Data Capture for Accounts Payable

Transcription

1 Data Capture for Accounts Payable Transforming Documents into Business Ready Data Q Featuring Insights on... Building a Case for Data Capture Underwritten in part by Imaging Solution Road Blocks The Data Capture Process Different Components of OCR Applications Manual vs. Automated Data Capture

2 Table of Contents Executive Summary...1 Building a Case for Data Capture...2 Imaging Solution Road Blocks...6 AP Automation From Front to Back...7 The Data Capture Process...8 Putting the Pieces Together...11 Manual vs. Automated Capture...12 Various Forms of Image Recognition...14 ABBYY...16 Conclusion...20 Research Methodology...20 About PayStream Advisors...20

3 Executive Summary The concept of Optical Character Recognition (OCR) has been around for quite some time. OCR is the technology that allows software to interpret machine printed text on scanned images. The idea of utilizing Accounts Payable (AP) Data Capture OCR technology is to reduce the amount of time AP employees spend processing invoices. With an automated OCR system, as much as 75 percent of the time spent using conventional methods of AP processing are saved, resulting in fewer errors, reduced costs, quicker invoice processing cycles, visibility, workflow, improved compliance and reporting. While paper invoices continue to decline, 52 percent of business-to-business (B2B) invoices are still traded on paper. Paper invoices require manual data entry or hand-keying into an accounting system. Invoice data capture software scans the invoices and scanned invoices are recognized with OCR. Relevant data is located, errors are corrected and data is exported directly to an organization s accounting software database. Advances in OCR technology has created intelligent, more accurate and highly scalable data capture and document processing software that automatically transforms documents into business-ready data; thus increasing efficiency by automating paper-based business processes. To reap the benefits of OCR technology, more organizations, particularly those with high volumes of invoices, have started scanning paper invoices upon receipt. Scanning invoices at the front-end or upon arrival permits invoices to be entered into an electronic workflow system for faster and more accurate processing. Scanning invoices at the front-end is also a gateway for end-to-end invoice processing that will allow companies to simply open the mail and process stacks of paper invoices straightthrough, without any special document preparation or manual corrections. This report examines current demand for OCR/Data Capture solutions and should serve as a guide for AP decision-makers currently considering a data capture solution. It also includes an overview of OCR/Data Capture solutions that are available on the market today. For more information of this and other research reports available from PayStream Advisors, visit PayStream s Research Vault.

4 Building a Case for Data Capture Paper invoices require manual, time consuming and error-prone data entry. By automating this process through invoice data capture software, the 52 percent of survey respondents that report trading B2B invoices via paper can save time, money and increase accuracy, see Figure 1. Figure 1 Breakdown of Invoices via Receipt Method 52% 31% 12% 5% Paper Electronic Invoice Portals / EDI Fax Paper invoices create challenges in invoice management. According to 2013 survey results, half (50 percent, down 2 percent from 2012) listed handling of paper invoices as their number one invoice management challenge, followed by manual data entry and inefficient processes (37 percent), see Figure 2. The good news is that the trend is moving in the right direction. As the use of paper to trade invoices continues to decrease, so to do the challenges associated with paper invoice handling.

5 Figure 2 Majority of invoices received in paper format 50% Challenges in the Invoice Management Process Manual data entry and inefficient processes 37% Manual routing of invoices for approval 29% High number of discrepancies and exceptions 12% Inability to approve invoices in time to capture discounts 15% Decentralized invoice receipt 12% Lost or missing invoices 13% Lack of visibility into outstanding liabilities 8% Over one-quarter (28 percent) of companies surveyed said they scan invoices on the front-end as soon as they are received and data shows that 6 percent of companies are currently deploying an imaging solution and will go live in the next 6 months, see Figure 3. Even more encouraging is that 25 percent of respondents report they are currently considering implementing an imaging solution.

