Digital lending has emerged as a game-changer in the fast-expanding financial services sector, offering borrowers convenience and accessibility while injecting efficiency and precision into the loan process. The Indian digital consumer loan market is expected to nearly triple in size by 2030, thanks to various favourable socioeconomic variables and timely legislative actions.
In another report by McKinsey, automation can save banks up to 50 percent on origination costs. Automation of underwriting and decision-making is a critical component of this revolution. This technology is transforming lending by speeding up the application process, improving risk assessment, and ultimately benefiting both lenders and borrowers.
How End-To-End Automation Will Work
Consider Raj, a borrower who asks for a Rs 2 lakh personal loan through a digital lending company – let's call it FastLoans (for reference purposes). The firm collects and analyses his data, which includes his credit history, income, and employment verification. To analyse credit risk, the automated underwriting process employs machine learning models.
Based on his solid credit history, secure work, and low debt-to-income ratio, the model forecasts that Raj will not default. His application is approved in minutes by FastLoans' automated decision engine, which follows predetermined rules. FastLoans then transfers Rs 2 lakh to Raj's bank account shortly after.
Now let’s take an elaborate look at this journey and explore the processes of the automated underwriting and decision-making of FastLoans that will help customers like Raj in obtaining a loan.
1. Data collection and processing
Lenders gather massive amounts of information about prospective borrowers. This data may contain personal information, financial history, credit reports, income statements, and other items. Now, FastLoans collects information on applicants like Raj such as their income, employment history, credit ratings, outstanding debts, and the purpose of the loan.
2. Data Integration
Data is integrated from a variety of sources, including internal records and external databases. The integration procedure entails cleansing and standardising data for analysis. FastLoans combines Raj’s data from credit bureaus, bank statements, and employment verification sources to produce a thorough picture of him as a genuine applicant.
3. Risk Assessment Models
Machine learning algorithms of FastLoans that are used to create predictive models would evaluate Raj’s credit risk. These models consider a variety of criteria, such as historical repayment behaviour, economic indicators, and demographic data. FastLoans uses these models to forecast the likelihood of default in Raj’s case. A logistic regression model of FastLoans for example, could be trained to estimate the likelihood of any future applicant defaulting on a loan.
4. Real-time Data Access
Digital lending platforms frequently have real-time access to data sources, allowing them to get the most up-to-date information. FastLoans would promptly access Raj’s current credit score, employment status, and bank account balances, guaranteeing that their decision-making process is based on the most up-to-date information.
5. Automated Decision Engine
To make lending decisions, an automated decision engine evaluates all acquired data and the output from risk assessment models. The automated decision engine at FastLoans evaluates Raj’s credit risk score, the amount sought, and the purpose of the loan to determine whether to approve or deny his loan application.
6. Regulations and Policies
Lenders establish criteria and standards that the automated decision engine must adhere to. Minimum credit scores, loan amounts, and permissible debt-to-income ratios are examples of such restrictions. The guideline of FastLoans, for example, would disqualify Raj if his credit score falls below 600.
7. Approval and Disbursement
The decision engine may make rapid lending choices based on the analysis and established regulations. If the application is granted, FastLoans will transfer funds immediately to Raj’s bank account within hours of submission.
The Outcomes
In the above journey, automated underwriting and decision-making would enable Raj to receive a loan in a matter of hours, rather than the days or weeks that traditional lending procedures require. The entire process is quick and depends on data-driven, computerised assessments of his creditworthiness.
Hence, it is safe to conclude that in India, the future of automated underwriting and decision-making looks promising. These significant innovations continue to redefine the financial environment, making credit more accessible and responsive to the requirements of today's fast-paced, data-driven society.
It is also important that the process is safe, non-discriminatory, based on informed consent, and there are built-in human-in-loop processes to address grievances.
As our digital infrastructure and data collection capabilities expand, automated underwriting will become more efficient and accessible, making financial services more inclusive and accessible to a larger population, thanks to the surge of various digital lending institutes booming in the Indian market.
Sonal Patni is Chief Technology Officer, Niyogin Fintech Limited. Views are personal, and do not represent the stance of this publication.
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