Processes such as loan management require a great deal of paperwork, data checks, and dependencies. Today’s customer expects quick, easy access to loan services at the time, place, and channel of their choosing. They seek loan products tailored to their specific needs that are available through their preferred channel and have quick approvals.
Robotic process automation (RPA) has been assisting lenders in gaining a competitive advantage during the loan origination process. RPA’s use in loan management has allowed banks to automate processes.
Simply put, robotic process automation is the technology that replaces human action for repetitive and redundant tasks by extracting and processing structured data. Robots mimic human tasks based on a set of instructions. They can process error-free applications much faster. The ‘bots’ can read an email or invoice, download files to a specific location, copy-paste, scan through data and file it. It is designed specifically for process-oriented industries.
Implementing RPA in loan processing can speed up processes, increase reliability, and reduce labor-intensive tasks by shifting the burden to robots and freeing the workforce to perform more complex tasks. RPA in loan processing can strengthen existing banking mechanisms while also assisting the industry in scaling its growth.
Challenges that RPA can overcome in loan processing and management
- Automatic Report Generation: RPA can generate compliance reports that flag fraudulent transactions and generate Suspicious Activity Reports (SAR). It scans compliance documents and sorts data quicker than humans.
- Customer Onboarding: Manual verification makes customer onboarding a time-consuming and inconvenient process. RPA can extract data from documents and compare it to the information provided.
- Mortgage Lending: Loan initiation, document processing, and financial comparisons are all aspects of mortgage lending that can be automated, resulting in a faster loan approval process. It also improves customer satisfaction.
- Loan due report: RPA robots can detect loans approaching their due date. RPA can collect files from various branches, combine them, and send them to the appropriate stakeholders. The process can be completed within a predetermined period, such as 15 days before repayment or EMI payments.
- Balance Register: RPA can be used to check the balance register and determine which charges must be collected. Using a set of instructions, accounts can be automatically updated to add balances.
- Loan Closure NOC: Non-Disclosure Certificates can be generated automatically. The bot receives the documents and the pending loan amount cheque and then searches the system for any remaining funds. If there is a bug, the process stops, and the remarks are updated. Otherwise, the bot designs the NOC and sends it to stakeholders for approval.
- Retail Asset Detail Modification: RPA bots examine the documents they receive, and if there is a difference in information, they update it in the banking system by a designated person in the bank. If there are no existing savings accounts and only the loan account, the bot modifies it with the help of a human approver.
- Loan Exposure Sheet: RPA can be used to collect social security numbers from the banking system and generate a final exposure excel sheet that can be auto-sent to stakeholders. It will help you calculate the loan exposure.
- SME Credit Management: With the help of robots, RPA can manage small credit lines and the capital needs of small and medium-sized enterprises (SMEs). Automated credit platforms speed up the application process while facilitating SME loan applications.
Several ways can be used to implement RPA for loan processing. The organization can decide which use cases to focus on after conducting a thorough assessment of its operations. You can speak with a DynPro expert at any time, and we will assist you in making the best decision for your company.
DynPro is a UiPath and Automation Anywhere partner, bringing the best solution to the customer’s needs. Our RPA solution automates processes, identifies inefficiencies, and provides insights, making the path to digital transformation quick and cost-effective. It makes use of existing systems to reduce disruption.