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In many document-heavy industries, businesses spend around 35% of time and resources on data entry, error correction, and other document-related activities. Intelligent document processing has revolutionized how businesses deal with raw unstructured data in complex documents. IDPs help businesses take advantage of data in complex documents, converting them to usable data for advanced analytics, decision-making, and gaining a competitive edge in the market. What is intelligent document processing? Intelligent document processing(IDP) uses advanced AI/ML models to capture data from documents and convert it into structured data formats for future processing. IDP solutions use technology like artificial intelligence, machine learning, computer vision, optical character recognition, and intelligent character recognition. IDPs do not just extract data from documents, they use human-like intelligence to understand the documents and natural language recognition for context recognition. IDPs identify the document type, classify them and use the right algorithms to extract the data and convert it into the required format. In addition, they also use supervised or unsupervised learning to improve performance with every document. They go beyond simple OCR and help you do more with the documents. How can your business benefit from an IDP system Some industries are heavily dependent on documents and face a multitude of issues in processing and managing them. Digitizing data in documents is a great challenge due to non-standardized and complex forms, thousands of document variations, handwritten texts, errors and bad data, and resources and time required for manual processing. This results in productivity loss, longer turnaround times, delays in decision-making, and lesser customer satisfaction. With intelligent document processing, businesses can handle different types of complex documents with ease and process them with high accuracy within seconds. Let’s explore how IDPs solve industry-specific document processing challenges and drive business outcomes, Why is intelligent document processing better than other processing methods? The table below offers a comparison of intelligent document processing technology with manual processing and legacy processing solutions. It can be noted how IDPs use AI/ML to enhance human capabilities and improve productivity while saving time, resources and cost. How IDP solutions solve industry-based document challenges The table below offers a comparison of intelligent document processing technology with manual processing and legacy processing solutions. It can be noted how IDPs use AI/ML to enhance human capabilities and improve productivity while saving time, resources and cost. Insurance The insurance industry is one of the sectors that primarily rely on documents in various forms in all departments. From customer onboarding to claims settlement, every stage requires data from multiple document types to make decisions. Manual processing can introduce errors, and cause delays which can lead to false assumptions, a longer time for claims settlement, and customer dissatisfaction. The time and cost required for manual document processing in different stages are very high. 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. The common use cases for IDPs in insurance industries are for 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. Healthcare Hospitals and other healthcare providers deal with a number of documents every day. Healthcare workers deal with physical documents and electronic records for multiple patients and processing speed and efficiency are of utmost importance for providing the best patient care. Automating some of the document processes and workflows with intelligent document processing can significantly save time and effort in healthcare. Compliance and privacy are also very important in the healthcare industry and IDPs help achieve security and compliance goals while helping to manage patient records efficiently. The common use cases for

“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

Automated document processing in the Banking Industry: Streamlining Operations and Enhancing Efficiency Intelligent Document Processing (IDP) has emerged as a transformative technology for the banking industry. IDP offers numerous benefits and solutions to the challenges faced by banks in managing various types of documents, right from automating and streamlining work to processing multiple documents. In this blog, we will explore the top 4 reasons for banks should consider IDP solutions such as – what IDP can offer the banking sector, different types of document processing in the banking domain made easy with IDP solutions, the benefits of intelligent document processing, and the challenges faced in document processing, along with corresponding IDP solutions. What IDP can offer for the banking industry? IDP combines optical character recognition (OCR), artificial intelligence (AI), and machine learning (ML) algorithms to automate and extract data from various documents, such as KYC documents, loan applications, financial statements, invoices, and customer forms. In this blog, we will explore the top 4 reasons for banks should consider IDP solutions such as – what IDP can offer the banking sector, different types of document processing in the banking domain made easy with IDP solutions, the benefits of intelligent document processing, and the challenges faced in document processing, along with corresponding IDP solutions. Benefits of Intelligent Document Processing in Banking Enhanced efficiency: IDP automates repetitive and time-consuming tasks, reducing manual efforts and