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Mar 7, 2023
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Don't Compromise on Automated Document Processing Accuracy and Security

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Chief AI for Everyone Officer

Automated document processing has revolutionized the way businesses handle their data and information. Put simply, by automating data extraction and document processing, companies have greatly improved efficiency while reducing errors. However, relying on tools such as Intelligent Document Processing (IDP) for information extraction has implications on data accuracy and security that require careful consideration.

This article explains the significance of data accuracy and security for organizations automating document processing workflows, or those interested in doing so in the future.

Why is data accuracy important?

Accuracy is a crucial aspect of automated document processing as it determines the quality of the information used for decision-making and business process automation. Software vendors often make bold claims about the accuracy of their data extraction technology, with many claiming 99% or even 100% platform accuracy. However, prospective buyers need to carefully evaluate these claims using their specific business data before and after making a purchase.

This involves ongoing testing of a significant sample of documents to ensure accuracy, using ground truth data for comparison, and monitoring for any changes in the data source or format that could impact accuracy. Failing to do so could result in incorrect conclusions and misinformed decisions, potentially causing reputational damage, revenue loss, and other issues. It's important to understand the difference between stated accuracy and actual accuracy, as even small inaccuracies can have significant consequences.

Accuracy greatly impacts the success of the overall process and the validity of the application. While this is true of all applications that use automation in the document and data processing steps, consider the following cases:

  • Insurance: An insurance company automates data extraction from customer policy applications. To determine the policy coverage, terms, and cost, the data must be extracted accurately. If the information extracted is incorrect, the policy may be underpriced, overpriced, or contain inaccurate coverage, which could lead to significant financial losses for the insurance company and inconvenience for the customer. For example, if the customer's age is inaccurately recorded as 35 instead of 45, the premium amount charged could be too low. If a claim is filed and the customer's true age is discovered, the insurance company may deny the claim, causing the customer to incur significant out-of-pocket expenses.
  • Healthcare: In the healthcare sector, accurate automated data processing is critical for patient care and safety. Errors in patient medical records could result in incorrect diagnoses, treatments, and medication prescriptions. The accuracy of patient information is crucial for ensuring that healthcare providers have the right information to make informed decisions about patient care.
  • Accounts Payable: Data errors in the accounts payable sector can result in significant financial losses for a company. When invoices and payments are processed inaccurately, this can lead to overpayments, duplicate payments, incorrect payments to the wrong vendor, or delayed payments to vendors. This not only affects the company's cash flow and bottom line, but it can also harm the company's relationships with its vendors. Inaccurate data can also make it difficult for a company to stay compliant with government regulations and industry standards. Additionally, if auditors find errors in a company's accounts payable records, this can lead to fines and legal penalties.
  • Public Services and Enterprises: In the public sector, data inaccuracies can compromise the public's trust in government institutions, as the public may begin to question the validity of the information being released by the government. Furthermore, wrong data can also lead to inefficient and ineffective decision-making by public officials, as they rely on the data to make informed decisions that impact the public.

Common causes of data inaccuracies in automated data processing

There are several common sources of errors in ADP, including

  1. Poor Quality of Input Data: Automated document processing relies on high-quality input data to produce accurate output. If the input data is poor quality, such as images that are too small, too blurry or have poor contrast, the output can be inaccurate.
  2. Formatting Issues: Automated document processing systems can struggle to interpret the text that is not in a standard format. This includes text that is written in different font types, sizes, or styles, or text that is written in columns or tables.
  3. Lack of Training Data: Automated document processing systems often require a large amount of training data to learn the patterns and structures of the documents they are processing. If the training data is not representative of the actual data that the system will process, the results can be inaccurate.
  4. Ambiguous Data: Automated document processing systems can struggle with ambiguous data, such as dates that are written in different formats or text that can be interpreted in different ways.
  5. Technical Issues: Technical issues can also lead to errors in automated document processing. This can include software bugs, system crashes, or network disruptions.
  6. Human Error: Finally, human error can also play a role in errors in automated document processing. This can include incorrect manual data entry, incorrect configuration of the processing system, or incorrect interpretation of the output.

Metrics for assessing data accuracy in automated document processing

Judging the accuracy of automated document processing tools can be challenging. Many vendors use confidence intervals to indicate accuracy. In reality, confidence intervals reflect the degree of certainty that a software tool has in the accuracy of its recognition or verification results, and is generated by a complex algorithm that takes into account various factors such as image quality, lighting, scanning equipment, pen stroke, and paper type, among others. However, it's important to note that confidence intervals are not a measure of the absolute accuracy of the recognition or verification results, and must be used in conjunction with other metrics, such as the operating point, to obtain a complete picture of system accuracy.

The operating point sets the standard for success, determines the return on investment, and is the basis for measuring performance. It is composed of two numbers: the read rate and the error rate. For example, if the operating point is 85% read rate and 1% error rate, out of 100 documents, the software will successfully read 85 and have an error in 1 document. The remaining 15 documents will need to be reviewed by a human.

