07 Aug How can intelligent data processing software improve data accuracy and reduce errors in business processes
“Poor data quality costs organizations an average $12.9 million every year”. – Gartner, 2021
Data quality and errors are major concerns for businesses. Bad data can result in bad decisions even if you have the best data analytics and intelligence tools. Therefore, data quality must be maintained at every stage from sourcing to processing of data. In document-heavy industries, data resides in the millions of documents that are processed every day and intelligent document processing software solutions can help in ensuring data quality here. Let’s explore how?
Intelligent document processing solutions are part of business process automation tools that help automate repetitive, tedious tasks and workflows to improve efficiency, save costs, and get better accuracy. They help in streamlining processes and standardizing operations. Extracting data from documents is a crucial but tedious and time-consuming task, especially when you are dealing with millions of documents. When it comes to document processing, simple automation is not always effective or possible. The challenges associated with automating document processing are:
Lack of standardization
Documents come in various formats, structures, and layouts, making it challenging to develop a one-size-fits-all automation solution. Handling variabilities in document types, such as invoices, contracts, or forms, requires robust algorithms and techniques to accurately extract information.
Data Extraction Accuracy
Automated document processing involves extracting specific data elements from documents, such as names, addresses, or invoice numbers. Achieving high accuracy in data extraction is challenging due to variations in font styles, formatting, or handwriting. Ensuring accurate extraction is crucial to prevent errors and ensure data integrity.
Quality of documentation
The quality of scanned documents, especially those submitted by suppliers or customers, can be questionable, impacting the accuracy of data extraction.
Complex documents
Multi-page documents with tables, content, and images spread across sheets can complicate the data capture process and affect data extraction accuracy.
Handwritten or Unstructured Text
Dealing with handwritten text or unstructured data within documents poses a significant challenge. Handwriting recognition algorithms may not always provide accurate results, leading to potential errors.
Language and Localization
Documents can be written in different languages, and document automation systems should support multilingual processing. Language-specific challenges, such as different character sets, grammatical rules, or text direction, need to be considered to ensure accurate processing and extraction across various languages.
Data privacy and security
Processing documents containing sensitive information like personal or financial data requires robust measures to ensure data privacy, security, and compliance with regulations.
Challenges in automating document processing
All these challenges can lead to errors and contribute to bad data. Manual document processing results in errors during data entry and can cause huge productivity loss. With structured and templated documents, template-based automation can be done. When dealing with unstructured complex documents, automation is difficult, and businesses have to rely on manual document processing.
To address the challenges in automating document processing without compromising on efficiency or accuracy businesses need intelligent document processing solutions that use advanced technologies, such as optical character recognition (OCR), machine learning, and NLP, along with robust data validation techniques. Intelligent document processing is not simply about extracting data but understanding documents and ensuring data integrity. Some of the ways in which IDP overcomes the document automation challenges are,
Intelligent document classification
IDP solutions use advanced machine learning algorithms and can be trained to recognize, read, and classify data based on the nature of documents. They are trained to capture or recognize information based on content, layout, and context and can effectively categorize varying document types and formats.
Pre-processing
IDP solutions include robust pre-processing methods to enhance document quality. By removing noise, improving clarity, and preparing documents for extraction, the accuracy of the data processing solutions can be improved even with low-quality scanned documents.
Complex document processing
IDP solutions can understand the structure and layout of complex documents. With continuous training and feedback on large datasets, the IDP solution can retrieve relevant content from multiple pages within a document, ensuring accurate data extraction.
Handwritten document processing
IDP solutions leverage machine learning algorithms trained specifically for handwriting recognition. Additionally, advanced natural language processing techniques can be employed to extract information from unstructured text, enhancing the accuracy of data extraction from handwritten or narrative sections.
Data privacy and security
IDP solutions prioritize data privacy and security by implementing robust encryption and access controls. Compliance with data protection regulations is ensured, and regular security audits and employee training are conducted to maintain a high level of data privacy and security throughout the document processing pipeline.
Customer onboarding
When onboarding a new customer, there are many documents involved from policy forms and contracts to medical certificates and IDs. Manual processing can be extremely lengthy but with IDP solutions, you can complete the onboarding process in minutes.
Claims processing
Processing insurance claims involve documents like claim forms, supporting documents, certificates, and medical documents. Validation of insurance claims is also a major step in the process. IDPs can simplify the entire claims processing and validation stage.
