In today’s fast-paced business world, faster loan approvals and loan processes are a key goal for both lenders and borrowers. Therefore, lenders are constantly looking for ways to improve their processes and increase efficiencies. 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, in which a lender verifies a potential borrower’s income, assets, debt, and property details to issue final loan approval.. Usually done manually, an underwriter or financial expert reviews the 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 reducing the risk of errors. Therefore, automated loan underwriting can be significantly more efficient and accurate than manual underwriting.
In this blog post, we’ll 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 automatic loan underwriting?
The automatic subscription of loans is the use of technology to process and approve loan applications.
The process involves analyzing the borrower’s credit report, income, and other financial data to determine creditworthiness. Automated underwriting systems use algorithms and data analysis to quickly and accurately assess a borrower’s risk and determine if they qualify for a loan. They look for patterns and risk indicators to analyze solvency.
Some technologies that can help with various aspects of loan underwriting include:
- Artificial intelligence (AI) algorithms and machine learning (ML) it can help analyze large 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. it can help lenders make quick decisions based on information collected during the underwriting process.
- Digital signatures and electronic signatures can help automate the process of obtaining consent and signatures from borrowers.
- automated validation It can help verify the authenticity of data provided by borrowers, such as income and employment details.
- robo-advisors can help automate the process of financial counseling and referrals 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 AI and ML automated loan underwriting work?
Automated subscription with AI and ML generally works as follows:
- Data collection: The first step is to collect the necessary data from the borrower. This may include information such as credit score, income, employment history, and other financial data. This data is generally collected electronically, using document management systems and digital signatures or electronic signatures.
- Data preprocessing: Once data is collected, it is pre-processed to ensure it is in the correct format and ready for analysis. This step may include cleaning the data, filling in missing values, and normalizing the data to ensure that it can be easily analyzed by AI and ML algorithms.
- Data analysis: AI and ML algorithms are used to analyze the data and determine the creditworthiness of the borrower. Algorithms look for risk patterns and indicators to analyze creditworthiness.
- Risks evaluation: AI and ML algorithms use data analysis and financial models to determine the risk level of the borrower. They assign a risk score to the borrower, which is used to determine if they qualify for a loan.
- Decision making: 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. Automated 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 the borrowers, such as income and employment details.
- approval or denial: The system will approve or deny the loan based on the data analysis and decision-making system.
It’s worth noting that this is an 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 Subscription vs. Manual Subscription
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 funding time.
- Improved Accuracy: Automated underwriting systems use algorithms and data analysis to evaluate loan applications, which can be more accurate than relying on human judgment. This can help reduce the risk of errors and bias in the underwriting process.
- Increased compliance: Automated underwriting systems can help ensure compliance with rules and guidelines, reducing the risk of non-compliance and penalties.
- Better risk management: Automated underwriting systems can help assess and identify loan default risk by analyzing large amounts of data. This can help lenders make more informed decisions and reduce the risk of loan loss.
- lower costs: Automated underwriting can help reduce the cost of the underwriting process by reducing the need for human resources and manual labor.
- 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 to evaluate loan applications than manual underwriting, which can help lenders increase your loan approval rate, reduce your riskY 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 loan application review process by analyzing data like credit scores, income, tax returns, employment history, and more. Nanogrids 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 borrower information, reducing the risk of errors and speeding up the underwriting process. Therefore, it can help them make more accurate and faster decisions, allowing lenders to process a higher volume of loans with less risk. It can also help increase compliance and provide a better overall customer experience.
Conclution
In conclusion, automating loan underwriting processes can bring significant benefits to retail lending companies. increase speed and accuracy of loan approvals, while also reducing the risk of errorsthus improve business and overall customer satisfactionI The use of AI and ML algorithms, electronic document management systems, automated decision-making systems, digital signatures, and automated electronic signatures and validation can help lenders automate the loan underwriting process.
