Imagine auditing a client whose business consists of pre-settlement funding — the business provides plaintiffs with cash advances to pay for necessary expenses as legal action is pursued. As an auditor who is auditing this company, how do you ensure that a case is still outstanding and therefore a true receivable at year-end? How do you know that a case is still active?
The old way: In New York, and many other jurisdictions, there are public websites where you can search the status of any case and find out all relevant information about it. This is great! Luckily for the auditors, they can search these cases and find out the status from a third-party source. The client gives the audit team MS Excel files with the case details. Auditors must then search the court websites to match the correct court case to a case number or partial case number from the data provided by the client for hundreds and in some cases thousands of cases, based on the sample size. The results from the search are either entered into a spreadsheet manually or copied and pasted. This is extremely repetitive, labor-intensive and very much an inefficient use of any auditor’s time (at any level).
The new way: Utilize RPA to the point where each task turns into a programmed process in which a bot performs all the repetitious steps:
- First, the robot cleans the data. RPA can be used as an ETL (extract, transform and load) tool to fix improperly entered case numbers and flag incorrect or incomplete for review. The data will then be automatically separated into specific Excel spreadsheets, based on the court (local court, appellate court, etc.).
- Second, the robot logs on to the court websites and conducts a search for the current case status. The robot will open a browser, go to the website URL, copy and paste the first case index number from the spreadsheet of case index numbers into the website’s search feature. The robot then scrapes all the relevant search results from the website, including the status, the most recent or next appearance date, among others, and writes them to a new spreadsheet that contains all the existing data and several new columns that contain the data points from the website — with near 95 percent accuracy. All cases verified by the robot are written to an Excel spreadsheet, while cases with insufficient data are written to another spreadsheet to be reviewed by the auditor. This frees up time for the audit team to actually analyze the results and exercise their judgement rather than spend hours on data collection.