increasing operational efficiency. It enables faster processing of documents, leading to improved customer experience and shorter turnaround times. Improved accuracy: IDP minimizes human errors associated with manual data entry and document processing. By leveraging AI/ML algorithms, it ensures high accuracy in data extraction and document classification, reducing the risk of errors and compliance issues. Cost savings: With IDP, banks can significantly reduce costs associated with manual document processing, data entry, and document storage. It eliminates the need for extensive manual labor, saving both time and resources. Enhanced regulatory compliance: IDP helps banks adhere to regulatory requirements by automating data validation and ensuring consistency across documents. It enables better risk management and audit trails, reducing compliance risks and penalties. Enhanced customer experience: With faster processing times and reduced manual intervention, IDP enhances the overall customer experience. Banks can provide quicker responses, seamless onboarding, and faster loan approvals, ultimately increasing customer loyalty. Scalability: IDP is scalable and can handle high volumes of documents without compromising on accuracy or speed. This scalability is crucial for banks dealing with large customer bases and complex document processing requirements. Different types of banking document processing made easy with IDP solutions: In the banking industry, different types of documents require processing and analysis. Here are some key banking document types: Know Your Customer (KYC) forms: IDP can streamline KYC processes by extracting relevant data from identification documents, address proofs, and other required documents that helps to speed up customer onboarding and reduces errors. Loan applications: IDP can extract relevant information from loan applications, such as personal details, financial statements, and supporting documents, enabling faster loan processing. Account opening forms: IDP automates the extraction of customer data from account opening forms, reducing the time taken to onboard new customers and ensuring accurate data capture. Invoices and billing statements: IDP can extract invoice details, such as invoice numbers, amounts, due dates, line-item descriptions, and vendor information, facilitating faster invoice processing and payment management. Challenges faced in different types of document processing in banking and IDP solutions Document processing in the banking industry comes with specific challenges and here are some of the common challenges and IDP solutions: Document variability: Different document layouts, formats, and languages pose challenges in extraction and processing. IDP leverages AI and ML algorithms to adapt to varying document structures and extract information accurately. Handwritten documents: Handwritten documents, such as customer forms and signatures, require manual effort for interpretation. However, IDP solutions equipped with advanced handwriting recognition capabilities can process such documents and extract relevant information. Document classification: Banks deal with a wide range of documents that need to be classified correctly for further processing. IDP employs AI/ML algorithms to automatically categorize documents, reducing the need for manual sorting. Security and privacy: Banks handle sensitive customer information and ensuring data security and privacy is crucial. IDP solutions include robust security measures such as fraud detection, access controls, and audit trails to protect sensitive data throughout the document processing lifecycle. By automating the processing of various document types, IDP streamlines operations and enables banks to provide faster and more accurate services to their customers. With the help of IDP, banks can stay ahead in a competitive landscape and deliver superior customer experiences while reducing costs and improving operational effectiveness. To know more about IDP solutions for the banking sector, contact our experts.

Logistics and supply chain management have become one of the most essential processes and a strategic necessity in the world of business. Businesses today need to survive in hyper-connected, globally competitive environments, which can thrive only through effective and efficient supply chain management. Logistics document processing is a crucial aspect of logistics management to handle the intricate network of moving goods and information. It involves acquiring, organizing, and managing various documents like purchase orders, invoices, delivery notes, packing slips, regulatory and customs, etc. The companies have started recognizing the need for custom AI models in logistics document processing and the benefits of implementing them. But it’s s a challenging task requiring a large infrastructure setup, cost, and expertise. Intelligent document processing solutions are a boon to industries like Logistics that processes diverse documents and formats in large volumes as part of their operations. IDP for Logistics and Supply Chain The rise of AI has spurred the creation of intelligent document processing solutions that cater to various complex use cases in Logistics. The adoption of IDP solutions enables logistics companies to stay ahead of the competition. Compared to the former methods of document processing, enabling document automation has many advantages such as: The following are the steps in data extraction process By implementing Intelligent Document Processing in Logistics and Supply Chain the software gets more intelligent the more it is used. With the help of machine learning, the system learns from the information gathered over time and improves its effectiveness.