6 Figure 3 We currently use an imaging solution - we image invoices at the front-end as soon as they are received. 28% Where do you stand with regards to the usage of an imaging solution? We currently use an imaging solution - but we only use imaging at the back-end to archive invoice images. 18% We are currently deploying an imaging solution and will go live in the next 6 months. 6% We are considering implementing a front-end imaging solution. 25% We do not have an imaging solution and have no plans to implement one. 23% With the benefits of front-end data capture clearly established, progressive companies are moving to convert static paper invoices into dynamic data to increase operational efficiency, add management control and visibility into spend, and comply with Sarbanes-Oxley reporting rules.

7 Figure 4 Top Financial Automation Goals for 2013 Automated workflow for invoices 33% Increase electronic invoicing 29% Automate payment processing 18% Implement invoice imaging 16% ERP application upgrade 3% Outsource portions of AP process 1% As more companies strive to achieve the benefits of front-end imaging and data capture, it s no surprise that 16 percent of companies report implementing invoice imaging as their top financial automation goal for 2013, see Figure 4.

8 Imaging Solution Road Blocks What s keeping 23 percent of companies on the sidelines? The number one reason organizations have not implemented an imaging solution is the belief that current process work (33 percent), followed by lack of budget (25 percent) and no executive sponsorship (14 percent), see Figure 5. Figure 5 Reasons Organizations have Not Implemented an Imaging Solution Current processes work Lack of budget 25% 33% No executive sponsorship 14% Lack of understanding of current available solutions 11% We do not think there will be a ROI 7% Lack of technical resources to manage an automated solution 0% Other 10% It s not unusual for large or even medium-sized companies to have more than 20,000 vendors spread over multiple continents. Some have over 80,000. Larger vendors, who, themselves know how hard it can be to work with so much paper, may submit their invoices electronically, or, if on paper, at least in a scan-friendly format. Others, there s no way of telling. Anyone who has ever tried to decipher a doctor s prescription can understand the challenge a machine might face translating handwritten squiggles into alpha/numeric values. Solution providers compete on whose solution can read the widest range of documents, in the widest range of languages, in the shortest time, with the least amount of document prep and manual corrections. Some solutions learn; some can be taught; and some, especially at the lower end, simply promise to do the best they can, eliminating the need for manual keying on most structured documents and leaving dicer data for human intervention.

9 AP Automation From Front to Back Invoice imaging is often the first step in automating accounts payable. Imaging helps tackle the toughest part of a manual accounts payable process, which is paper. Once a paper invoice is translated into an electronic document, it becomes much easier to manage. Front-end imaging is the most beneficial, since it captures an invoice as soon as it enters the organization and can be easily tracked throughout the entire process. By connecting with an automated workflow system, the invoice approval and payment process increases which leads to more on time payments and discounts captured. Quicker approval of invoices ranks as the biggest benefit of front-end imaging, see Figure 6. Figure 6 Biggest Benefits of Front-End Imaging Quicker approval of invoices 59% Increased employee productivity 47% Lower processing costs 33% Fewer lost invoices 31% Improved visibility over liabilities 22% Reduction in late payment penalties and interest 16% Better compliance with regulatory requirements (SOX, FASB) 9%

10 The Data Capture Process This report focuses on OCR technology as it relates to accounts payable functions and improving the invoice-to-pay cycle. The graphic below illustrates the components of most OCR applications, although different vendors may place greater emphasis on one component over another. Figure 7 Different Components of OCR Applications Data Extraction Reporting & Analysis Data Validation Quality Control Document Classification Automatic / Manual Indexing Data Capture and Extraction Today s best practices call for front-end imaging and data capture. Paper documents are scanned upon receipt, either at a central processing site or a remote facility, to digitize the data. Information is then extracted using one of several types of recognition technologies:»» OCR and ICR are character-based recognition systems and are acronyms for Optical Character Recognition and Intelligent Character Recognition, respectively.»» OMR, or Optical Mark Recognition, recognizes the presence of marks put in specific locations on an invoice.»» Barcode Recognition is another method of electronic data capture.

