November 2022

How is cognitive automation transforming the insurance industry?

Technology is transforming the insurance industry in ways never imagined. It can now deliver faster, more accurate customer service and improve business decisions while reducing costs by eliminating manual processes.

Cognitive automation is one such technology that has the potential to revolutionize how insurers interact with their customers completely. The result will be a more responsive and customer-centric industry that can better compete in the digital age.

But what exactly is automation in the context of the insurance industry?

An overview of cognitive automation in the insurance sector

With automation making a significant impact in the insurance sector in the last few years, cognitive automation technologies have played a key role in helping insurance firms enhance customer service, streamline operations, reduce costs, and improve their overall efficiency.

For instance, cognitive automation in insurance can process claims quickly and efficiently and identify fraud or errors, which helps insurers ensure accuracy while reducing potential errors. Automation also makes it simpler for customers to submit their claims and access information about their policies.

Cognitive automation can also help insurers improve customer service by providing faster response times, better access to information, and more personalized services such as recommendations or discounts. It can even reduce paperwork, allowing customers to sign up for a policy or make payments quickly and easily.

In addition, automation is making it easier to manage risk by providing better data analysis and predictive analytics tools. This allows insurers to better assess potential risks before underwriting policies and track customer behaviors that may indicate a higher risk later.

What is cognitive automation?

Cognitive automation is a technological solution that combines Artificial Intelligence and Machine Learning to automate processes, improve decision-making and optimize business operations. This uses natural language processing (NLP), computer vision, and data analytics to recognize patterns in large datasets, analyze them at scale and make decisions based on the data gathered.

Cognitive automation helps to automate complex business tasks and processes, providing organizations with more accurate and efficient decision-making. By leveraging it, businesses can reduce costs, eliminate manual labor, improve employee efficiency, and increase competitive advantage in the market.

How does automation reconfigure the insurance operation model?

The insurance industry is undergoing a dramatic transformation as automation and digitalization rapidly change how people buy, manage, and use insurance policies. Automation technologies such as AI, Machine Learning, RPA, and Natural Language Processing can significantly enhance underwriting, pricing, claims processing, and policy servicing activities.

This shift towards automation dramatically reconfigures the traditional insurance operation model to include agile processes, automated decision-making, and customer-oriented engagement. In addition, leveraging cognitive automation can streamline customer service interactions and provide customers with a more personalized experience.

By automating basic customer service functions, insurers can reduce costs and improve customer satisfaction. For instance, a leading US insurance company leveraged AssistEdge, a cohesive automation platform by EdgeVerve, for ticket & claim management and diminished the error rate to under 3% and saved up to $6 million annually. Thus, cognitive automation in insurance is helping companies become more efficient, reduce costs, and better manage their operations, ultimately providing a more valuable customer experience.

How does automation help insurers focus on customer centricity?

Automation can help insurers focus on customer centricity by streamlining processes, increasing efficiency, and reducing the time to market. Automation tools, such as Robotic Process Automation (RPA), Artificial Intelligence (AI), and Machine Learning (ML), can automate mundane tasks and eliminate the manual processing of data.

By understanding customer needs, insurers can tailor their products and services to meet individual needs and preferences, thus creating a more personalized service. For instance, with AssistEdge, insurance companies achieved 95% accuracy for claims processing by transforming the entire customer experience through highly efficient & automated systems.

Automation tools also allow insurers to provide better analytical insights into customer data, enabling them to make more informed decisions about the best way to serve customers. With these insights, insurers can better understand customers and develop strategies to improve customer experience.

Current trends and emerging areas of automation in the insurance sector

The insurance sector is increasingly leveraging automation technologies to drive digital transformation and significantly enhance customer experience. Here are some current trends and emerging areas of automation in the insurance sector:

Benefits of cognitive automation

Increased efficiency and productivity: Cognitive automation can enable organizations to process large amounts of data and quickly make better-informed decisions. It enables employees to concentrate on more value-adding activities rather than spending time on mundane manual tasks.

Enhanced customer experience: Organizations can provide more personalized and relevant customer experiences by automating customer service processes. Customers can get the information they need quickly and accurately, leading to better customer satisfaction.

Cost savings: Automation eliminates labor-intensive manual processes, reducing costs associated with hiring personnel to do such tasks. It also reduces errors resulting from human mistakes, thus increasing accuracy and saving time.