Insurance companies face the challenge of handling vast amounts of unstructured data contained in documents such as policies, claims, and customer information. Manual processing of these documents is time-consuming, error-prone, and inefficient. However, with the advent of automated document processing software, insurance companies can optimize their operations, improve efficiency, and enhance customer experience. Let’s explore how insurance companies benefit from using automated document processing software. Automated document processing in the Insurance industry Insurance companies face the challenge of handling vast amounts of unstructured data contained in documents such as policies, claims, and customer information. Manual processing of these documents is time-consuming, error-prone, and inefficient. However, with the advent of automated document processing software, insurance companies can optimize their operations, improve efficiency, and enhance customer experience. Let’s explore how insurance companies benefit from using automated document processing software. Insurance Document ClassificationUsing AI, insurance companies can automatically classify attached documents based on their intent. This reduces manual tasks in claims processing, improves efficiency, and ensures policyholders receive services faster. Intelligent Data CaptureIDPs are automated data extraction software that can accurately extract data from various sources, including handwritten notes, email requests, and receipts. This enables claims adjusters to convert unstructured data into usable business data, which can be automatically entered into internal databases, improving data accuracy and efficiency. Document ComparisonAutomated document processing platforms can compare data points from multiple sources with above-human accuracy. This reduces human error in processes such as checking policies against external sources for data validation, resulting in improved data quality. IDPs are not just automated data extraction tools for insurance industry but have various other uses in improving process efficiency, overall productivity, and even decision making. Here are some ideas for using insurance document automation, 10 Insurance Document automation ideas for optimizing your business Customer OnboardingAutomating the customer onboarding process can simplify and expedite the enrollment of new policyholders. IDP software can extract and validate customer data from application forms, identification documents, and other relevant paperwork, ensuring accurate and efficient customer onboarding. Policy UnderwritingAutomating underwriting processes reduces errors and manual re-work involved in resolving them. It helps insurers evaluate risks faster, speed up the underwriting process, and make higher-quality decisions on policy applications. Claims Processing AutomationAutomating claims processing streamlines the entire claims handling process, from initial reporting to the final settlement. It improves policy-holder management, reduces costs, improves turnaround times, and provides faster and more accurate customer service. Customer Service:Automated email responses and routing enable insurers to provide 24/7 customer service and support. Natural language processing and machine learning technologies can answer common customer questions, resolve simple issues, and escalate complex inquiries to the appropriate department or agent when needed. Fraud DetectionAutomating fraud detection helps insurers identify and prevent false claims, identity theft, and premium evasion. Using preset rules and signature validation, insurers can proactively detect fraud and mitigate risks. Integration with existing fraud models allows for flagging claims that require human intervention for quick batch review before payout. Data AnalyticsAnalyzing customer data enables insurers to control data quality for compliance and gain deeper insights into customer behavior, market trends, and risk factors. By integrating automated document processing software with analytics, insurers can get insights from structured and unstructured data and make more informed business decisions, deliver better products and services, and identify cross-selling and upselling opportunities for existing policyholders. Optimizing Sales OutreachIntelligent document processing platforms can assess policyholder information, compare it to similar data, and alert sales representatives or brokers of cross-selling opportunities. This helps maximize sales potential by leveraging existing customer data. Processing Proof of Prior Insurance DocumentsAutomation simplifies the common step of checking for proof of prior insurance during customer onboarding. By automatically extracting relevant information from policy declaration documents, ID cards, carrier reports, and other documents, insurers can expedite the process. Automatic Policy Cancellation or RenewalBy leveraging AI to detect intent, automated document processing platforms can determine if an email request is for policy renewal or cancellation. Extracting relevant information from the email or attachment, such as policy number, customer details, and dates, enables the automatic processing of policy changes. Regulatory Change ManagementInsurance companies need to stay updated with regulatory changes and adjust their policies and processes accordingly. Intelligent document processing can aid in monitoring and analyzing regulatory updates, extracting relevant information, and automating the assessment of their impact on existing policies and procedures. IDP use cases for insurance industry Benefits of document automation in the Insurance Industry Cost SavingsBy automating document processing tasks, insurance companies can significantly reduce operational costs. Manual data