RPA use cases in banking

RPA use cases in banking

We will learn step by step tutorial for “RPA use cases in banking”. RPA is a configurable software that sits on top of a company’s existing IT infrastructure, pulling data, performing algorithms, and creating reports.RPA makes the most significant impact on manual work processes that are repetitive and recurring, and often have high human error rates. A single “robot” can be configured to perform a variety of processes enabling multi-use robots, and variability as your business needs change.

if we talk about the digital transformation, banking organizations and other financial institutions have established themselves as the early adopters of smart technologies for growing their organization fast. Smart software is already making a difference in front-end, customer-facing systems. However, organizations still need to strike a better balance between the front and the back office, while becoming faster and more reliable. With Robotics process automation, operations get streamlined and relieve some of the accumulated heat

Following are the key processes where RPA can be implemented in banking include:

  • Mortgage lending
  • Direct loan underwriting
  • Trial balancing
  • Accounting onboarding
  • Account maintenance
  • Fraud and risk review
  • Account closing
  • Account reconciliation
  • Account processing
  • Commercial banking operations
  • Escrow
  • Wire administration
  • Item processing
  • Loan origination
  • Collateral management and imaging
  • Card services
  • Exception processing
  • Lockbox operations
  • Print and statements
  • Loan application processing
  • Loan servicing

RPA use cases in banking Example :

Loan Application Processing

Process Background :
Title Documents are sent to loan analysts to be keyed into loan accounts and set to downstream process team for
servicing via a workflow.

Process Inputs/Outputs:

  • Unstructured Documents(Title documents in pdf format)
  • loan servicing system

Intelligent Automation Solution :
RPA Rules applied to the initial front end process to provide the lead into analysts to train the robot to recognize actionable fields from the title document; Machine learning tasks applied to the title documents to extract key data; RPA rules applied to accept and closeout ticketing of title service request.

Business Benefits:

  • 55% Automation with RPA
  • 82% adding RPA + Exception Handling with 96% Quality
  • Decrease Title serving time from 60+ minutes to 2 min per transaction (1000+ volume/mo)
  • 37 FTE saving @ $33,000 fully loaded cost per FTE($1.22MM)

Conclusion

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