With an automated data extraction solution, loan documents can automatically be processed end-to-end without any human errors and delays. Automation in loan document processing prevents downtimes, eliminates data redundancy, and allows companies to respond faster to client queries. By combining machine learning with deep learning and OCR, companies can eliminate huge costs, derive actionable insights, and streamline loan processing and approvals through efficient data extraction and analysis.
In this article, we’ll discuss the need for automated loan document processing and extraction for companies, including covering what type of documents can be processed and challenges faced in the finance sector.
Let’s get right into it:-
Documents required for small business loans
Borrowers have to go through a verification process before lenders are willing to disburse the loans they are applying for. Here is a list of documents requested by banks, NBFCs, and financial organizations for issuing small business loans to individuals:
1. Business financial statements
These are accounting statements that show deposits and withdrawals made by businesses. It demonstrates the borrowers’ ability to repay loans and acts as proof of income or employment.
2. Proof of identity
Lenders always request borrowers to verify their identity by furnishing proof of identity or citizenship documents. In the United States, borrowers have to present at least two or more forms of identification from the following:
- Driver’s License
- Passport
- Certificate of Citizenship
- Military ID
- Birth Certificate
- State or Government IDs
3. Employer and income verification documents
These are meant for borrowers who work in organizations and do not run their own businesses. It shows their financial standing and stable cash flow to and from their accounts. Borrowers traditionally verify their income by furnishing the following documents:
- Bank statements
- Paystubs
- W-2 and 1099 forms
- Employer information
Self-employed individuals and freelancers have to furnish these documents:
- 1099s
- Bank statements
- Income tax returns
4. Credit score and history
Banks and NBFCs need profit and loss statements, credit history, and assess borrowers’ credit scores.
5. Proof of address
These include utility bills, lease and rental agreements, home insurance documents, and proof of vehicle insurance. Voter registration cards are also needed as supporting documentation.
Challenges involved with processing loan documents
Loan documents come in a variety of file formats, layouts, and styles, which means lenders have to spend a lot of time in categorizing and processing them. Loan processing services have to follow a set of predetermined rules when analyzing borrower profiles and route customers to the appropriate agents based on their submitted documents.
AI in loan processing software can make a significant difference in how companies make sense of customer data, derive insights, and compare borrower profiles in order to reach out to reliable customers and make the best loan offers to them. When it comes to pain-points, for companies who are relying on traditional IT processes to analyze loan documents, here is a list of key challenges they face:
1. Document frauds
Document fraud instances include cases of signature forgeries, duplicate files, and falsifying various details for the purpose of processing transactions.
Officials fail to verify individuals and leak sensitive information to cyber criminals when they pose as borrowers. Loan document automation is used to verify the identity of official applicants and ensure documents come from legitimate sources, automatically establishing a connection with legal authorities.
2. Huge volumes of data
Businesses suffer from data congestion and are tasked with processing high volumes of customer data for loan applications. Many supporting documents come with these applications and it is almost impossible to manually verify and go through them. Furthermore, each document type features its own layout and format for interpretation. Every applicant is unique and the complexity of processing various documents coupled with their applications is an area loan agencies struggle with, when using manual data processing tools. Employees involved in manual data entry also take downtimes and are prone to making mistakes which means re-corrections, delays, and additional business expenses are involved in processing applications.
3. Migration from legacy systems
Organizations struggle with the problem of updating legacy systems and moving away from traditional IT architectures. When it comes to migrating data, information needs to be pulled from multiple sources and processed in real-time. There are vulnerabilities exposed when migrating data offline to online, high error rates, and the problem of operational downtimes when upgrading systems. Businesses that service demanding customers or have a giant client-base cannot afford to cause delays in their core processes when migrating data across different platforms.
4. Slow and tedious
If an employee gets fired, on leave, or absent, other employees are left in the dark and don’t know how to fix issues. There is a backlog of pending documents and new employees have to be trained on how to process these. Manual data entry professionals have a steep learning curve as well and there are hidden costs involved with the slow turnaround times.
5. Lack of legal compliance
Loan companies are failing to meet legal and regulatory compliance standards when processing documents manually. They are forced to convert policy agreements to electronic records and update databases since regulatory bodies require them to do so. Due to increased competitiveness in the industry and digital transformations, loan agencies cannot afford to fall behind and use traditional loan processing systems.
6. High customer risk
It is harder for loan agencies to run background checks on customers when doing manual processing. Customers may decline to furnish all their details and there is no way of pulling out their financial data manually. Determining their risk appetite accurately would mean learning about their portfolios, assets, credit scores, and repayment records. Lenders also cannot make predictions or derive insights from data when manually processing applications since there are so many variables involved..
Final Words
AI, OCR technology, and RPA tools make it convenient for lenders to enjoy a smooth experience when it comes to running background checks, verifying income sources, and processing multiple loan documents. The processing turnaround times become shorter and organizations save a lot on data entry costs since it becomes fully automated. If you are looking to provide your borrowers the best customer experience, sign up for a free demo with Docsumo today.