Whether it be cash management, structured credit, syndicated loans, trade finance, supply chain finance or other types of financing, the processing of transactions involving Corporate Banking products and services are still very reliant on forms and documents. Corporate Banking operations staff perform many process steps that are manual and monotonous, but at the same time require thoroughness due to the increasingly demanding regulatory environment.
There are indeed vast opportunities for digital transformation in corporate banking operations, and RPA is only part of the solution. Implementing RPA helps with data quality, compliance with regulations, cost and operational efficiency, particularly in document management as well as some data-quality related processes. By automating high-volume, standardised, and repetitive tasks, staff are free to focus on the analytical tasks and activities that grow the business and enhance relationships with clients.
The Bot in a Nutshell:
The bot scans through documents, extracts data, and classifies it into the corresponding fields. Using the data, it performs necessary compliance checks and all instructed validations against databases, government agencies, and other verified third-party sources. For more complex cases, the bot can add any exceptions to a manual processing queue for investigation and further analysis by the staff.
Document management might seem trivial but becomes less so when considering the ever-increasing volumes of input data. The bot can help to significantly reduce work completed manually in data input and improve data quality as well.
The bot can also execute all these routine procedures in the background and leave staff to do more value-added activities – investigation, analysis, decision-making, managing relationships and communication with clients.
Main Functions:
The Bot can scan through numerous documents quickly and extract and validate the relevant data, enter data into the relevant banking systems, thereby reducing manual input, processing time, and data quality errors
Employees can focus on value-adding tasks, such as further investigations, managing complex or exceptional issues or transactions.
High-Level Plan
Main Benefits:
Customer satisfaction
Cost & scalability
Process reliability
Data quality
Hands-On Considerations
Pain points targeted:
Repetitive manual processes which are time-consuming and tedious
Poor data quality due to manual data handling that is error prone
Limited scalability during peak times
Extensive reconciliation and four-eyes principle
Challenges to expect:
Variation in format of documents
Complexities and exceptional cases
What will the bot do?
To read more, find out our RPA use case on Client Onboarding in Banking