Improved accuracy: Automation helps to reduce errors by providing more accurate data analysis, often faster than humans can do manually. This leads to better-informed decisions and improved organizational customer outcomes.

Role of cognitive automation in the insurance industry

Claims processing: It helps manage the claims processing process by automating data entry and analysis functions for faster claim settlement. It can also help insurers detect fraudulent activities, preventing further losses.

Policy underwriting: Cognitive automation assists insurers in assessing customer risk profiles quickly and accurately by analyzing available data. As a result, insurers can make better decisions when designing policies and setting more appropriate premium rates.

Customer service: Cognitive automation helps improve customer service by providing faster resolution of customer queries while reducing the need for manual intervention and increasing customer satisfaction.

Fraud detection & prevention: It helps insurers identify suspicious activities and fraudulent claims by analyzing real-time data, thus assisting insurers in reducing losses associated with the fraud.

Risk management: Cognitive automation can be used to assess potential risks more accurately, helping insurers make better decisions when it comes to risk management. By using cognitive automation, insurers can better understand their customer base and market trends, allowing them to develop better strategies for mitigating risk.

Use cases of cognitive automation in the insurance sector

In the insurance sector, organizations use cognitive automation to improve customer experiences and reduce operational costs. For example, it can be used for automated claims processing and fraud detection.

All of these use cases demonstrate the potential for cognitive automation to revolutionize the insurance sector in terms of customer experience and operational efficiency.

Applying cognitive automation in the insurance sector can help reduce errors, speed up processes, and improve customer satisfaction. To stay ahead of the curve, insurers must embrace new technology and adopt a data-driven approach to their business. By doing so, they will be able to improve efficiencies, better assess risks, and provide more personalized products and services to their customers.

How can Artificial Intelligence reshape customer experience?

Conventional customer service does assist consumers and offers its own set of benefits; however, it can be enhanced even further by implementing Artificial Intelligence (AI). Along with benefits, challenges such as latency, inefficiency, and clerical error also come, which companies encounter when delivering services. However, companies can overcome these challenges by deploying Artificial Intelligence (AI) to boost customer service and take it to a new level. Hence AI customer experience is the way forward to ensure a sustainable yet innovative future for businesses.

Automation and AI can speed up processes and improve efficiency, while ensuring better accuracy, accountability, and integration with other apps. It can also scale up processes to make them more expansive, with greater handling capacity. This augments the balance between quantity and quality.

What is AI, and why is it important for boosting customer experience?

Artificial Intelligence is essentially where machines or robots can simulate human cognition and intelligence by applying algorithms in logical pathways. This involves using specialized hardware, identifying and recognizing patterns, as well as analyzing data to form (statistical probability-based) predictions – i.e., extrapolation.

With self-learning, corrective and autonomous reasoning capabilities, AI is really a potent tool to supercharge almost any process. The insights it brings, accompanied by reactive outputs, place AI in good stead to manage an ever-evolving digital world. Self-awareness and machine vision captures pertinent information for future analysis.

Whether it is healthcare or retail, every business can benefit from AI in some form or another. The customer journey will be set to pave the way for a brighter future with more intuitive workflows.

For illustration, with the healthcare industry requiring a lot of manual and tedious tasks, the industry has moved towards automation by applying IA to finish these repetitive tasks as quickly and easily as possible. Thus, automating these tasks improves customer satisfaction, particularly if the tasks are customer-facing. The tasks that fall under this process and which can be automated include processing insurance claims, verifying the doctor’s accessibility, arranging appointments, eligibility checks, and data migrations.

Be it streamlining processes, reducing mundane repetitive tasks, or even curbing fraudulent transactions; AI is a redeemer for business processes. A study by Mckinsey says that only 7% of customer concerns are actually shared with the experience team as it is a tedious task. However, the application of AI increases the likelihood of understanding customer concerns and preferences on a live basis and offering a customized plan for every individual. This rewarding experience becomes memorable for everyone, making customers feel valued.

How can AI transform customer experience?

With services becoming customer-centric, most companies are vying for better customer experience (CX), a channel for enhancing customer satisfaction, loyalty, and advocacy. However, a complete understanding of customers is the initial step toward an excellent customer experience.

