In today’s fast-paced business world, faster loan approvals and lending processes are a key goal both for lenders and people applying for loans. Lenders are therefore constantly seeking ways to improve their processes and increase efficiency. Automation can have a significant impact on this process—particularly the loan underwriting process.
Loan underwriting is the step before a loan is approved or denied, where a lender verifies a potential borrower’s income, assets, debt and property details in order to issue final approval for the loan. Typically done manually, an underwriter or financial expert goes through relevant documents to verify your income and assets and determine if you’re qualified to borrow.
Automating the underwriting process can increase the speed and accuracy of loan approvals, while also reducing the risk of errors. Thus, automated loan underwriting can be significantly more efficient and accurate than manual underwriting.
In this blog post, we will explore the processes and benefits of automated loan underwriting, and how using an OCR platform like Nanonets can help lenders achieve these benefits.
What is automated loan underwriting?
Automated loan underwriting is the use of technology to process and approve loan applications.
The process involves analyzing a borrower’s credit report, income, and other financial data to determine their creditworthiness. Automated underwriting systems use algorithms and data analysis to quickly and accurately assess a borrower’s risk and determine whether they qualify for a loan. They look for patterns and indicators of risk to analyze creditworthiness.
Some technologies that can help with various aspects of loan underwriting include:
- Artificial intelligence (AI) and machine learning (ML) algorithms can help analyze vast amounts of data and identify patterns that can be used to predict creditworthiness.
- Electronic document management systems can help automate the process of collecting and verifying information from borrowers.
- Automated decision-making systems can help lenders quickly make decisions based on the information gathered during the underwriting process.
- Digital Signatures and e-signatures can help automate the process of obtaining consent and signatures from borrowers.
- Automated validation can help verify the authenticity of the data provided by borrowers, such as income and employment details.
- Robo-advisors can help automate the process of financial advice and recommendations to borrowers. According to a recent study by McKinsey and Company, Robotic Process Automation (RPA) can help reduce loan-processing time by up to 80%.
How does automated loan underwriting work with AI and ML?
Automated underwriting with AI and ML typically works as follows:
- Data collection: The first step is to collect the necessary data from the borrower. This can include information such as credit score, income, employment history, and other financial data. This data is typically collected electronically, using document management systems and digital signatures or e-signatures.
- Data pre-processing: Once the data is collected, it is pre-processed to ensure that it is in the correct format and ready for analysis. This step can include cleaning the data, filling in missing values, and normalizing the data to ensure that it can be easily analyzed by the AI and ML algorithms.
- Data analysis: AI and ML algorithms are used to analyze the data to determine the borrower’s creditworthiness. The algorithms look for patterns and indicators of risk to analyze creditworthiness.
- Risk assessment: The AI and ML algorithms use data analysis and financial models to determine the borrower’s risk level. They assign a risk score to the borrower, which is used to determine whether they qualify for a loan.
- Decision-making: The automated decision-making system uses the risk score to make a loan approval or denial decision. This can be done in real-time, allowing lenders to provide feedback to the borrower on the status of their loan application. Robo-advisers can be used to automate the process of financial advice and recommendations to borrowers.
- Validation: The automated validation system verifies the authenticity of the data provided by borrowers, such as income and employment details.
- Approval or denial: The system will approve or deny the loan based on the data analysis, and the decision-making system.
It’s worth noting that this is a general overview of how automated underwriting with AI and ML can work, and the specific implementation may vary depending on the lender and the technology being used.
Automated underwriting versus Manual underwriting
Automated underwriting and manual underwriting are two different methods of evaluating loan applications. Lenders should embrace automated loan underwriting because it offers several benefits over manual underwriting, including:
- Increased speed and efficiency: Automated underwriting systems can process loan applications much faster than manual underwriting, allowing lenders to make faster and more accurate decisions. This can help increase the number of loans approved, and reduce time to funding.
- Improved accuracy: Automated underwriting systems use data analysis and algorithms to evaluate loan applications, which can be more accurate than relying on human judgment. This can help reduce the risk of errors and biases in the underwriting process.
- Increased compliance: Automated underwriting systems can help ensure compliance with regulations and guidelines, reducing the risk of non-compliance and penalties.
- Better risk management: Automated underwriting systems can help evaluate and identify the risk of loan defaults by analyzing large amounts of data. This can help lenders make more informed decisions and reduce the risk of loan losses.
- Lower costs: Automated underwriting can help reduce the cost of the underwriting process, by reducing the need for human resources and manual work.
- Better customer experience: Automated underwriting can also help provide a better customer experience, by reducing the time it takes to approve a loan, and by providing real-time feedback to customers on the status of their loan application.
In short, automated loan underwriting offers a more efficient, accurate, and cost-effective way of evaluating loan applications than manual underwriting, which can help lenders increase their loan approval rate, reduce their risk, and improve the overall customer experience.
How OCR and Nanonets can help with automated loan underwriting
Optical Character Recognition (OCR) is a technology that can be used to automate the process of collecting and verifying information from borrowers. OCR software can be used to scan and extract text from documents such as ID cards, pay stubs, tax returns, and more.
Nanonets is an AI-based OCR platform that allows lenders to easily train and deploy machine learning models for loan underwriting. It can be used to automate the process of evaluating loan applications by analyzing data such as credit scores, income, tax returns, employment history, and more. Nanonets can also be used to extract unstructured data from formats like images and PDFs, which can be difficult for traditional OCR.
Using Nanonets’ OCR technology would allow lenders to quickly and accurately collect and verify information from borrowers, reducing the risk of errors and speeding up the underwriting process. It can thus help them make more accurate and faster decisions, allowing lenders to process a larger volume of loans with lowered risk. It can also help increase compliance and provide a better overall customer experience.
In conclusion, the automation of loan underwriting processes can bring significant benefits to lending companies by increasing the speed and accuracy of loan approvals, while also reducing the risk of errors, thereby improving business and customer satisfaction overall. The use of AI and ML algorithms, electronic document management systems, automated decision-making systems, digital signatures and e-signatures, and automated validation can all help lenders to automate the process of loan underwriting.