Every month the client considers a large group of people with mortgages to potentially send out offers of refinance to. Before machine learning became a solution to this, hand-written rules were manually created by subject matter experts to reduce the large pool of potential recipients to a smaller number of borrowers to send offers to. Of these thousands of offers, typically less than 10% of the recipients were filing a loan application to reﬁnance with the client. Machine Learning can more intelligently identify which borrowers are most likely to reﬁnance therefore allowing the client to speciﬁcally target those most likely to respond. This helps the clients increase the number of borrowers that reﬁnance with them and helps them to spend their marketing budget more effectively by only sending offers to those more likely to respond.
Employees were spending countless hours reviewing a small sample of incoming loans, and even so, they did not have enough resources to accurately review and label every loan they were servicing. Inaccurately labeling the status of the loans was causing frustration and a loss of a $50,000 incentive for the company to accurately report the status of each loan. With the use of RPA, bots were able to accurately label every single loan with the correct status in a fraction of the time that the employees were taking to go through the original Electronic Data Reporting process.
Data updates to systems can be cumbersome especially if there isn't a standard data structure and the system does not allow for bulk updates through spreadsheets. A common example of such an update are product updates. A supplier may update product specifications from time to time and this can result in an extensive data entry exercise for staff. This involves reading the updated product data from a spreadsheet or document. Then updating this data into the company's internal system. This use case shows a robot reading product data from a spreadsheet and then entering this data into the ERP.
Employee On-boarding requires various verifications and data entry into multiple systems. These include, employment and background verification, payroll, and IT provisioning systems to name a few. This can be time consuming. On-boarding activities include: Send and receive on-boarding documents from new employee Perform employment eligibility verification Create employee record in payroll system Enter employee tax information in payroll system. Create IT system accounts (login, email, etc.) for new employee The Automated solution is able to access received verified documents via email or a file folder, and perform the various checks required including E-Verify or background verification. Upon successful completion.
Invoice processing is a time-consuming task. Invoices need to be scanned and read and various checks need to be applied before these can be entered into the Accounting System. Receive and read invoice Check invoice against vendor and PO Send for approval if discrepancies are found Enter the invoice into the accounting system Mark purchase orders as complete The Automated solution is able to receive invoice documents and access the accounting system to perform the Vendor Name and PO validations. If any discrepancies are found these are escalated for human intervention. Once all validations pass the invoice is entered.
Accounts Receivable staff spends a considerable amount of time chasing payable invoices. An out of control or aging AR can result in considerable strain on business cash-flows, hence accounting teams are under intense pressure to keep AR Current. This "chasing invoice" activity includes Run the AR Aging report Opening each invoice to check invoice details Access the customer record to retrieve contact information Write an email to the customer including a copy of the invoice. The Automated solution is able to access both the accounting and email systems and perform all these actions in an automated fashion allowing your.