Artificial Intelligence can transform and elevate the customer by easily automating the repetitive tasks that would conventionally need human intervention. For instance, a customer service agent can implement AI to answer common questions or booking appointments automatically, which would certainly free customer service from tedious tasks to offer more personalized service.

A case study by EdgeVerve suggests that a prominent UK-based telecommunication giant transformed its customer services by implementing AssistEdge Engage, which facilitated exceptional customer service, thus reducing AHT by 30% and saving approximately £25 million.

Most of the larger companies have already begun to integrate AI to augment their customer service. With this AI technology, these companies will surely be able to deliver faster service to their customers and better suggestions.

Benefits of AI in customer experience

Offers instant support and adapts to requirements quickly: The key reason for organizations beginning to apply in their processes is that it enables instant and on-demand support. AI has an innate ability to detect situations and then learn to adapt as per situational demand, which makes it an essential solution for elevating customer experience.

Saves costs and resources: The ability of AI to promptly adjust to different requirements leads to further efficiency and, subsequently, cost and resource savings (including time). Hence, companies can handle larger volumes of data easily, which leads to a significant reduction in customer handling time. As a result, organizations can now identify customer requirements more precisely and accurately.

Predicts latest trends: Implementation of AI can allow companies to predict trends more accurately and proactively as well. AI also enables automating alerts and notifications, which can be the mainstay for keeping everyone updated with the latest trends.

Enhances and endorses customer retention: AI boosts adaptability ability to constantly evolving trends for better flagging of service glitches that is useful in curbing customer diversion and promoting greater loyalty and retention. Such a proactive approach prioritizes customer insights to always ensure that they are put first.

Ensures better quality of products: By using artificial intelligence, companies can now simplify product testing, assure quality and usability. Convenience is another benefit, as the intent of customers can be better projected and acted upon accordingly with accurate data insights. Hence, customer experience can be enhanced significantly.

Also, a targeted and personalized service (as aforementioned) incentivizes psychological control over what customers want. Subsequently, engagement rates are set to rise with better clarity of what exactly customers want, how, from where, when, and why

Thus, with each passing day, AI has been transforming the rapport between businesses and customers, resulting in a growing trend for omnichannel experiences among customers. Thus, considering all the benefits AI can offer to create customer-centric experiences, most companies are shifting their attention toward the implementation of AI.

How does supply chain traceability contribute to business growth?

When consumers demand more insights about the origins of goods they purchase and the latter’s sustainability sources, transparency is no longer an option. Businesses also prefer visibility into their operations surrounding their products’ lifecycle. Hence, the demand for supply chain traceability has doubled in recent years. It immunes organizations of the various challenges that industries worldwide are currently confronting.

An overview of current supply chain traceability and other challenges

Even though a company’s supply chain was primarily the primary focus for digital strategies, a recent study showed that the digitization level for an average supply chain was around 43%, which was the lowest among all business areas examined. Reasons for misplaced priority could be technology gaps and management choices. Digital solutions did little to help the supply chain network, which was already grappling with challenges ranging from material and labor shortages to rising freight costs, port congestions to limitations in demand forecasting. However, the major roadblock would be the existing siloes and disjointed sections in the network that perform as separate entities. Hence, real-time information about what is happening in the upstream or downstream supply chain is not available when needed. Therefore, operations like supply chain traceability suffered greatly. However, in some instances, like the food industry, for example, commercial interests restrict data access.

Unarguably, the ability to trace goods in the product lifecycle can definitely improve the integrity of a supply chain. But achieving complete transparency can be a challenging endeavor. A complex and geographically dispersed network of suppliers and distributors makes it harder to embed tech-enabled supply chain traceability solutions into the existing infrastructure. Remote operations remain invisible and poor collaboration between partners adds to the problem. Regardless of the current bottlenecks, supply chain traceability can prove effective in building a resilient, reliable, responsive, and sustainable supply chain to battle ongoing geopolitical instability.

Evaluating the importance of supply chain traceability

Traceability is crucial for businesses as it enables owners to track their offerings from the source of origin to end users. Supply chain traceability allows enterprises to gain more insights into their manufacturing processes and accurately forecast lead times to manage inventories. Moreover, brands can leverage supply chain traceability software solutions to highlight their ethical sourcing practices, as demanded by consumers.