entry, document handling, and verification processes are time-consuming and prone to errors. With IDP software, these processes can be streamlined, leading to cost savings and improved efficiency. Faster Claims ProcessingOne of the significant benefits of IDP software in the insurance industry is the accelerated claims processing. By automating data extraction and document analysis, insurers can expedite the evaluation and settlement of claims. This not only improves customer satisfaction but also reduces the time and resources required to handle each claim. Enhanced ComplianceInsurance companies must comply with various regulatory requirements and ensure data privacy and security. IDP software can help automate compliance-related tasks, such as verifying policyholder information, cross-referencing data against external sources, and ensuring accurate record-keeping. This reduces the risk of compliance breaches and associated penalties. Improved Customer ExperienceAutomation in insurance document processing leads to faster response times, reduced paperwork, and improved accuracy. This, in turn, enhances the overall customer experience. Insurers can provide prompt and accurate information to policyholders, process claims efficiently, and deliver personalized services, leading to increased customer satisfaction and loyalty. Data Insights and AnalyticsIDP software can extract valuable insights from structured and unstructured data. By analyzing customer information, claims data, and policy documents, insurers can gain deeper insights into customer behavior, identify trends, and make data-driven decisions. These insights can drive product innovation, marketing strategies, and risk assessment. Scalability and AdaptabilityIntelligent document processing software is highly scalable and adaptable to evolving business needs. As insurance companies grow and handle larger volumes of documents, IDP software can easily

Today’s world of business demand digitization and faster ways to achieve it. Collecting, arranging, and maintaining paper documents, files, and records isn’t easy. Taking too much time and space, keeping physical documents requires great attention and responsibility. There is a dire need for digital documentation that has many takers like: Healthcare and PharmaceuticalsDigitizing patient prescriptions and medical PDFs is a crucial challenge in the healthcare and pharmaceutical industry. Handwritten text detection also impacts patient enrolment and form digitization, improving the user experience for hospitals and pharmaceutical companies. InsuranceThe insurance industry faces delays in processing claims due to various handwriting styles in claims documents. Automating claim processing with handwriting recognition can accelerate the pipeline and prevent costly delays. BankingIn many developing countries, banks process many cheques manually, verifying entries like signatures and dates. Implementing handwriting text recognition can save costs and hours of human work, making cheque processing more efficient. Online LibrariesTo make historical knowledge accessible, image scans of documents are digitized. Handwriting recognition plays a vital role in identifying text in images, enabling indexing, querying, and browsing of medieval and 20th-century documents for modern-day use. Though technology is galloping forward, there are companies still buried under paperwork. Upgrading to digital documentation allows companies to access data instantly from anywhere and anytime. Saving time and money, allowing better collaboration with employees and partners, and ensuring the confidentiality of sensitive information. Challenges of document digitization: Docketry is an intelligent document processing solution built to help enterprises working in document-heavy industries to improve operational efficiency, reduce processing time and errors, and enhance data accuracy and security. This helps in cost savings, increased productivity, and better compliance with regulatory requirements. Docketry also has advanced features like document summarization, and sentiment analysis helping businesses to gain valuable insights into customer behavior and preferences, enabling enterprises to make data-driven decisions and gain a competitive advantage. Want to know how Docketry can help your business? Talk to our experts now. Challenges in handwriting recognition and conversion: Technologies used in digital conversion of data: Optical Character Recognition (OCR) software is the one usually used to convert images and documents with data into editable text. The conversion allows easy organizing and sharing of your ideas, information, and message you want to communicate. There is many OCR software in business that have been used from time-to-time. But with OCRs, there are some performance limitations when it comes to extracting complex, unorganized, unstructured data. The OCRs are either unable to recognize and extract data with precision or are prone to making errors. This can be overcome by using Intelligent Character Recognition (ICR) and advanced intelligent document processing (IDP) software like Docketry. The advancements in Deep Learning have fast-tracked the progress in cracking handwritten text recognition. Recognizing handwritten text is done with algorithms that are much more intelligent than generic OCR. While Docketry uses OCR backed by powerful AI and Machine Learning technologies that help digitize even the complex documents that may contain cursive handwritten texts. With the ever-increasing use cases across sectors such as banking, insurance, logistics, etc., Docketry is evolving with time catering to a spectrum of demands with innovative and efficient solutions for digitizing complex, unstructured, unorganized, and handwritten documents, and data.

