What is Automated Loan Underwriting?

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In today’s fast-paced business world, faster loan approval and lending processes are the main goal for both lenders and people applying for loans. Therefore, lenders are constantly looking for ways to improve their processes and increase efficiency. Automation can have a significant impact on this process, particularly the loan origination process.

Loan underwriting is the step in which the lender checks the potential borrower’s income, assets, debt and property details before approving or denying the loan to give the final loan approval.. Usually done manually, an underwriter or financial expert goes through the relevant documents to verify your income and assets and determine if you qualify for a loan.

Automating the underwriting process can increase the speed and accuracy of loan approvals while reducing the risk of errors. Thus, automated loan placement can be significantly more efficient and accurate than manual.

In this blog post, we’ll explore the processes and benefits of automated loan origination and how using an OCR platform like Nanonets can help lenders achieve those benefits.

What is Automated Loan Underwriting?

Automated loan disbursement using technology to process and approve credit 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 risk patterns and indicators to analyze creditworthiness.

Some of the technologies that can help with various aspects of lending 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 make quick decisions based on information gathered during the underwriting process.
  • Digital signatures and electronic signatures can help automate the process of obtaining consent and signatures from borrowers.
  • Automated validation can help verify the authenticity of information provided by borrowers, such as income and employment information.
  • 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%.

Automate your mortgage processing, underwriting, fraud detection, bank reconciliations or accounting processes with a ready-to-use custom workflow.

How does automated loan origination work with AI and ML?

Automation in lending

Automated placement with AI and ML typically works like this:

  1. Data collectionThe 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 usually collected electronically using document management systems and digital signatures or electronic signatures.
  2. Data preprocessingAfter 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 it can be easily analyzed by AI and ML algorithms.
  3. Data analysisAI and ML algorithms are used to analyze data to determine a borrower’s creditworthiness. To analyze creditworthiness, algorithms look for patterns and indicators of risk.
  4. Risk assessmentAI and ML algorithms use data analysis and financial models to determine a borrower’s risk level. They give the borrower a risk score that is used to determine if they qualify for a loan.
  5. Decision makingThe automated decision making system uses the risk score to decide whether to approve or deny the loan. This can be done in real time, allowing lenders to provide feedback to the borrower on the status of their loan application. Robo-advisors can be used to automate the process of providing financial advice and recommendations to borrowers.
  6. ValidationAn automated validation system verifies the authenticity of data provided by borrowers, such as income and employment data.
  7. Approval or rejectionThe system will approve or deny the loan based on data analysis and decision making system.

It’s worth noting that this is a general overview of how automated underwriting with AI and ML might work, and the specific implementation may vary depending on the lender and the technology used.

Automated Placement vs. Manual Placement

Automated placement and manual placement are two different methods of evaluating loan applications. Lenders should implement automated loan underwriting because it offers several advantages over manual underwriting, including:

  • Increased speed and efficiency. Automated underwriting systems can process loan applications much faster than manually, allowing lenders to make faster and more accurate decisions. This can help increase the number of approved loans and reduce funding times.
  • 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 error and bias in the allocation process.
  • Increased complianceAutomated 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 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 losses.
  • Lower costsAutomated placement can help reduce the cost of the placement 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 providing customers with real-time feedback on the status of their loan application.

In short, automated loan origination offers a more efficient, accurate, and cost-effective way to evaluate loan applications than manually, which can help lenders. increase their loan approval rate, reduce their riskand: improve the overall customer experience.

Automate your mortgage processing, underwriting, fraud detection, bank reconciliations or accounting processes with a ready-to-use custom workflow.

How OCR and Nanonets Can Help With Automated Loan Origination

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 slips, tax returns, and more.

Nanonets is an AI-powered OCR platform that allows lenders to easily train and apply machine learning models to underwrite loans. It can be used to automate the credit application evaluation process by analyzing data such as credit scores, income, tax returns, employment history, and more. Nanogrids can also be used to extract unstructured data such as images and PDFs, which can be difficult for traditional OCR.

Using Nanonets OCR technology will allow lenders to quickly and accurately collect and verify information from borrowers, reducing the risk of errors and speeding up the underwriting process. Check out Nanonets automated bank statement to JSON workflow.

So it can help them make more accurate and faster decisions, allowing lenders to process a higher volume of loans with reduced risk. It can also help increase compliance and provide a better overall customer experience.


In conclusion, automating loan origination processes can bring significant benefits to lending companies increasing speed and accuracy of loan approvals while also reducing the risk of errorsthereby overall improvement of business and customer satisfactionl. The use of AI and ML algorithms, electronic document management systems, automated decision-making systems, digital signatures and electronic signatures and automatic validation can help lenders automate the loan origination process.

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