Finance

Case Study

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Process: Holdings, Futures, Security and Trade

Challenge

  • Originated by security, trade and holding data files received via mail
  • Directed and manually processed by multiple support teams such as the Data Quality Monitoring team, finance and accounts team
  • Daily completion before market opens at 9am
  • Integral to the stock market value and financial market data of the organisation
  • Manual correlation of multiple data sources required (FX rates, futures, securities, holdings)
  • High quality data required in order to comply with regulations
  • Complex data files with high human error rate
  • Reliant on skilled FTE for daily completion thus high risk profile.
  • Processes needs to interact with multiple applications such as CRM, Bloomberg, Terminal, legacy systems, web portals, email and excel.
Finance Case Study

Solution

  • The bot logs in as a named user to the online portal email and FTP server.
  • Bot checks for requests and data files present and extracts the data as per type of request. Initial data validation/ cleaning is done.
  • Bot then logs into the required applications as a named user and completes the transaction as per the process request type (to the design). These include interactions with secured and unsecured portals as well as interactions with real-time financial market data systems such as Bloomberg terminal.
  • Once the process has been completed successfully the bot then emails the agent, fund manager, business unit with the appropriate update to the enquiry
  • a log file of processed transactions is created by the bot and emailed to the nominated business unit.
  • If a transaction fails for any reason the bots notifies and transfers the enquiry back to the nominated business unit to complete (i.e. files data not present)
  • if the transaction fails to complete due to an application not being available the bot will re-try and notify the business unit/service desk of possible outage.

Finance: Use Case

Benefits

  • Quality improvement to 0% error rate achieved
  • Degree of successful throughput: 100% of effort automated
  • Improvement of Service levels from - 45min to 7 min
  • Reduction in risk profile
  • Reduction in operational costs
  • Improved scalability (On-boarding bots 15 min v Human +6 weeks
  • Reduction of over-reliance in skilled FTE
  • Improved staff retention - reduced high volume low value work to focus on complex/higher value work
  • ROI: 4 months

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