As the lending industry grows and lenders attempt to scale quickly, lending businesses are at greater risk of making poor decisions inadvertently.
Poor decision-making often results in the growth of non-performing assets (NPAs) and potentially funding fraudulent borrowers.
Therefore, lenders need access to various data points to make data-driven decisions. Merely evaluating income statements or proof of income statements is not enough to make a sound decision. Lenders need access to more comprehensive data when processing the full spectrum of loans, from personal and education to business loans.
This is where access to alternative data and its efficient analysis can be a game-changer for lenders. Adopting credit underwriting software can help streamline the analysis process, painting a realistic picture of a borrower’s financial status.
Let’s understand more about such software and its data-driven analysis capabilities.
What Is Credit Underwriting Software?
Credit underwriting software is an automated financial analysis solution powered by a mix of technologies, such as Artificial Intelligence, Machine Learning, robotic automation, Python, and optical character recognition (OCR).
- Lenders can feed all financial data into the software and automate the underwriting process, making it quicker and more accurate.
- The solution provides a creditworthiness score based on the data, which allows lenders to make quick lending decisions.
- These systems often integrate with external data sources (credit bureaus, financial institutions, public records) to provide a more comprehensive borrower profile.
- Underwriting software reduces human error by automating data entry and analysis and ensures consistent lending policy application across all applications.
Beyond Income Statements: The Need for Alternative Data
However, a key challenge is that income/ revenue statements alone may not be sufficient to offer an accurate score, especially if a business has no credit history and is a first-time borrower.
On the other hand, a business with a good credit history may inflate its financials through practices such as circular transactions.
Lenders must consider alternative data sources that can paint a realistic picture of a borrower’s financials to safeguard lender interests and capitalise on opportunities.
Use of Alternative Data In Credit Underwriting Software
Credit underwriting software can analyse the following alternative data sources to enable accurate underwriting decisions. Here’s how:
1. Bank Statements
Bank statements of savings accounts and current accounts provide detailed information on the cash flows of individual and business borrowers. However, manual analysis of such volumes of data can be labour-intensive and time-consuming.
There’s also a potential for errors and omissions. Credit underwriting software, on the other hand, leverages OCR technology to extract all the data and categories into inflows and outflows and sub-categories such as salaries received or paid out, EMI payments, penalties for late EMI payments, office expenses, revenues, and interest generated, to name a few.
Such micro-isolation of data into over 60 categories helps lenders understand consumer habits, behaviours, and real-time cash flows.
2. Credit Score & History
Today, credit bureaus record a borrower’s record in detail. Lenders can access a potential borrower’s in-depth credit history, including credit utilisation ratio, EMI repayment history, and credit usage patterns.
This data can be fed into credit underwriting software to consolidate a borrower’s financial status further.
3. Goods & Services Tax Return (GSTR)
Borrowers with a yearly income of over INR 20 lakhs must register on the GST portal, have a GST number and file GSTR regularly. Hence, a borrower’s GSTR is another popular data point due to its significance.
For instance, cross-referencing GSTR data against revenue-related bank transactions can help lenders confirm revenues a borrower declares.
Advantages of Superior Credit Underwriting Software
Here is a list of the key benefits delivered by credit underwriting software, which can be leveraged by banks, non-banking financial companies (NBFCs), and digital lending platforms:
1. Automatic Migration of Alternative Data
With the growth of the bank aggregator system, credit underwriting software can swiftly and securely access a borrower’s data with the borrower’s consent. The software can also directly pull data from the GST portal with the borrower’s consent.
Hence, the lender can process loan applications with comprehensive data, reducing the back and forth between applicants and lenders to fill in gaps.
2. Superior Risk Assessment Capabilities
Credit underwriting software can analyse a voluminous amount of data, categorise it into relevant inflow and outflow categories, and detect unusual transaction patterns that raise red flags.
The data is organised and analysed, and the solution delivers a creditworthiness score that measures the likelihood of a borrower repaying a loan timely and in full. Accurate risk assessment is a key benefit of the software.
3. Cross-Referencing GSRT data
Credit underwriting software comes with GSTR cross-referencing capabilities, which automate cross-checking every tax return with the corresponding revenue-related transaction in a bank statement.
4. Inclusive Credit Solutions
Analysing alternative data enables lenders to move away from traditional lending models that limit them from giving loans to individuals and businesses without a credit history or assets.
Lenders can now offer loans based on borrowers’ real-time cash flows and current and potential revenues.
For example, they can facilitate invoice financing for smaller businesses that must fund inventory to fulfil orders. Credit underwriting software can also recommend customised solutions based on a business’s real-time funding needs, thus reducing the cost of loans.
5. High Priority For Data Security
Financial data is susceptible, so there is always a risk of data leakages. As lenders collect more borrower data, they must ensure it is processed, stored, and managed securely.
Superior credit underwriting software prioritises the privacy of all data, ensuring that it is stored and managed securely.
6. Data-Driven Decision Making
Adopting credit underwriting software empowers lenders to shift to a data-driven underwriting culture. Data can be leveraged to power individual lending decisions.
However, the software’s AI capabilities can forecast trends based on historical and current borrower data.
For instance, access to data enables lenders to build a more customer-centric debt recovery and collections strategy that retains borrowers while quickly recovering debt.
Key Takeaway
A credit underwriting software can work with a wide range of data points. This approach makes the lending process inclusive while simultaneously making the underwriting process comprehensive.
Lenders can expedite the loan approval process, make data-driven decisions, and lay the foundation to build a thriving, sustainable, profitable lending business.
Finezza, the cutting-edge micro loan management software, streamlines lending processes. It automates applications, credit assessments, and approvals, ensuring security and compliance. It also harnesses cutting-edge AI technology to provide tailored analytical solutions, enhancing loan decision-making, reducing lead-to-loan time, and effectively evaluating creditworthiness for faster loan approvals.
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