11 All of these technologies offer excellent accuracy, usually 80 percent or above on the first pass read rate, depending upon the quality of the original document and the hardware used. Accuracy can be increased to more than 99 percent by validating the information against back-end databases. Today s solutions support a wide range of brand name hardware, including network and desktop scanners. In addition to offering image and data capture, the applications provide image enhancement features such as de-skewing, de-speckling, text smoothing, edge trimming, dot/line/border, flipping, and more. Once invoices have been scanned and enhanced to optimize recognition, the solution uses scripted rules to locate, extract, and validate the desired information, regardless of its location on the invoice. Data Validation To ensure accuracy, OCR solutions check data extracted against user-defined business rules, as well as field specification, expressions, check sums, and line item validations. The software may use automatic table lookup to validate data with client databases. Specific items, such as purchase order (PO) or vendor numbers can also be used to pull related data such as vendor name and address. Some OCR solutions can sort and route invoices, based on type, to specific data validation assistants. In addition, some allow the customer to bypass manual validation and go straight through to content management storage once minimum data capture confidence levels have been met. Even further tweaking can sometimes be possible, including changing the minimum data capture confidence levels with a system parameter setting, or allowing different data capture confidence levels for different document invoice types. Quality Control Invoices where data capture accuracy falls outside the pre-defined confidence levels will be moved to a separate work queue where they can be manually verified and corrected by an AP operator. Many solutions display the invoice image and the data that the OCR engine has seen side-by-side. If there is a failed validation or low confidence character recognition, then the field is highlighted for acceptance or correction by the staff member. If there are none, then the processor simply clicks to the next invoice. Most OCR solutions available on the market today provide an easy-to-use and robust interface for operators to validate information, including color coding the exceptions.

12 Document Classification and Indexing Most solutions provide the ability to capture specific fields, classify documents using key values and features, view invoice information, and correct errors or lowconfidence characters. Some technology offers automated training methods that setup and facilitates classification. These auto learning classifications depend upon document samples that the software uses to create templates. Specific zones or data fields can often be included as part of a document image index. Smart indexing may be an option when and index field such as vendor or PO number is available in the document and in the client s database. When the field is not present in the document, index lookup fields are used to find the information in the database table. Reporting and Analysis OCR solutions usually come bundled with a variety of standard reports, including batch status, operator statisitics, accuracy of information extracted, etc.

13 Putting the Pieces Together Successful front-end data capture requires the development of a fault-tolerant system capable of accurately extracting data from a wide variety of non-standard invoices, of inconsistent print quality and page count, in random order, and in multiple languages. Figure 8 illustrates front-end processing using scanning and OCR. Figure 8 Front-End Processing Using Scanning and OCR OCR and Data Capture Document Receipt Scanning Classification are the central elements of modern AP automation initiatives. Data Capture IDC / OCR Validation Image & Data Storage Retrieve & Export

14 Manual vs. Automated Capture There are two ways to extract data from these front-end OCR scans manual and automated. 1. Manual In a manual data capture system, AP staff key information from invoice images into forms on their computers. This method has traditionally been most effective for companies that get a lot of handwritten, or other non-standard invoices. 2. Automated With automated extraction, the computer analyzes scanned documents and extracts data according to programmed criteria. This process can dramatically reduce or eliminate manual keying of data, but it is also subject to error and typically requires some kind of human oversight. Automated capture raises the bar for image quality, because machines are typically not as intuitive as humans when it comes to interpreting partial or garbled data. PayStream analysts attribute the steady increase in front-end imaging solutions to improvements in invoice pre-processing and data capture tools. Invoice preprocessing is the methodology for using data capture and pre-defined business rules to allow the majority of invoices to bypass accounts payable. By bypassing AP, invoices go directly to workflow for coding and approval, and are completely automated with PO or contract matching, and go directly to the ERP. PayStream research shows that in 2013, 48 percent of survey respondents reported 100 percent accuracy, meaning they had no manual corrections in data entry. This number increased from 42 percent in 2012, see Figure 9. Figure 9 Front-End Imaging with Manual Data Entry 42% 48% % 19% 17% 12% 14% 14% 7% 5% Zero (None) 1-25% 26-50% 51-75% More than 75%

