Today, Artificial Intelligence (AI) plays a transformative role across various sectors. In financial services, a recent survey, for instance, indicated that 13% of banks are already leveraging AI in lending operations to boost efficiency and performance.
Over 50% of the surveyed banks aim to invest $100,000 or more in machine learning-driven credit models over the next three years.
One of the top areas where lenders aim to bring in more accuracy and efficiency with the use of AI is the credit underwriting process. For instance, lenders can analyse multiple borrower data points rather than merely relying on credit scores. This approach can help lenders boost the number of good loans by as much as 215%.
In this blog post, we outline key challenges in the underwriting process and how the adoption of AI can help lenders drive inclusion, revenues, and profitable operations.
What is Credit Underwriting?
Credit underwriting is an analytical process that enables lenders to review a borrower’s financial position. Based on the results, they can predict if the borrower has the capacity to repay a loan in full and if they can adhere to the EMI terms and conditions. This is a critical step in the lenders’ loan review process.
However, as the number of loan applications increases, lenders may find it challenging to underwrite quickly and accurately. As lenders scale, they will need to adopt a streamlined, data-driven, and comprehensive approach to the underwriting process.
This is where the adoption of AI can help lenders address multiple challenges of loan origination and management.
4 Key Challenges in Credit Underwriting
Here’s a snapshot of the current difficulties faced by lenders when it comes to credit underwriting:
1. Large Volumes of Data
With the growth in digital transformation, lenders are inundated by the sheer volume of data. The amount of time needed to manually analyse all the data is significant and requires extensive human resources. This approach can also lead to errors and omissions.
2. Lack of Credit History
First-time borrowers may not have a credit history. This reality may exclude them from the organised credit ecosystem. The lack of alternative data can disqualify potentially favourable borrowers from becoming customers. This scenario often translates into an opportunity cost for lenders.
3. Huge Number of Applications
A 2023 study found that 71% of consumers prefer digital loan applications over traditional methods. Access to digital platforms has opened up the floodgates of loan applications to lenders.
In turn, lenders are struggling with a lack of capacity to process loans quickly and efficiently.
4. Wide Spectrum of Borrowers
The profile of borrowers is diverse, ranging from small, medium, and large size businesses to retail borrowers who may be self-employed or working full-time. The type of loan and ticket size may also vary.
ICRA, for instance, estimates that the financial year (FY) 2024 saw a 16% growth in credit, with demand led by unsecured loans of a small ticket size.
However, lenders continue to offer standardised products that prove unaffordable to a spectrum of borrowers.
5 Benefits of AI-powered Credit Underwriting
Now, let us explore the key advantages that AI offers lenders in credit underwriting:
1. Efficient Data Migration and Extraction
The financial data of borrowers comes from multiple sources in the form of documents. The extraction of raw data and its assimilation into a usable form is a complex and time-consuming process.
However, AI automates the migration, extraction and organisation process.
- A superior AI-powered financial analysis software directly pulls data with the consent of the borrower.
- Such software tools can extract financial data from over 700 document formats.
- The extracted data is then organised into the relevant transaction inflow and outflow categories swiftly, efficiently, and accurately.
Also, note that 35% of lenders cite repeated data entry, and 33% highlight inefficient document gathering as major obstacles in loan origination, leading to data omission and duplication.
With AI, lenders can tackle these issues and process loan applications as they flow into a lender’s ecosystem.
2. Effective Use of Alternative Data
The use of AI-powered financial analysis software enables lenders to evaluate alternative data more effectively. These include:
- Bank statements
- Credit score and history
- Goods and Services Tax Returns (GSTR)
Lenders can, for instance, cross-reference GSTR with revenues to ascertain the cash flows of a first-time borrower business. This approach paves the way for lenders to cultivate an inclusive lending ecosystem and capitalise on new business opportunities.
3. Comprehensive Financial Analysis
Financial analysis is one of the most important aspects of credit underwriting.
- The use of AI enables lenders to automate the financial analysis process.
- As a result, every financial transaction is accounted for, and the entire process is completed within minutes.
- Superior AI-powered credit underwriting software generates an alternative credit score, taking into account all aspects of a borrower’s finances.
- Lenders can also generate detailed reports to improve their understanding of a borrower’s finances.
In turn, they can have a realistic picture of real-time cash flows.
4. Superior Fraud Detection
The growth of digital transformation in the lending sector has made lenders more vulnerable to the threat of loan fraud. This is where AI-powered financial analysis systems score over legacy software in preventing fraud.
AI is deployed in multiple ways to detect different types of fraud:
- It can swiftly flag irregular patterns in banking transactions that are invisible to the human eye.
- AI spots inconsistencies in financial documents, which can translate to potential forgery.
- To tackle identity theft, AI-powered software can flag inconsistencies related to a borrower’s identity through an exhaustive review of their documents.
5. Customised Solutions
As the diversity of borrower needs and repayment capacities grow, lenders must be in a position to customise lending solutions quickly and effectively.
- The use of AI in superior financial analysis software automates the entire process. Based on a borrower’s real-time financial information, the software recommends relevant loan solutions.
- The terms and conditions, such as interest rates, EMI value, and repayment tenure, are designed to align with a borrower’s monthly cash flows.
This approach to credit underwriting translates to a lower number of Non-Performing Assets and borrower delinquencies.
Lenders can also capitalise on newer demographic demands, such as unsecured loans of a smaller ticket size.
Key Takeaway
Early investments in AI-powered credit underwriting can enable banks, Non-Banking Financial Companies (NBFCs), and digital lenders to optimise the underwriting process. The key benefits include comprehensive and efficient financial analysis, superior detection of fraud and irregularities, and inclusive financing for a diverse set of borrowers.
Finezza offers a comprehensive set of tools and solutions to manage your lending portfolio. Our end-to-end lending management solutions are trusted by brands like ftcash, Hiranandani Financial Services, gromor Finance, and UC Inclusive Credit, to name a few.
Here’s how the platform helps in credit underwriting:
- Data Automation: Automates data collection and real-time analysis from multiple sources.
- Custom Scoring: Provides customisable credit scoring models for tailored risk assessment.
- Fraud Detection: Identifies inconsistencies and flags suspicious applications.
- Borrower Profiles: Creates comprehensive profiles using traditional and alternative data.
- Compliance: Ensures adherence to KYC, AML, and other regulatory requirements.
Book a demo to know more.
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