Bulk data entry
At times of natural disasters or mishaps or in certain seasons, there might be many insurance claims coming all at once. IDPs can do bulk document processing and easily extract data from complex multipage documents and feed it to downstream systems.
KYC processing
Identity document processing is an important part of insurance policy and claims management. IDP solutions can process multiple kinds of IDs like driver’s licenses, passports, etc, and verify and validate the information.
Claims automation
IDPs can help automate document workflows in the insurance industry and do faster, more accurate, and more reliable data processing with minimal or no human intervention. This will help in faster claims settlements and increased customer satisfaction.
Invoice processing
Invoices play an important role in the insurance industry and are different from typical business invoices. Processing invoices is a crucial task in the insurance sector and IDPs can help insurance firms process invoices of different categories with high accuracy.
Mailroom automation
Insurance companies deal with thousands of emails every day and with mailroom automation, IDPs read, understand, and extract information from emails and attached files and feed them into the database.
Fraud detection
Fraudulent claims are a major challenge in the insurance industry. Fraud detection involves identifying anomalies in insurance documents and intelligent document processing software can validate data like signatures for fraud detection.
Data collation and analytics
The scale of documents processed in the insurance industry can be intimidating. Insurance industry deal with big data which can be leveraged for advanced analytics and decision-making. IDPs help convert data from complex documents to standardized formats and create centralized data repositories that can be used for analytics.
Risk profiling
In order to accurately assess the level of risk associated with insuring an individual or entity, insurance companies typically require a variety of documents that provide information about the applicant’s background, financial situation, and other relevant factors. IDPs can help in extracting valuable information from documents to conduct risk assessments and risk profiling.
Role of IDP in error reduction
Intelligent document processing solutions can provide high data accuracy and reduce errors in business processes as data accuracy and integrity are ensured during every stage of the data extraction process. Let’s take a closer look at the steps,
Document Capture
In the initial stage of document capture, IDP employs various steps like automatic image enhancement to improve document readability, noise reduction to eliminate background artifacts, and deskewing to correct skewed or tilted documents. By optimizing the document image quality, IDP enhances the accuracy of subsequent data extraction processes.
Document Classification
IDP uses machine learning algorithms to classify documents based on their content and context. During this stage, IDP checks for errors by comparing the document’s characteristics against pre-defined classification models. Any misclassifications or errors can be flagged for review or corrective action, ensuring accurate routing and processing of documents.
Data Extraction
IDP employs optical character recognition (OCR) and machine learning algorithms to extract data from documents. To ensure data quality, IDP uses techniques such as validation rules, regular expressions, and data format checks. By validating extracted data against predefined rules, IDP identifies potential errors or inconsistencies. Any discrepancies are flagged for review and verification to ensure data accuracy.
Data Verification and Validation
Data verification and validation involve cross-checking the extracted data against existing databases or external sources to ensure consistency and accuracy. IDP can also perform data validation using business rules or reference data to identify errors, duplicates, or missing information. For instance, you can use field-based confidence scores and request human validation for fields with a confidence score below a threshold value.
Exception Handling, Human Reviews, and Error Resolution
In case of errors or exceptions encountered during document processing, IDP includes robust exception handling mechanisms. It flags potential errors, missing data, or inconsistencies for manual review or corrective action by human operators. IDP provides intuitive user interfaces and workflows to facilitate error resolution, allowing users to correct and validate the data before final processing.
Data Integration and Output
Once the data has been extracted and verified, IDP integrates the processed data with other systems or workflows. During this stage, IDP ensures data quality by performing data mapping, transformation, and formatting checks to match the required output format or destination systems. Any data transformation errors or discrepancies are identified and resolved before the final output is generated.
How IDP ensure data quality with every stage of document processing
Throughout each step of the document processing stages, IDP employs error detection, validation, verification, and exception handling mechanisms to ensure data quality.
Intelligent document processing solutions get better with continuous training and improvement of automation models and algorithms and they enhance accuracy and adaptability to evolving document formats and requirements.
By improving accuracy and reducing errors, IDPs help businesses in
Embracing IDP for data accuracy
Intelligent document processing software solutions like Docketry reduce human errors and ensures accuracy of at least 95% with complex documents and lets businesses automate their document workflows with ease. They are scalable and flexible and can deal with high volumes of documents in any format. IDPs understand documents like humans do and get better and more accurate with every document. With manual validation and feedback, you can achieve 100% accuracy and can ensure the quality of data that is fed to downstream processes for decision-making.