15 The number of companies utilizing front-end imaging that required manual data entry on over 75 percent of scanned invoices dropped from 17 percent in 2012 to 14 percent in As solutions continue to mature and improve, electronic data capture accuracy rates will continue to increase, resulting in less manual keying. Figure 10 Front-End Imaging with OCR/IDR 56% 48% % 17% 19% 13% 8% 9% 3% 3% Zero (None) 1-25% 26-50% 51-75% More than 75%

16 Various Forms of Image Recognition The structure of a business s incoming documents is an important consideration in selecting a data capture solution. With some so-called structured documents, such as an employment application, information always appears in the same location on the page. For example, an applicant s name may always appear in the same box in the same place on the form. With unstructured documents, such as a hand-written note, the information is more free form and can appear virtually anywhere on the page. Invoices, which tend to vary in format from vendor to vendor, are called semistructured documents and fall somewhere in between. The types of documents a company processes will have a direct bearing on the extraction solution it selects. Figure 10 Characteristics of Structured/Unstructured Documents Document Type Structured Semi Structured Unstructured Document Characteristics Examples Ideal Data Capture Method Use By Accounts Payable Familiar data appears in familiar places Credit card applications and medical claim forms Template based OCR/ICR Low volume operations for relatively few invoice formats Familiar data appears in both familiar and unfamiliar places Invoices, payment orders Intelligent Data Capture, keyword search Invoice processing Unfamiliar data appears in unexpected places Contracts, hand written notes Pattern recognition technology based on scripted rules High volume operations and many invoice formats Templates Fast and fairly accurate, template-based image recognition works like a digital cutand-paste, with users defining fixed capture zones or floating zones tied to visual anchor elements to accommodate routine position shifts in documents during scanning. The biggest advantages of template-based recognition are speed and accuracy with first-pass accuracy approaching 100 percent after validation. The biggest drawback is that a different template is required for each type of document, and each template requires 15 to 30 minutes of setup time.

17 Template-based recognition works best with structured or semi-structured documents. Free-form Free-form image technology relies on keywords, rather than search boundaries, to tell the solution what data to extract. Often used in conjunction with directional clues, a typically free-form instruction would tell the system to extract data found to the right of a keyword. Free-form recognition is particularly valuable when dealing with semi-structured and unstructured documents that cannot be captured effectively using templatebased recognition. Some free-form solutions use rules to learn data locations, although this process needs to be closely monitored to prevent false assumptions. The trade-off, with this, and any other form of intuitive recognition, is that it typically takes longer for the system to think than to just cut and paste according to the template. Once a document has been recognized, the system can be programmed to remember that type of document and apply a virtual template to speed data recognition. Intelligent Data Capture A sophisticated solution with fuzzy search capabilities, Intelligent Data Capture (IDC) enables end-users to accurately extract content from documents of all types, without prior knowledge of the layout of the documents or the type of data they contain. An ideal IDC engine is able to correctly identify and sort batches of both structured and unstructured documents on the fly, locate data fields in both kinds of documents, and extract the desired content from those fields. An IDC solution does not require time to code elaborate sets of rules or design form templates. A simple training procedure that utilizes a relatively small batch of representative data samples is all the preparation required. IDC solutions dynamically compare extracted information with disparate enterprise systems to match and validate data. Fault-tolerant search capabilities improve extraction results by allowing the solution to search the complete document text for similar information rather than relying on keyword searches. It also makes IDC solutions language-agnostic, allowing organizations to process high volumes of international documents in multiple languages.

