A major American Auto Insurance company offers a discount for their policy holders who complete a “Cautious Driver” course. After course completion, the Insurance company receives files from three different course vendors and uses two different websites to record the results, populate driver information, look up the insurance policies, and apply the appropriate discounts. This process involves several different files and screens resulting in the full-time efforts of up to 10 employees to process over 800 new good driver discounts daily. Using RPA, this process was able to be fully automated and now involves no manual labor and provides the company a major reduction processing time overall.
A $250 Million-Dollar International Manufacturing and Retail Company uses both SAP for data entry and DocuWare to keep track of their inventory and non-inventory invoices. Currently the Accounts Payable department is responsible for sorting through over 500 invoice lines for 100+ invoices daily. This task is very monotonous and can take up to an hour per invoice; with RPA alone, the automation was able to eliminate 50% of the AP Team’s daily volume by processing the digital invoices. However, with the addition of Optical Character Recognition (OCR) technology to the initial automation, the bot is able to handle nearly 100% of all inventory and non-inventory invoice processing.
The Accounts Payable Department for an international manufacturing company is responsible for locating and uploading all incoming invoice information into the company’s SAP system. This process is currently extremely manual and consumes up to 75% of the AP team’s day during peak times. At a volume of more than 200 invoices daily, the AP team was struggling to keep the SAP system current with all invoice information. This task was crucial to operations as vendors place more orders during certain months of the year and often place orders that need to be completed quickly. To increase accuracy, efficiency, and customer satisfaction, the business decided to use RPA to automate the process. However, to automate this process in its entirety and fully optimize the results, Optical Character Recognition (OCR) was integrated within the automation to read PDF invoices and enter the data accurately into SAP. Although this process was originally low-hanging fruit in terms of possible processes fit for automation, once automated, this task became one of the biggest wins for the company’s RPA program.
The Shared Services division of a large manufacturing company is responsible for overseeing the payout of wages and salaries to their employees across a number of different plants and locations both inside and outside of the United States. As various employee groups have their hours logged differently and with different ranks and positions comes a different wage or salary, this process was lengthy and highly repetitive as it consumed a majority of each pay-date to enter the data and wait for reports to generate across multiple different pay groups. Find out how this automation was able to save the company up to 80% of their original manual labor.
The Accounting Department for a Major Global Law Firm is responsible for stacks of unused checks that need to be voided in an internal accounting system. This process involves looking up each check in an internal folder that is updated every day to capture the status of each check, whether it be outstanding or unpaid, and then voiding the unused checks and uploading invoices into the accounting system. This process is a year-round task that fluctuates in volume and on average requires the finance team to void tens of thousands of checks monthly. This case study dives into the creation of a process automation bot to read checks with OCR, determine the check status, fill out all the relevant information in all internal accounting systems, and cancel the voided checks, thus saving the accounting department a substantial amount of time.
A Major Loan Servicing Company is responsible for managing their borrower’s suspense accounts. The administrative department is responsible for looking through 160 loans daily and moving money from the suspense account to make loan payments to the appropriate area on behalf of their clients. This task takes up 4 hours of every workday and is extremely tedious and repetitive, so it was an ideal candidate for automation.
A Global Oil & Natural Gas Exploration/Production Company employs a team of Financial Analysts each month to close out all projects from the previous month and run queries on project cash flows. Using finance department applications and a business warehouse system in SAP, this important process takes the team of Financial Analysts 10 days at the end of each month plus 20-30 hours of overtime for each team member to complete the job. To reduce errors, produce a better analysis of business projects, and eliminate the need for overtime, Accelirate created a unique 10-Day Automation that involves the use of a Business Intelligence Analyzer tool and an automation process that is unique for each day of the Equity Investment Reporting task.
A Leading Online Education Provider Company is affiliated with more than 300 schools in the U.S. and receives more than two million applications from potential students annually. The application requirement for each individual school associated is different and each application is submitted with several supporting documents, such as: Birth Certificate, Prior Schooling Transcripts, Proof of Address, and so on. During peak enrollment times, the client was employing, training, and maintaining 120 part-time enrollment officers on top of their 35 full-time employees to spend full days, plus overtime, processing enrollment applications. Specifically, the enrollment officers had to manually process all the documents, adjust all images submitted to meet an exact set of image guidelines and then label each application as accepted or rejected based on the fact that all documents were the appropriate formats and met the specified guidelines. Accelirate created a brand new solution combining Machine Learning, Computer Vision, and RPA to Automate this process for the Client entirely, saving them $1.2 Million Annually.
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.