritisation engine , lenders can segment borrowers based on risk level and allocate collection resources smartly. This strategy helps identify risky borrowers before their loan is due, allowing for targeted collection strategies.

Additionally, the prioritisation engine's recommendations aid in presenting an alternate eNACH (Electronic National Automated Clearing House) date  for bounced payments. In short, by using alternate data in the collection process, lenders can avoid hiring & training personnel, minimise occurrences of borrower harassment, save on costs, & significantly boost debt recovery rates, making it an incredibly viable strategy for lenders worldwide.

Wrapping Up!

Implementing alternative data sources in lending revolutionises credit assessments and enables lenders to make better decisions. By opening up new borrower segments, improving the Gini Coefficient, facilitating tailored products, helping risk-based pricing, and aiding collections, lenders can gain a competitive edge in the market & ensure their customers have access to the best credit facilities for their needs.

DeviceConnect by FinBox can help lenders with the alternate underwriting process by providing a holistic view of borrowers' financials and behavioural predictors. It leverages over 5,000 parameters across millions of data points and markers to generate a FinBox Inclusion Score that helps lenders accurately underwrite borrowers across various cohorts and ultimately price products in real time with superior understanding. 

Request a demo today & see how DeviceConnect by FinBox can supercharge your underwriting processes and make them much more effective  & efficient.