18 ABBYY Founded in 1989, ABBYY is a leading provider of document recognition, data capture, and linguistic technologies and services that transform information and enable greater productivity for business and personal computing applications. ABBYY s key products include the ABBYY FineReader line of optical character recognition (OCR) applications, ABBYY FlexiCapture line of data capture solutions, and development tools, supporting a variety of platforms and computing environments. ABBYY solutions streamline with unmatched accuracy, leading the way in the global information revolution. Paper-intensive organizations from all over the world trust ABBYY solutions to automate time- and labor-consuming tasks and to streamline business processes. ABBYY processes 9.3 billion pages a year for clients in a number of industries including healthcare, government, insurance and transportation. ABBYY s allin-one solution scales from a stand-alone installation for small and medium size enterprises or to a distributed enterprise system. ABBYY OCR applications are shipped with equipment from the world s top manufacturers such as Epson, Fujitsu, Fuji, Xerox, Microtek, Panasonic, Plustek, Ricoh, and Toshiba. Website Founded 1989 Headquarters Moscow, Russia Other Locations Milpitas, USA; Ontario, Canada; Munich, Germany; Kiyv, Ukraine; Theale, UK; Taipei, Taiwan; Tokyo, Japan; Sydney, Australia; Nicosia, Cyprus Number of Employees Number of Customers 30 million (across all ABBYY products) Key Clients Datacap, Fujitsu, EMC Documentum, IBM, Microsoft, Panasonic, Xerox, Ricoh, Pitney Bowes, HP, Nokia, Toshiba, HTC, Lexmark, Datacap, KnowledgeLake, Konica Minolta Partners / Resellers ScanStore, WiseTrend,, Scantron, Digital Documents, CONARC, Process Fusion Transactions Processed 9.3 billion pages per year Annually Awards / Recognitions KMWorld Trend-Setting Product; AIIM Best Practice AWARD; Buyers Lab; PC Magazine; ZDNet Editor s Choice Solution Name ABBYY FlexiCapture Year Introduced 2001

19 Key Differentiators»» Accuracy in locating and recognizing data in documents of any complexity, resulting in increased efficiency in AP business processes and reduced costs of data entry by eliminating manual labor.»» Rapid deployment and onboarding of new suppliers through Invoice Accelerator.»» All-in-one solution scales from a stand-alone installation for SME or departmental level to a distributed enterprise system.»» Flexible workflows accommodate any specifics of the business process offering database lookups, custom processing stages, connecting thirdparty processing engines, and modification of the user interface. Installation ABBYY s average installation takes between two and four hours and can be handled by an administrator with basic knowledge of Windows OS and IIS. To ensure accuracy, ABBYY s FlexiCapture solution is implemented by certified professionals. ABBYY also supports remote installation. Solution engineering and support set-up times vary, depending on the project and can range from 8 hours to 80 hours, depending on the number of document types and their complexity. Configuration Data fields are defined per project, but the captured fields are typically Invoice Number, Invoice Date, Vendor Name, PO Number, Total Amount and Line Items. For semi-structured documents, such as invoices, FlexiCapture uses a proprietary FlexiLayout technology based on keywords and the relative position of elements including text, separators, barcodes, white spaces, etc. A single FlexiLayout can be created to capture defined fields from a wide range of common invoice formats. ABBYY customers can also choose to create templates for specific vendors that account for the bulk of their incoming invoices. This ensures an exceptionally high level of accuracy and processing speed. ABBYY recently launched their Invoice Accelerator that puts the power of adding new forms and onboarding new vendors in the hands of their customers. Invoice Accelerator requires no coding, no layout modification and no deep knowledge of technology. The solution is extremely customizable and flexible, allowing users to quickly and easily add new vendors themselves by simply adding new vendor names to the database table. Document Acquisition Intelligent rules eliminate the need for barcodes and separators, with no sorting required, except to make sure that multi-page documents are arranged in the proper sequence. ABBYY customers have the flexibility to choose the parameters

