Welcoming you to the realm of credit underwriting, a process that’s been quietly experiencing an enormous boost due the artificial intelligence.
In the ever-changing industry of banking and finance, the credit subwriting is the foundation of lending. The traditional process was based on manual processes and old processes; underwriting is currently going through a subtle revolution powered by Artificial Intelligence (AI). This revolution isn’t only related to speed and scale. It is fundamentally altering how lenders assess risk, spot fraud, and make credit-related decisions.
This is part of the very first part of a series exploring the ways in which AI has changed the way that credit underwriting is done. We will dissect the fundamentals as well as outline the drawbacks of traditional methods and explain the ways that AI-based solutions like CreditAssist by Perfios help lenders create smarter, more rapid, and more diverse credit ecosystems.
Table of Contents
What is Credit Underwriting?
Imagine you’re a banking institution. Your task is to loan money; however, not with recklessness. It is important to determine the likelihood of someone paying you back. This is the role of credit underwriting.
In essence, credit underwriting is the method by which lenders determine the risks of loaning money to the borrower. It involves evaluating the borrower’s financial health, their repayment ability, credit history, and other aspects prior to the loan being approved.
For a long time, the traditional decisions for credit underwriting relied on a predetermined number of criteria, such as income proofs, credit scores, as well as collateral values, and, in many cases, documents to be checked manually. While this was a good system for a while, it has struggled to keep up with the increasing complexity and speed of today’s credit requirements, particularly in the emerging markets.
Challenges in Traditional Credit Underwriting
While it is a crucial part of credit lending, the traditional credit underwriting has encountered a number of ongoing problems. Here are a few of the most difficult issues:
- Time-consuming: Manually reviewing documentation and entry of data may slow loan approvals, specifically in high-volume credit underwriting settings.
- Limited visibility: Relying on conventional data such as credit scores is not a good way to exclude a significant populace, particularly MSMEs, as well as gig-workers that may not have a formal credit history.
- Subjective Decision-Making: Human biases and inconsistent standards for evaluation can cause inaccurate outcomes or missed opportunities.
- Fraud Risk: Forgery of documents, as well as identity mismatches and fraudulent financial information, could get past the manual review of credit underwriting.
- Compliance Pressure: The requirements for compliance change constantly, and making sure compliance is met without automation can be expensive and prone to errors.
There is an urgent need for a more fluid, flexible, and data-driven method.
A Quick Look Back: The Evolution of Credit Underwriting
Credit underwriting has advanced from relying on personal judgments in the 1800s to relying on standardised credit scores by the middle of the 20th century, and currently, we are in the age that is based on AI as well as machine learning.
To appreciate the extent to which we’ve come in the last few years, let’s reset the time!
Imagine the end of the 1800s. There aren’t CRIF reports or CIBIL scores, and definitely, there aren’t any bank statements PDFs. A banker in the area is sitting next to a borrower — let’s call him Raj– and is attentively listening to the pitch of the loan he needs to expand his business in spices. The banker is acquainted with Raj and his family members, and may even purchase his spices in the market. Based on this connection and a gut instinct that he has, he accepts the loan.
Underwriting was in its first form, highly personal and dependent on relationships; however, it was extremely unreliable.
The 1970s are the next decade. The financial world starts to become more formalized. Credit bureaus appear on the scene with structured scores for credit. Creditors are now using more uniform standards, like income proof, such as salary slips, and collateral, to make their decisions. Underwriting becomes more reliable and exclusive, excluding those with no formal footprint of financial history.
Then came the 2000s, the era of rule engines and spreadsheets. Banks began automating a portion that was involved in the underwriting process by using software that could apply fixed criteria to loans. But here’s the thing, they were only as effective as the rules they were programmed to. If a borrower did not tick each box.
AI, as well as machine learning, has entered the conversation. It’s no longer about simply ticking boxes. We’re discussing studying patterns, analyzing the unstructured nature of data, and forecasting the behavior of borrowers. The change is significant from a rigid, rule-based decision-making to a more intelligent, flexible credit underwriting that can change according to each borrower’s individual story.
Think of it as a transition from paper maps to a real-time GPS. Both will get you there. However, one can help navigate using nuance.
