AI-Powered Credit Underwriting: The Four-Step Approach

The adoption of AI in the credit underwriting process is expected to be one of the most significant changes in the lending sector over the coming decade. 

The technology spend on AI systems in the lending sector, for instance, is expected to increase from $11.7 billion in 2021 to $27.7 billion in 2025. 

One of the key factors that will drive the adoption of AI is the race to fulfil and capitalise on India’s credit demand. 

For instance, though the credit gap for MSMEs in India is estimated to be approximately $1.9 trillion, only $289 billion is fulfilled by traditional lenders such as banks. 

In this blog post, we outline why traditional underwriting metrics are no longer relevant in an AI-powered lending ecosystem. We also recommend a four-step approach to power smarter credit underwriting. 

What is Credit Underwriting?

Credit underwriting is the process of analysing a potential borrower’s finances to determine if they will be in a position to repay a loan on time and in full. 

However, the predictive nature of this process makes it challenging for lenders to determine accurately, especially at scale. 

On the other hand, manual credit underwriting typically relies on metrics such as income, collateral, and credit history to assess creditworthiness. 

However, these metrics come with limitations, especially in the contemporary lending landscape. 

Key Challenges with Traditional Credit Underwriting Metrics 

The traditional credit underwriting metrics come with the following limitations:   

1. Credit History

This metric is only applicable to those borrowers with a history of successful loan applications. Thus, credit history is a factor that excludes first-time borrowers. 

Over-reliance on credit history leaves a large pool of borrowers outside the lending system’s purview. Hence, this approach comes at an 

opportunity cost for lenders. 

2. Collateral

Not all borrowers have access to collateral in the form of real estate, machinery, vehicles, stocks, bonds, and other instruments. 

Hence, borrowers who do not possess collateral are at a disadvantage.  

3. Income/ Revenues 

Income and revenues can offer insight into a borrower’s bottom line. However, they do not inform lenders about aspects such as:

• Erratic credit patterns such as late/ non-payment of EMIs/ credit card bills
• Poor consumption behaviour habits, such as overspending 
• Too many overdrafts 
• Sources of funding 
• Potential Loan Fraud
• Misuse of funds for terrorist/ money-laundering activities 

What Is the 4-Step Approach to AI-powered Credit Underwriting?

The use of AI paves the way for credit underwriting to become more accurate, accessible, and scalable. This 4-step approach is helping lenders transform the process: 

Step 1: Leveraging Alternative Data

Lenders can utilise alternative data sources more effectively due to AI’s unique capabilities, such as automation, personalisation, predictive analytics, and generative AI.  

Examples of alternative data include:

• Bank account statements over the relevant time frame 
• Credit score and detailed credit history
• Goods and Services Tax Returns (GSTR)

By using a superior loan management system, lenders can cross-reference GSTR with business revenues. This practice allows them to verify the authenticity of projected revenues against the relevant tax return data.

Step 2: Smart Data Access and Extraction

Once the data sources are identified, lenders can efficiently and optimally gather and structure a borrower’s financial data. The use of a superior AI-powered loan management system helps automate the process, reducing the potential for errors, omissions, and duplications. 

The use of AI ensures a comprehensive and efficient approach to accessing, extracting, and categorising data. 

• Data can be directly retrieved from relevant data sources with the borrower’s consent.  
• AI-powered software can extract data from over 700 types of documentation formats. 
• The extracted data is categorised into inflow and outflow categories. The data is further segregated into over 30 sub-categories under each. This approach ensures that all transaction sources and destinations are accounted for.  

Step 3: Comprehensive Financial Analysis

Lenders grapple with large volumes of alternative data for each loan application. The use of AI enables lenders to automate the financial analysis process, a key aspect of superior credit underwriting. 

• Automated financial analysis makes the process efficient, accurate, and speedier as compared to manual analysis. 
• Lenders can customise dashboards to adhere to regulatory guidelines, ensuring compliance across the loan origination process. 
• Every financial transaction is accounted for. 
• The process is concluded within minutes. 

Step 4: Data-Driven Decision Making 

Data plays a crucial role in enabling lenders to make more informed credit underwriting decisions. 

• Lenders can base their credit underwriting decisions on detailed, AI-generated credit reports that result from a comprehensive analysis process. 
• Transactions where the funding sources and destination accounts cannot be verified are isolated, making them accessible to lenders for further investigation and review.  
• The software assigns every borrower a unique credit score that can be used to benchmark borrowers across the spectrum. 
• Superior financial analysis paints a realistic picture of a borrower’s cash flows. Hence, lenders can base their decisions on cash flow rather than traditional metrics such as income, credit scores, and collateral, which do not paint a complete picture.  

Additional Benefits of AI-driven Credit Underwriting

The use of AI enables lenders to reap additional benefits as follows:

Boosting Fraud Detection Capabilities 

Here are some examples of how AI strengthens fraud detection capabilities:

• Detects irregular patterns in banking transactions within seconds.   
• Tracks inconsistencies in financial documents, such as bank statements, thereby identifying and weeding out forged documents. 
• Identifies inconsistencies in names, addresses, and other identifiers associated with the identity of borrowers. In turn, cases of identity theft can be weeded out early on.  

Generating Personalised Loan Solutions

One of the top benefits of leveraging AI-based credit underwriting is that lenders can deliver customised solutions to a spectrum of borrowers.

• The process is automated, end-to-end. 
• The software generates loan terms and conditions that are aligned with a borrower’s real-time cash flows.  
• This approach ensures that loan products are affordable.
• The turnaround on loan applications is expedited. 

Takeaway 

Traditional credit underwriting metrics such as income, credit history, and collateral come with limitations that paint an incomplete picture of a borrower’s finances. 

This is where AI-powered underwriting can bring more efficiency, accuracy, innovation, and speed to the process. This approach enables lenders to deliver customised solutions, boost fraud detection capabilities, enhance risk management, and scale operations faster. 

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.

Finezza’s intuitive bank statement analysis software is geared to analyse bank statements with speed and accuracy, and detect potential fraud more effectively. Our core solutions include:

• Bank statement analysis
• GSTR analysis
• Credit report analysis
• Account aggregator connector

Book a demo to know more.