Further, traceability serves various purposes, such as mitigating risks of mislabeling, fostering more detailed audits, and enabling rapid traceability analysis during a product recall. Supply chain traceability also assists businesses in the identification of disruptions in the supply chain and staying responsive to sudden changes. And information about strategic opportunities is available on time to allow prompt reactions to demand fluctuations.

Supply chain traceability has garnered enough importance primarily for three reasons:

As mentioned above, traceability is the need of the hour, and organizations need to embrace the change to stay competitive in the already saturated market.

What is supply chain traceability?

Supply chain traceability refers to the ability of businesses to effectively track elements of a product or substance and trace their movement along the network from raw goods to finished products.

To keep up with the increasingly volatile business landscape, the global network of supply chains should be more resilient, agile, and flexible. And to stay competitive, enterprises should embrace a transparent and circular value chain to reduce or reuse materials to lower cost and manufacturing waste. This profound shift in the paradigm supported by traceability solutions allows companies to reset their business objectives and strive for operation optimization and excellence. Supply chain traceability provides such information, helping companies to make accurate predictions, optimize operations, respond to changes in demand faster, and fulfill orders more efficiently.

These above-mentioned objectives will eventually transcend into higher returns, better revenue growth, lower costs and increased market share, and overall good stakeholder returns.

Hence, simply put, organizations failing to invest in supply chain traceability solutions will remain vulnerable to market disruptions and allegations about product safety.

Steps to implement supply chain traceability

Since the purpose of supply chain traceability is to build a robust network of suppliers and distributors, the following steps would help enterprises implement various traceability measures effectively.

Identify risks: It is imperative for any organization to evaluate the various risks the company is immediately exposed to, such as compliance risks, historical disruptions, and poor supplier relationships.

Define objectives: After plotting potential operations issues, enterprises can define specific goals that would positively impact their overall performance.

Gather data: Tech-enabled supply chain traceability solutions are leveraged to map the network end-to-end. This improves visibility and fetches valuable insights into the supply chain, allowing enterprises to gauge cargo flow volume, suppliers and processes, potential gaps and risks, and opportunities for improvement and innovation.

Create levels of disclosure: Here, organizations should create various parameters for disclosures of information as stakeholders and customers require. Collecting relevant data and adding actionable insights should be circulated among stakeholders to build brand trust and loyalty.

How does supply chain traceability work?

Traceability empowers companies to easily track the upstream and downstream movement of products and goods along the supply value chain. Further, it also provides exact information about procurements, supplier sourcing, and conversion processes. Such information facilitates accurate predictions, running scenarios, and optimizing operations solutions. This, in turn, helps owners better serve their customers, identify unnecessary resource wastage, respond faster to fluctuations in demand, and deliver orders on time. Supply chain traceability uses various tech-enabled solutions to perform the aforementioned functions, thereby supporting enterprises in identifying strategic opportunities, innovating faster, minimizing the negative impact of internal and external disruptions, and certifying sustainable processes and products.

Key benefits of supply chain traceability

The primary benefits of supply chain traceability would be ensuring regulatory compliance and enhancing brand reputation. Other advantages include the following:

Conclusion

In order for businesses to achieve end-to-end visibility into their supply chain, they need to opt for traceability solutions that are effective and tech-enabled. Supply chain traceability provides the necessary information about the network on time so enterprises can take adequate measures to ensure their business continuity. It creates a collaborative network and facilitates the easy availability of required data. In addition, it communicates information pertaining to products and procurement sources to customers as per their demand.

Supply chain disruption – Definition, challenges, effects, and strategies

Organizations worldwide are susceptible to supply chain disruptions, from natural disasters and economic instability to the health pandemics like COVID-19. The factors contributing to these global risks are beyond a company’s reach; businesses are, however, not entirely powerless. By applying the correct Automation and AI tools, companies can better stock up the popular products ahead of demand, forecast slowdowns, and even produce needed parts in-house.

Hence, tech-enabled software solutions can be implemented to curtail supply chain disruptions for themselves as well as the customers the supply chain companies serve.

Current global supply chain challenges

Limited resources: The sudden spike in consumer demand following the pandemic lockdown raised concerns about insufficient input. Post that, supplies of essential materials continue to dwindle. Hence, merchants and suppliers need help meeting this demand.