It's important to note that humans are likely to have a higher error rate compared to software tools, so it's best to use a combination of software and trained personnel for the most accurate and efficient results. At super.AI, we’ve engineered humans into our document processing workflow with our Data Processing Crowd, an on-demand resource pool of trained experts that can be scaled up or down as project requirements evolve.

The key to determining the operating point for automated document processing projects is to utilize confidence values. This involves collecting a large sample of data, including accurate answers input by humans, and evaluating the recognition results against the truth data. A skilled data specialist could then analyze this information and determine the operating point, which is the optimal balance between the read and error rates. This operating point can be fine-tuned to meet the specific requirements of the organization, ensuring that the final results are both accurate and reliable.

We know this sounds like a complex undertaking. Our IDP platform was built to simplify the process of gauging accuracy for our users. Rather than worry about extensive testing, super.AI users simply define quality, cost, and speed thresholds at the beginning of a project then we take care of the rest. Using a combination of AI, trained human workers, and over 150+ quality assurance mechanisms we’re able to guarantee outputs will meet or exceed user defined thresholds. If you would like to learn more about this, or get started processing your data on our platform, schedule a meeting with one of our experts.

The relationship between data accuracy and security in automated document processing

Inaccurate data can lead to security breaches and potential harm to individuals, organizations, and systems. For example, in a document processing system used for identity verification, inaccurate data can result in false positive or false negative decisions. False positives can cause inconvenience for individuals who are incorrectly flagged as having a different identity, while false negatives can result in unauthorized access to sensitive information.

In order to ensure data accuracy and security, organizations must implement robust security measures and checks, including strong authentication and encryption methods, regular data backups, and access controls. Encryption can be applied to data at rest, such as files stored on a server, as well as data in transit, such as email messages and file transfers. This helps prevent unauthorized access to sensitive information and protects against tampering, theft, and other malicious activities.

In addition to encryption, organizations could also consider using digital signatures and other security measures to ensure the authenticity and integrity of electronic documents after the data accuracy has been validated by standard methods. By combining encryption with data accuracy measures, organizations can reduce the risk of data breaches, minimize the potential for errors, and maintain the accuracy and security of their automated document processing systems.

Steps to ensure data accuracy and security in automated data processing

When choosing a document extraction tool for automated document processing, several factors must be considered to ensure that the tool meets the needs and requirements in terms of accuracy and efficiency. The first step is to evaluate the tool's ability to accurately extract information from a variety of document types, such as PDFs, images, and handwritten notes.

The choice must also consider the tool's compatibility with existing systems and data storage solutions, as well as its ability to integrate with other relevant software and technologies. Additionally, the level of security offered by the tool must also be considered, as well as the level of support and training available to ensure that the tool can be effectively used and maintained over time. Other factors to consider include the cost of the tool, its scalability, and its performance in real-world applications.

Some managerial steps that will help in enhancing data accuracy and security of automated data processing platforms include

  1. Defining the requirements: Determine the type of data that needs to be processed, what the desired outcome should be, and what the acceptable level of accuracy is.
  2. Selecting appropriate software and hardware: Choose software and hardware that is designed for the type of data processing required, and that is capable of meeting the desired accuracy standards.
  3. Implementing quality control measures: Implement processes to validate the data that is processed and monitor the quality of the output. This can include checks for missing data, incorrect data, and errors in the formatting or layout of the data.
  4. Using verification techniques: Implement verification techniques such as manual review, double-entry, and cross-checking of data to ensure its accuracy.
  5. Training personnel: Provide training to personnel who will be involved in the data processing to ensure that they understand how to use the software and hardware correctly, and how to apply the quality control measures effectively.
  6. Monitoring the process: Regularly monitor the automated document processing system to identify any potential errors or inaccuracies and to make necessary adjustments.
  7. Course correcting as needed: If errors or inaccuracies are identified, make the necessary adjustments to the process to improve the accuracy of the data.

Finding a trusted document automation partner

Data accuracy and security are essential aspects of automated document processing. Ensuring the validity and reliability of the information being processed through automated systems is crucial for organizations to make informed decisions, minimize errors and prevent negative consequences. Companies must take into account the various factors that can impact the accuracy of the information, such as the quality of source documents, the algorithms and technologies used, and the processes and controls in place to validate the information. By prioritizing data accuracy and security, organizations can gain the full benefits of automated document processing, including increased efficiency, improved decision-making, and enhanced customer experiences.

Keep in mind that you don’t have to worry about all of this on your own.  At super.AI, we take accuracy and security seriously and can be a trusted partner for your organization as it seeks to automate its document processing workflow. From enterprise-grade security to output guarantees, we have you covered. You can learn more about our compliance and internal policies in our trust center. If you would like to speak to someone about your specific document processing needs, don’t hesitate to reach out to us for a discussion.

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