Mortgage process still remains largely manual with multi-page documents, error-prone and inefficient methodologies which impact the customer experience. Robotic Process Automation (RPA) is the technological solution that can help automate the processes in mortgage industry end to end.
Advantages of using RPA in Mortgage Industry
Enhances borrower experience with faster loan processing and loan closures.
Helps in maintaining regulatory compliance by improvising process and reducing costs with accurate reporting.
Enhances operational efficiency and productivity by reducing manual process and improving fraud detection accuracy in Loan Origination Systems (LOS).
Mitigates the risk by reducing data risk arising from error prone manual handling of large volume of data.
Integrations with loan origination software (LOS) and other industry-related platforms like point-of-sale (POS) software.
Improves transparency and visibility in the lending process by workflow automation and implementation of business logics and rules.
Since Mortgage Industry is predominantly process driven, there are a lot of routine processes in loan processing which can be subjected to RPA to reduce cost and improve efficiency.
RPA can enhance and improve operational efficiency with minimum disruption to existing IT infrastructure.
Use cases for RPA in Mortgage Industry
Data Extraction: Using RPA technology it is possible to read, extract and compile pulled from multiple sources in multiple formats from loan application forms, uploaded borrower documents and other data sources.
Workflow automation: RPA can be implemented efficiently in all processes that follow structured rules. RPA can also help in ordering and sequencing the processes in the most effective manner so as to increase operational efficiency.
Audit Trails: RPA can automate processing of audit trails that demonstrate the adherence to compliance rules and regulations.
Data entry and validation: RPA can automate data entry and transfers to or from third party or non-integrated internal systems. In addition to data entry/transfer, RPA can also validate data accuracy when it is transferred between systems for analysis and reporting purposes.
Financial Analysis: RPA can help in gathering, aggregating and formatting data from several different systems or sources necessary for financial analysis.
Fraud Detection: Using configurable business rules, RPA can help in identifying loans that require fraud analysis and in combination with using predictive analytics can determine the risk of a given loan.
Challenges in implementing RPA in Mortgage Industry
Unstructured data and diverse data formats
Constant changes in regulatory compliance rules
Managing process changes within the mortgage bank
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