A Fortune 500 Financial Services Company receives more than 200 Credit Dispute Complaints from customers daily. Utilizing a staff of three FTE’s and Four Credit Agents to investigate, action, and respond to each dispute resulted in hours of work both within and beyond working hours to appropriately resolve each dispute within thirty days after customer submission. The staff in charge of handling credit disputes was struggling to complete this task on top of their several other assigned customer-oriented jobs. Realizing that the credit dispute task alone was consuming more than 140 hours a month for the company and that a majority of this task was a monotonous process of logging, recording, and assigning each dispute to a credit agent, Accelirate suggested that this process was a good fit for RPA savings.
For any company Customer Relationship Management is an integral part of running a profitable business. Being able to quickly update, upload, and download accurate and up-to-date information, for a Financial Service Company especially, is important in order to monitor customer risk and alert agents and customers of possible fraudulent activity on an account. Currently at a Global Financial Services Company there is a team of investigators that spend approximately 20 minutes per alert to manually fill out a Know-Your-Customer (KYC) Template. At volumes of around 1,000 alerts per month, the team spends a lot of time capturing data and submitting supporting documentation to evidence via a KYC Template and several other internal data systems. In an effort to save money and reduce the time it takes to alert customers of a risk, an RPA Bot was implemented to prep and gather the data, fill out the KYC Template, and summarize the data pulled for each specific alert case.
A Global Insurance Company is made up of 100’s of insurance brokers that act as agents for their respective insurance clients. The company has an internal team of accounting employees currently spending a portion of their day dedicated to checking through every policy broker’s accounts and portfolio of corresponding invoices to see if any of their payments are overdue. Along with this, the team checks to see how long the payment has been overdue and are responsible for crafting and sending out individual emails with the appropriate attached policy information, invoice, number of days the item has been overdue to the insurance client and copying the holder’s internal insurance representative on the email to notify them as well. This time-consuming and resource-draining process is one that was a perfect fit for process automation savings.
The Default Management Function was originally broken into four separate business units by the client. Within one of these units was an important activity called “Breach” that involved a loan servicer to aggregate all relevant loan data and compile it into a format that would appropriately meet the unique requirements specified for breach letters by individual U.S. States. Even with this added functionality, Service Level Agreements were not being met and employees were often needed to work overtime to accommodate for volume. Accelirate took a holistic approach to this problem, looked at the overall goal for this process, and worked alongside the client to thoroughly analyze the breach letter process. We then came up with a solution that involved the implementation of a Waterfall Matrix to strategically develop modules that would be used by each State’s varied Breach Letter Format, saving the client time, money, and increasing their employee satisfaction.
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.