Logistics and supply chain operations encompass various aspects such as warehousing, shipping, transportation, manufacturing, and product distribution. In today’s business landscape, logistics companies often grapple with the challenge of managing vast volumes of documents and data, which are traditionally processed manually by supply chain managers. However, the advent of technology has brought about significant advancements in logistics automation software, revolutionizing the industry. By leveraging a document processing tool such as Docketry, logistics businesses streamline their otherwise complex and time-consuming document processing. Other than reducing time dedicated to document processing, organizations also increased accuracy among different types of documents from printed documents to hand-written documents, and better tracking of documents as well, which is usually seen as a part and parcel of the logistics businesses. Docketry is an advanced IDP solution for the logistics industry that can process complex unstructured documents with high accuracy. Docketry’s confidence scores and analytics help in additional validation ensuring that errors do not creep into the data. With pre-built APIs and SDKs, logistics companies can easily seamlessly integrate Docketry into existing processes, customize it to their needs and automate document workflows. In addition to fast and accurate data extraction, Docketry’s automated document processing for logistics offers document search for easy document archiving and retrieval, sentiment analysis for providing better customer service, and signature validation for fraud detection. How Docketry reads “better” than its competition Efficient volume handlingIDP excels at rapidly and accurately handling high volumes of documents. It can seamlessly extract data from PDF or handwritten documents, regardless of their format or structure. This capability enhances operational efficiency and reduces bottlenecks. Error reductionBy automating document processing in logistics, IDP significantly reduces the occurrence of human errors. For instance, automated invoice processing ensures precise data extraction, leading to improved decision-making and reduced operational risks, particularly for small businesses. Accelerated processesLeveraging AI-based document search and retrieval, IDP drastically reduces document processing time. This enables quicker turnaround times and enhances customer satisfaction, as critical operations can be expedited. Enhanced visibility and traceabilityIDP’s AI-powered document management and digital workflow automation provide a comprehensive digital trail for every document. This advanced feature facilitates improved visibility into processes, allowing businesses to identify areas for enhancement and make informed decisions. How to choose the best document automation software Evaluate your automation needsOrganizations usually have specific requirements of which documents they wish to be automated and some documents which will still be manually processed. So, organizations usually list specific features and functionalities, for the best outcomes. UI/UXThe tool needs to be intuitive and easy to use, a low learning curve means that the solution is adopted at a higher rate. Customized Integration optionsDocketry can be integrated in your existing systems to deliver a streamlined workflow, the idea being here to minimized human intervention throughout your workflow. Security and access controlWhen talking about document automation, security, and access control is paramount, depending upon regions and clients, the importance of security goes up. How to choose the best document automation software Docketry for logistics and supply chain document automation Logistics and supply chain are one of the sectors where adoption of technologies is slowest, a document automation tool can really help these logistics and supply chain organizations to reach new levels of productivity, data accuracy and streamline their entire workflow. To know how Docketry can address your specific use case, talk to our team today.

Invoice management is often overlooked by CFOs as it is considered part of the overhead costs of running a business. This leads to the assumption that the process will take care of itself. However, this assumption is far from accurate. In reality, many companies lack a comprehensive understanding of their Accounts Payable function and the total cost associated with paying invoices. To address this issue, it is essential to address the inherent flaws in the current process. Implementing invoice management automation technology can provide a straightforward and sustainable solution. By streamlining the process, automation frees up the Accounts Payable staff to focus on more strategic tasks that contribute to overall financial performance improvement. Issues such as time-consuming manual processes, entering inaccurate or incomplete data, complex approval workflows, data safety & security, lack of standardization and lack of clarity of Cash flow. The bigger impact of inaccurate data is that it affects decision-making and inaccurate reporting only complicates future business decisions. A data extraction tool like Docketry can help businesses across businesses and industries to streamline their accounting processes. Common Challenges in Invoice Data Extraction Invoice data extraction poses several common challenges that organizations face. Firstly, unstructured data formats, such as scanned PDFs or paper invoices, make it difficult to extract relevant information accurately. Secondly, variations in invoice layouts, languages, and terminology add complexity to the