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:-
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:
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.
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:
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:
Self-employed individuals and freelancers have to furnish these documents:
Banks and NBFCs need profit and loss statements, credit history, and assess borrowers’ credit scores.
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.
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:
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.
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.
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.
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.
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.
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..
Below is a list of steps lenders need to take in order to automate their loan data processing workflows in organizations:-
RPA is used for automating routine tasks in an organization otherwise done manually. Coupled with deep learning and OCR, Lenders can use RPA to set specific criteria for approving applications, auto-reject, or white list potential borrowers by making credit risk assessments through them. Loan automation doesn’t just work with standard documents but other types of financial documents, bank statements, and invoices as well.
Lenders can auto-classify documents, categorize and label them, sort data, and make information more accessible to both borrowers and agents. This makes the whole application review and approval processes a lot smoother.
Set data parsing rules with intelligent APIs to extract relevant data from loan documents. Not all the information is needed by organizations and customers come from different business backgrounds. You can eliminate a lot of data redundancy by doing this and ensure only critical information is extracted. This will also help you significantly speed up processing times for your customer loan applications.
Document classification APIs in an IDP solution can scan documents, auto-categorize multi-page documents, and categorize document types without needing human intervention. APIs classify documents according to their respective categories and assign them to appropriate team members for further processing.
Intelligent APIs recognize nested tables, custom fields, and a variety of document layouts. Lenders can pull financial and personal documents from different sources and do not have to write custom rules for reading them. Machine learning algorithms in APIs are self-learning which means the more data is fed the smarter and adaptive they get.
The benefits of automated loan document data extraction are:
A huge amount of money is spent on manual data entry which is saved when using automation workflows. Companies’ process up to thousands of invoices on a daily basis and loan document OCR technology can help save countless hours of time by eliminating manual intensive labor involved in filing them.
Humans are prone to making mistakes and it doesn’t matter how big or small the margin, there is always an inherent risk. Mistakes cost companies and sometimes happen in the form of wrong data entry, missing fields, and duplicate data. Loan automation solutions can completely prevent this and ensure maximum data accuracy.
Lenders have to process loan applications from multiple systems, sources, and formats. They are tasked with assessing the creditworthiness of borrowers, doing background and income verification checks, etc. Automated data extraction software can pull data from disparate systems and recognize unstructured text. It enables lenders to provide swift turnaround times for loan processing and ensure they provide faster responses to customers. Intelligent loan document processing platforms can also automate tagging and data annotation, and make information available faster to loan companies, thus speeding up the time taken for processing financial transactions.
Paper-based signatures, written characters, telephone numbers, currencies, and more, all these can be read and interpreted correctly using ICR in loan automation software.
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.
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Despite dedicating a whole lot of resources to manual data extraction for businesses, it could result in slower turn-around time, especially if the number of documents processed per month is simply too high. There’s always the angle of ‘human error’ involved with manual document processing. So, if you’re trying to automate data extraction for your business but you cannot find a vendor to help you with, this article is for you.