As we advance and move forward, the question isn’t more “Can this borrower repay?” but instead “What does the full story of this borrower’s financial life look like?” And thanks to AI technology, we’re in a position of being able to answer this question faster and more accurately than we have ever been.
How AI is Changing the Game For Credit Underwriters
AI makes credit-based underwriting a more complete and objective process. Here’s how:
Leveraging Alternate Data for Smarter SME Credit Decisions
Take a look at a mid-sized logistics firm with its headquarters in Jaipur, with a rapidly expanding footprint throughout North India. The company is able to maintain steady cash flow and strong relationships with vendors and is able to demonstrate regular compliance with the law. But, due to the lack of actual exposure to credit and an undocumented trail, the profile may seem “thin” to traditional underwriting models.
CreditAssist solves this problem by cleverly combining traditional financial data with a wide range of other data sources, like Bank statements, GST reports, patterns of cash flow, utility bill payments, as well as business registrations, geographical footprints, and transactions.
By cross-validating the information, for example, by comparing GSTR returns to revenue declared or reconciling vendor payment with balance sheets, CreditAssist builds a contextual risk-adjusted credit profile that is a reflection of the company’s financial health and its operational discipline.
This allows lenders to evaluate previously unaddressed borrowers with greater confidence!
Smart Data Access and Extraction in Credit Underwriting
One of the time-consuming elements of underwriting involves sifting and sorting information from several documents. AI systems are now able to collect data from bank statements, invoices, and scanned copies. With the consent of the borrower, data can be extracted directly from trusted sources such as Account Aggregators, Net Banking portals, and GST databases.
This reduces manual labor and guarantees complete data transparency in just minutes.
Comprehensive Financial Analysis
The true benefit of AI is not only in data collection, but in understanding the data! AI underwriting tools can calculate financial ratios, monitor patterns in cash flow that are seasonal and identify any irregularities in expenditure behavior, and evaluate borrower performance against industry benchmarks.
Importantly, Dashboards can be adapted to comply with internal and regulatory credit guidelines, allowing underwriters to stay in compliance without any burden.
Data-Driven Credit Underwriting & Decision Making
After the financial analysis is completed, AI systems generate detailed credit reports, which provide an all-around view of the borrower. These reports provide confirmed income streams, unsubstantiated (or suspicious) transactions, a score on creditworthiness, and loan recommendations that are customized in real-time.
CreditAssist by Perfios: Built for the New Era of Credit Underwriting
At Perfios, we’ve seen the firsthand way that lenders are searching for smart systems that can analyze, validate, and make decisions at scale.
This is the point at which CreditAssist, the AI-powered underwriting engine, is available. With years of experience in the field of financial data aggregation, CreditAssist makes use of:
- AI-Powered Answers to Query Assistance: Instantaneous responses to queries from underwriters with the help of NLP via an interactive two-way dialog.
- SmartQuery Prompts: Provides follow-up questions to improve the quality of decisions.
- Security Guardrails for Data Integrity: Avoids hallucinations by incorporating security for data and keeps AI responses reliable and tailored to the specific application.
- Multi-Document Reconciliation: Cross-validates financial metrics using a variety of sources to verify their precision.
- External Database Validation: Combines alternate data sources and open data sources to provide a more thorough assessment, with cross-validation for additional
- Indicators of Strength and Risk: Identifies anomalies by using transaction and other data analytics to determine the most important risks and strengths
- Fraud Prevention: Finds out if there are fraud patterns and discrepancies by analyzing cross-analysis.
- Regulatory Compliance: Assures regulatory conformity by thoroughly studying negative indicators that are present in filings
It doesn’t matter if you’re a bank that underwrites SME credit or an NBFC catering to employees on gigs. CreditAssist can help your team of credit professionals make smart choices faster without causing friction.
Conclusion: Underwriting Shouldn’t Be a Bottleneck!
If your credit departments are still struggling with spreadsheets, chase documents, or re-evaluating borrower profiles, there’s a problem. In today’s world of lending slowdowns, blind spots, and errors made by hand are a waste of opportunities.
CreditAssist from Perfios provides solutions that are clear and are provided through automated processes. We raise the level of underwriting using sophisticated scoring models, customizable guidelines, as well as live insight that assist your credit team in making informed, reliable decisions on a large scale.
You get fewer drop-offs. Smarter approvals. You can rest assured that your decisions don’t rest on speculation.