extraction process. Additionally, poor data quality, including errors and inconsistencies, can hinder extraction accuracy. Furthermore, handling a large volume of invoices within tight timelines can be overwhelming. Lastly, integrating data extraction with existing systems and workflows seamlessly presents technical and operational challenges. Overcoming these challenges requires advanced technologies, such as optical character recognition (OCR), machine learning, and process automation, to ensure efficient and accurate invoice data extraction. Why is intelligent document processing better than other processing methods? Manual data entry errors and their consequencesManual data entry errors can have significant consequences on businesses. Inaccurate data can lead to faulty financial analysis, incorrect inventory management, and unreliable decision-making. Moreover, it can result in billing and payment errors, customer dissatisfaction, and damage to the company’s reputation. Efficient data validation processes are crucial to mitigate these risks. A data extraction tool like Docketry can help businesses across businesses and industries to streamline their accounting processes. Handling diverse invoice formats and layoutsData can come in different forms, such as semi-structured or unstructured in different invoice formats and layouts. To handle data extraction efficiently, it is essential for data extraction software to support common unstructured document formats like DOCX, PDF, or TXT. By effectively processing these formats, businesses can fully utilize the data they receive and derive valuable insights from it. Dealing with handwritten and scanned invoicesBusinesses often face the challenge of dealing with handwritten and scanned invoices, which can be time-consuming and prone to errors. Implementing advanced optical character recognition (OCR) technology can automate the extraction of data from these invoices, streamlining the process and improving accuracy for efficient invoice management. A data extraction tool like Docketry can help businesses across businesses and industries to streamline their accounting processes. Extracting data from unstructured text fieldsDealing with unstructured data poses numerous challenges for businesses. Firstly, the data often comes in lengthy and disorganized formats, requiring the need for unstructured data extraction. This can be time-consuming for staff members. Secondly, unstructured data, especially when stored in large files, consumes significant storage space. In contrast, structured data is presented in precise and tabular formats, making data extraction a much simpler process. Choosing the Right Data Extraction Solution Selecting the right data extraction solution is a critical decision for businesses seeking to optimize their data management processes. With the vast amount of data available, it is essential to choose a solution that can efficiently extract, transform, and load data from various sources. Factors to consider include the solution’s compatibility with existing systems, scalability, ease of use, and security features. Additionally, the solution should offer advanced data cleansing and validation capabilities to ensure accurate and reliable data extraction. By carefully evaluating these factors and choosing a robust and flexible data extraction solution, businesses can streamline their operations, improve data quality, and make informed decisions based on reliable insights. A data extraction tool like Docketry can help businesses across businesses and industries to streamline their accounting processes. Below are some of the important considerations when choosing an ADP for your business. Document SearchDocketry’s automated document solution utilizes advanced natural language processing (NLP) and intelligent optical character recognition (OCR) technology to ensure precise and dependable results. With the power of contextual search, users can efficiently search for information within extensive document repositories by considering the surrounding text, metadata, and other pertinent details. This approach enhances the accuracy and relevance of search results, enabling users to find the information they need more effectively. Document SummarizationDocument summarization allows you to obtain a concise overview of the essential information contained within each document, eliminating the need for extensive reading. By utilizing this technique, you can efficiently extract the most crucial details from documents without having to go through the entire text. An invoice data extraction tool like Docketry can efficiently summarize documents across formats and layouts. Sentiment analysisSentiment analysis enables you to gain insight into the emotions and attitudes expressed in texts, allowing you to better understand your customers. By analyzing the sentiment behind their words, you can delve deeper into their thoughts and feelings, helping you build stronger connections and tailor your approach accordingly. Fraud DetectionLeverage advanced analytics and predefined validation rules to efficiently process and authenticate signatures. With the power of advanced analytics and a set of predetermined validation rules, you can streamline the signature processing and validation process. This enables you to accurately verify the authenticity and legitimacy of signatures, ensuring a smooth and secure workflow. Key Techniques for Overcoming Challenges Efficient data extraction is of utmost importance for accountants who handle a high volume of financial documents. Tediously reviewing each document, identifying pertinent information, and manually inputting it into management systems can be time-consuming