20 for pushing documents to manual review, as this depends on the customer s specific requirements. ABBYY recommends 300 dpi scanning; however, 200 dpi is supported. Classification Documents captured by FlexiCapture can be automatically classified using imagebased or content-based approaches. In the image-based classification mode, ABBYY s system can quickly process similar looking documents, such as forms or invoices from the same vendor, which allows customers to achieve a much faster data extraction. The content-based approach helps classify and process unstructured documents that may have very different layouts within the same document type, such as contracts, letters, etc. Automatic Learning FlexiCapture can learn parts of the basic document layout automatically from sample images. For the most precise capture; however, especially with automated document separation, manual tweaks may be required. In version 11, FlexiCapture provides operator-assisted learning, that allows a customer to point and click on the required field that the software failed to locate. The software will utilize this information to build rules for finding data on subsequent documents. ABBYY typically requires five to ten samples of each type of document to teach the system a document. Validation / Correction Business rules are validated automatically on the server and may include database validation, list validation, format check, and any additional scripted rules. Rule errors are presented to the operator at the visual validation stage. The number of fields that need to be manually corrected or entered varies from zero to several, depending on the project set-up. Manual corrections, such as entering fields that have not been captured automatically, take five to 30 seconds per document. In addition rubber-band OCR can be utilized. The ABBYY solution supports visual validation by a single user, double validation (where two users verify the same fields and their results must coincide), distributed validation (where field of a document are split between two operators), in addition to other customized user validation configurations. The number of invoices that can be added per hour depends on the number of operators, 200 invoices per hour by a single operator, while up to 10,000 per hour can be added by multiple operators.

21 Matching/Error Detection ABBYY s solution provides automatic data matching and does not require separate software. Line item definitions can either be defined on a per-vendor basis or, in the case of a generic definition, the operator can enter them manually via rubber banding. Maintainability There is sensitivity to the introduction of new document formats requiring new documents to be defined, unless they fall under the generic definition, in which case changes may not be required. FlexiCapture recognizes nearly 200 languages including Chinese, Japanese, and Korean. Pricing ABBYY software is sold primarily through VARs and system integrators who provide the quote and the first line of product support. Pre-sale support and technical support from ABBYY are available from 9 to 5 on workdays by phone and . The software is purchased, not leased, and ABBYY charges an annual software maintenance and upgrade assurance fee, typically added on as a percent of the annual software licensing fee.

22 Conclusion While the use of paper to trade B2B invoices continues to decline, the fact remains that over half of businesses are still trading invoices on paper. This over reliance of paper invoices in an ever increasing electronic marketplace has progressive accounts payable practitioners investigating proactive solutions to paper paralysis. With the benefits of front-end data capture clearly established, more companies are now converting static paper invoices into dynamic data to increase operational efficiency, add management control and visibility into spend and comply with Sarbanes-Oxley reporting rules. With the average company receiving invoices from more than 20,000 vendors in a variety of formats, finding a scanning and data capture solution flexible enough to accommodate their specific vendor profile is critical. This 2013 Data Capture for Accounts Payable report and the accompanying ABBYY profile provide a good overview for companies currently considering an OCR/Data Capture solution. Research Methodology The findings in this report are based on the results of PayStream Advisors 2013 Invoice & Workflow Automation Adoption and Electronic Invoice surveys. Participants in the surveys included more than 300 AP professionals. Based on our experience and the number of respondents, the survey has a confidence level of +/-5 percent. About PayStream Advisors PayStream Advisors is a technology research and consulting firm that improves the way companies plan, evaluate, and select emerging technologies to achieve their business objectives. PayStream Advisors assists clients in sorting through the growing complexities of IT applications related to business process automation with the goal of making objective, analytical, and actionable recommendations. Wherever business process automation technology is an issue, PayStream Advisors is there to help. For more information, call (704) or visit us on the web at com.