Loan lending management systems simplify lending processing while eliminating the need for human intervention and potentially costly and time-consuming mistakes.
FREMONT, CA: Financial institutions benefit from loan lending automation solutions that provide flawless data processing and exceptional customer service. This process usually takes several days and requires several people throughout the organization to complete, but now it takes only a few minutes.
Streamline loan lending processes by creating custom workflows, integrating with loan management systems, and automating part or all of the process. Streamlining the entire process, from loan origination and application to underwriting and payment processes, while ensuring complete transparency is possible with a digital workforce.
Most organizations must process paperwork as part of traditional loan lending workflows. Manual processes can cause backlogs in other departments.
Stakeholder management, onboarding mortgage loan customers, credit scoring, and maintaining customer satisfaction are all time-consuming tasks.
Lenders and borrowers can benefit from automating custom workflows.
Loan application: Manual data collection and back-and-forth with applicants are the biggest challenges for bankers when initiating the loan process.
An applicant can complete a loan application online, collect supporting documents from sources, and upload them digitally on the banking portal. A borrower can download a PDF version of their recurring bank statement from the loan provider's site and submit it online.
Lenders can read documents using OCR technology and extract specifics using the technology, and RPA can then copy and paste the data into the bank's systematized format. This will improve efficiency and streamline the document submission stage of a loan application.
Loan application processing: Bank employees are responsible for verifying loan applications' accuracy, completeness, and currentness. Loan processing software leverages IA to automatically check whether all the required borrowers have filled in all the necessary blanks. It scans documents to check whether they meet pre-approved criteria and cross-check the submitted information against sources of truth, like the company's name against their tax slips.
Underwriting: An underwriter assesses a loan applicant's risk and determines if the applicant qualifies for a loan. A credit score determines the riskiness of an applicant, and other metrics can complicate the underwriting process.
The use of automation in risk assessment is still underrepresented in banks, with 31 percent using it. By analyzing the content of the applicant's submitted documents, adding automated comments to each field, and comparing their index (the applicant's level of profit to revenue ratio compared to those in similar fields) with industry standards, these ML models are further enhanced by technologies such as RPA, OCR, and NLP.