In the intricate sphere of insurance, one crucial aspect often falls under the spotlight – claims processing. It’s the moment of truth for insurance companies, where policyholders eagerly await compensation for their losses, and they are ought to deliver on their commitment. Thus, timely and efficient claims processing drives customer satisfaction, loyalty, and cost reduction. According to Deloitte, claims processing typically accounts for around 70% of insurers’ costs. Claims processing, as you know, has historically been a complex and labor-intensive manual process. It’s a process prone to errors and fraud, ultimately affecting the efficiency of claims processing and incurring high costs for insurance companies. What is claims process automation? Claims process automation is the use of technology designed to automate the handling and processing of healthcare and insurance claims. By leveraging cutting-edge technologies like machine learning, artificial intelligence, and natural language processing, claims automation streamlines operations, reduces manual work, and significantly improves efficiency in claims processing. The importance of efficient claims processing Claims processing can be a slow and tedious affair. Inefficient manual processes often take days or even weeks for complex claims to be resolved. The consequences are significant: frustrated policyholders and high costs for insurance companies. Not to mention, the longer a claim remains open, the more its processing costs rise, ultimately affecting both loss and combined ratios. The volume of unstructured data involved in claims processing further compounds the issue, making manual processing prone to errors and fraud. Insurance fraud alone costs the industry billions of dollars each year. Choosing the right data extraction solution Selecting the right data extraction solution is a critical decision for businesses seeking to optimize their data management processes. With the vast amount of data available, it is essential to choose a solution that can efficiently extract, transform, and load data from various sources. Factors to consider include the solution’s compatibility with existing systems, scalability, ease of use, and security features. Additionally, the solution should offer advanced data cleansing and validation capabilities to ensure accurate and reliable data extraction. By carefully evaluating these factors and choosing a robust and flexible data extraction solution, businesses can streamline their operations, improve data quality, and make informed decisions based on reliable insights. A data extraction tool like Docketry can help businesses across businesses and industries to streamline their accounting processes. How claims automation with Docketry cuts costs Claims automation becomes the beacon of hope for the insurance industry. By implementing advanced AI technologies like Docketry insurance companies can significantly improve efficiency, reduce costs, and enhance the customer experience. Here are the ways in which claims automation helps in achieving these objectives: Faster time to resolutionPrompt resolution of insurance claims is pivotal to customer satisfaction and cost management. Long-drawn-out claim processing leads to frustrated policyholders, potential loss of clients, and increased costs for insurance companies. Through automation the initial receipt and processing of claims documents become faster. The quicker you gather all the necessary data for a claim, the faster you can resolve it, reducing costs in the process. Reduction of manual activityAutomating manual processes within claims submissions results in significant cost reductions and improved loss ratios. With AI handling tasks such as transcribing call centre phone calls and intelligent intake assessing damages based on photos, the need for extensive manual effort decreases, speeding up the adjudication process. More complete claims submissionsAutomation allows insurance companies to gather more data from claims documents. This extensive data collection facilitates more nuanced adjudication of claims, especially in commercial insurance. A fair and accurate assessment of claim exposure reduces costs related to loss ratios and impacts both loss and combined ratios positively. Detection of insurance fraudAI technologies help in identifying fraudulent claims efficiently. Technologies like intelligent intake can process and understand data and compare it to other datasets, enabling the detection of fraud. Recognizing potential indicators of fraud that might be challenging for humans to identify reduces loss ratios by flagging claims that should be denied. Improved customer experienceBy streamlining processes and reducing the need to ask customers to submit the same documents multiple times, claims automation not only cuts costs but also enhances the customer experience. Faster claims processing leads to happier policyholders and higher NPS, ultimately boosting the reputation of insurance companies. The future of claims processing in the insurance industry Claims automation is the future of the insurance industry, offering a myriad of benefits, with cost reduction being a significant driver. Automation not only streamlines the claims process but also enhances efficiency, reduces errors, and mitigates fraud. As insurance companies shift their focus to achieving higher customer satisfaction and loyalty, embracing claims automation with the right technologies becomes an imperative step in the right direction. The advanced AI solution like Docketry not only cuts costs but also paves the way for a brighter future for the entire industry.

