India’s economic growth hinges on the success of more than 633.9 lakh micro, small, and medium enterprises (MSMEs) which account for nearly 30% of gross value added (GVA) in India’s gross domestic product (GDP).
Despite their valuable contribution, access to credit remains one of the major barriers for MSMEs, particularly unorganised MSMEs due to lack of sufficient collateral, repayment history, and other factors.
Traditionally, lenders have predominantly relied on data provided by credit scoring agencies like TransUnion CIBIL, Equifax, Experian, etc. for credit underwriting.
However, owing to the rise in credit volume, lenders need to revamp and update their credit underwriting models. With access to technology and granular credit data, lenders can streamline this process, cater to a wider customer base, and more importantly, reduce defaulters.
This article explores how lenders can leverage alternative credit data to improve decision-making during credit underwriting.
What Are Credit Underwriting and Alternative Credit Data?
Credit underwriting is an assessment model deployed by lending institutions to determine a borrower’s creditworthiness by evaluating different data points including employment history, age, income, credit history, etc.
While these data points remained the cornerstone of credit underwriting, access to alternative data is helping lenders make data-driven and informed credit decisions.
But, what is alternative credit data?
All data points, not included in the standard or traditional credit report that aid lenders evaluate a borrower’s creditworthiness are known as alternative credit data.
Alternative data sources include:
- Rental payments
- Alternative types of credit
- Income sources
- Bank account transactions
- Social media behaviour
- Online spending patterns
Upsides of Alternative Credit Data
More than 60% of survey respondents stated that they are exploring the potential of alternative data and harnessing analytical techniques to improve credit portfolio management. We expect more and more lenders to lean on alternative credit data sources without disregarding the influence of conventional data sources on credit decisions.
Here are a few compelling reasons why reliance on alternative credit data is set to increase:
Overcoming Pitfalls of Traditional Credit Scoring Models
Alternative credit data opens up new avenues for lenders to tap new customer segments. As the lending landscape becomes increasingly competitive due to the entry of modern Fintech companies, lenders can no longer be complacent and overlook the need to expand credit access.
Loan rejection rates are significantly high for thin-file customers with limited credit history as lenders predominantly favour applicants with strong credit history and transactional data.
While the narrative remains the same for borrowers with poor credit scores, these rejections also harm lenders as they can’t make credit accessible to good candidates who do not fit their “ideal credit profile”.
Alternative credit data helps lenders overcome this hurdle by providing a comprehensive view of a potential borrower’s finances beyond their conventional credit underwriting models. This approach allows them to reach a wider audience and serve new customer segments that were previously overlooked.
Innovative and Personalised Credit Tools
Nearly 68% of stakeholders in the banking sector said that they prioritise personalisation to attract new customer segments.
Financial institutions, particularly modern Fintech startups are redefining the Indian financial system catering to a wider audience, and offering personalised and transparent financial tools and services. The arrival of Fintech companies has created a stir in the Indian financial ecosystem compelling formal lending sources including banks to ditch their “one-formula fits all” approach and embrace personalisation.
Alternative credit data can help banks revamp and align their dated credit underwriting techniques with evolving customer financial requirements and preferences.
For example, payroll data, typically inaccessible to lenders offers a clear view of a borrower’s income and association with the employer. Lenders can harness the potential of payroll data connectivity and get real-time data including contract terms, salary history, etc., to provide innovative and personalised credit products.
Access to real-time information equips lenders to modify their product offerings according to customer preferences and thereby nurture customer relationships.
Replace Manual Processes to Minimise Fraud and Improve User Experiences
Alternative credit data accelerates accurate decision-making and also paves the way for easy applicant verification processes. As mentioned above, while access to real-time customer payroll data allows lenders to modify their offerings, it also ensures swift income, identity, and employment verification.
It is safe to say that redundant and time-consuming credit underwriting processes are either already automated or likely to be automated in the future. Automation will play a key role in driving better user experience and more importantly, minimise fraud.
Additionally, customers do not have to spend hours figuring out how to retrieve and upload critical documents manually.
Automated credit underwriting will also be crucial in eliminating manual credit scoring and building a standardised error-free credit management process. Despite operating in manual mode for decades, lenders are now increasingly in favour of replacing manual due diligence during credit underwriting. These include interviews with borrowers, field visits, manual know-your-customer (KYC), etc.
Parting Words
Lenders require sound and effective foolproof data strategies to stay competitive and attract new customers. As the Indian middle class becomes more affluent, spending patterns have seen a sizeable shift, creating a significant rise in loan applications over the past few years.
Additionally, lenders also need to find the right channels to reach the underserved and marginalised communities and make credit accessible to them.
Alternative credit data models backed by sophisticated technologies including machine learning (ML) and artificial intelligence (AI) have emerged as a viable solution to bridge the credit gap and cater to the dynamic financial consumer needs.
Finezza’s cutting-edge lending lifecycle management solution empowers lenders to take credit underwriting to a new level by analysing borrower data in seconds. Our Document Identification Framework provides a 360-degree view of a borrower’s transactional and non-transactional data to aid decision-making during loan disbursal.
Contact us to know more!
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