What is Robotic Process Automation (RPA)? Robotic Process Automation (RPA) is a human emulating technology revolutionizing business by automating repetitive tasks through software robots. These robots perform activities like data entry and form filling without human intervention, enhancing operational efficiency and freeing up employee time What is Robotic Process Automation (RPA)? Robotic Process Automation (RPA) is a human emulating technology revolutionizing business by automating repetitive tasks through software robots. These robots perform activities like data entry and form filling without human intervention, enhancing operational efficiency and freeing up employee time for more valuable tasks. RPA is affordable, compatible with existing processes, and widely adopted across industries. Programmed with pre-determined rules, RPA bots mimic human actions within specific systems or applications, ensuring maximum productivity 24/7 with minimal supervision. Highly scalable and seamlessly integrated, RPA revolutionizes manual processes, improving accuracy, speed, and cost-effectiveness. Embracing RPA is crucial for businesses aiming to thrive in today’s fiercely competitive market, streamlining operations, optimizing workflows, and driving success. What is Robotic Process Automation (RPA)? Business use case development The Docketry platform offers a wide range of complex business cases, including source-to-downstream processing and sentiment analysis in customer service. These tasks involve handling data at different stages and analyzing customer sentiments for insights. The platform is highly customizable, catering to diverse client demands such as multiple language support and internal compliance guidelines. For example, some clients require global language support, while others need adherence to internal compliance regulations. Docketry is designed to address various client requirements and industry-specific challenges. Automating data extraction from business documents, like invoices from vendors, is a critical step in streamlining processes and saving time. By leveraging advanced technology, organizations can automatically extract and process crucial information from invoices, ensuring accurate matching of invoices and payables. However, challenges arise when invoices have similar structures but come from different vendors or when dealing with new or discontinued vendors. Efficient automation of data extraction from invoices is vital to reduce manual effort, improve accuracy, and streamline payable processes. Implementing intelligent systems helps organizations overcome these challenges and handle vendor invoices efficiently. Choosing the right data extraction solution Step 1 – Identify the Process for AutomationTo embark on the path of implementing Robotic Process Automation (RPA), it is essential to first identify the processes that are well-suited for automation. When it comes to invoice processing, several tasks lend themselves to automation, such as extracting data from invoices, performing data entry, matching invoices, and resolving discrepancies. Identifying and automating such tasks in the invoice processing journey not only enhances operational efficiency but also minimizes errors and delays. By implementing RPA, organizations can streamline their invoice processing workflows, freeing up valuable time for employees to focus on more strategic and value-added activities. Step 2 – Develop and test the RPA BotSpecific RPA bots are developed according to the specific use cases. During the programming phase, the bot’s instructions are carefully crafted to align with the desired outcomes of the identified tasks. This involves defining the specific steps and logic that the bot should follow to effectively complete each task. Through meticulous coding and configuration, the bot is equipped with the necessary capabilities to perform the automated tasks seamlessly. The testing phase is crucial as it ensures that the bot is capable of consistently delivering accurate results and performing tasks efficiently. By thoroughly validating the bot’s capabilities, organizations can have confidence in its ability to automate the identified tasks and contribute to increased productivity and effectiveness within the processes Step 3 – Deploy the RPA BotOnce the RPA bot has undergone thorough testing and refinement, it enters the deployment phase. During this stage, the bot is prepared for implementation, which involves several key activities. Firstly, end-user training is conducted to ensure that individuals who will be interacting with the bot are familiar with its functionality and capabilities. This training aims to empower end-users with the knowledge and skills required to effectively collaborate with the bot in performing their tasks. The deployment phase encompasses not only the technical aspects of integrating the bot, but also the human element of training end-users and establishing monitoring mechanisms. Through careful planning and execution, organizations can successfully deploy the RPA bot and leverage its capabilities to streamline operations and enhance overall productivity. Step 4 – Monitor and OptimizeContinuously monitoring and optimizing the performance of the RPA bot is essential to maintain its efficiency and effectiveness over time. This ongoing process involves closely observing the bot’s operations and making necessary adjustments to enhance its functionality. One aspect of monitoring involves evaluating the bot’s algorithms. By analyzing the bot’s performance data and metrics, potential areas for improvement can be identified. This analysis may reveal opportunities to refine existing algorithms or develop new ones to optimize the bot’s decision-making and task execution. Conclusion (repetitive tasks with required periodic manual interventions) In a time where efficiency and accuracy are of utmost importance, RPA emerges as a formidable tool, especially in domains such as repetitive tasks with required periodic manual interventions. By incorporating RPA into their operations, organizations can automate labor-intensive tasks, reduce expenses, enhance precision, and increase overall productivity. It is important to remember that achieving successful RPA implementation necessitates meticulous planning, execution, and continuous optimization. By adhering to the steps laid out in this guide, your organization can unlock unprecedented levels of operational excellence. FAQs (Frequently Asked Questions)