Rising freight costs: Contrary to initial expectations, the need for container transportation has increased dramatically throughout the pandemic. Global lockdown measures have led to a spike in online purchases, increasing demand for imported raw materials and finished consumer items (a large percentage of which are moved in shipping containers).

And because this demand was so much higher than expected, there was a record-breaking shortage of empty or available containers and inadequate shipping capacity. This scarcity has resulted in a sharp price increase.

Challenges in demand forecasting: Legacy demand forecasting methods proved inefficient for supply chain management during the global pandemic for many businesses. For many retailers and providers of consumer goods/services, the global health crisis virtually destroyed the projections, leaving them without a reference for how much inventory to stock or make at any one time to meet the sudden spike in demand for essential items.

Therefore, difficulty has arisen from attempting to enhance client demand estimates while, in many cases, relying on intuition rather than data-driven study. Supply chain managers seek out new data sets for forecast models and continuously hone their findings for the highest level of accuracy.

Port overcrowding: Given that port owners, carriers, and shippers are still looking for a practical solution to this issue, port congestion brought on by the epidemic continues to be one of the most critical issues for the world’s supply chains. When a ship docks at a port but cannot load (or unload) its cargo because that station is already full, congestion occurs.

Although the loading and unloading procedure generally proceeds as planned, manpower shortages and societal alienation brought on by the pandemic have noticeably changed things (creating major bottlenecks at some busy global docks).

A shift in consumer mindset: The epidemic also has significantly altered consumer attitudes and habits, lowering the bar for delivery times, and raising the standards for a satisfying customer experience, for example. The difficulty lies in building a flexible supply chain that can use automation to maximize fulfillment and quickly meet increased demand. Inventory management software and multichannel order fulfillment services are great examples of this flexibility in the supply chain.

The digital revolution: Digital transformation and IoT can be a mixed blessing for supply chain operations. Many technologies, such as artificial intelligence, drones and robots, electric cars, and on-demand delivery, can improve how we approach the conventional supply chain.

The difficulty lies in deploying these systems/services across a company’s current supply chain activities, even while their long-term goal is to make e-commerce supply chain procedures more efficient and cost-effective.

Employing these technologies requires exertion and organizational reconfiguration, especially when dealing with various warehouses or omnichannel selling. However, supply networks must constantly change if they want to remain competitive.

Impact of supply chain disruptions

Disruptions like those brought on by the epidemic have a high global cost. These interruptions significantly impact the entire chain and have various consequences that will manifest themselves subsequently. The following implications of supply chain interruptions are well-known:

Key strategies to overcome supply chain disruptions

Safety stock: Even though increasing buffer capacity improves resilience, it leads to underutilized production facilities or inventory that exceeds the need for safety stock. Buffers are costly; hence, demand sensing is applied to reduce the risks of wastage or loss of items or shelves running dry during a demand surge. However, top corporations deploy buffers during new product introduction or development of new growth markets. Additionally, companies maintain buffer stock by carefully implementing contract manufacturers for their surge needs.

Multi-sourcing: Supply chain leaders should maintain a wider supplier network to facilitate a multi-sourcing strategy. Furthermore, they classify suppliers depending on their spending capacity and any disruptive event possibly affecting their revenue. Diversifying is accomplished by reaching out to more suppliers or working with an existing sole or single-source supplier who can produce from many places.

Bring the product closer: several businesses are striving to shorten the cycle times for finished goods and reduce regional dependence on their worldwide networks. However, regional or local supply chains are more expensive because they increase the ecosystem’s complexity and the number of participants. But local supply chains enable greater inventory control and bring the product closer to the customer.

Harmonization: Harmonization helps with process standardization to ensure consistency of all sites that are aligned to maintain quality. Many organizations are extending manufacturing to suppliers to maintain harmony in the supply chain. Furthermore, harmonization also foresees effortless movement of items throughout the network. This facilitates the proper placement of more enormous volumes among various providers. It simplifies sourcing policies, increasing resilience.

Alliances: The pandemic highlighted the need for a diversified sourcing network. Also, strong ties with contract manufacturers and international 3PLs are crucial for businesses. Hence, many enterprises work with outside manufacturers and logistic partners.

Bottom line

Although supply disruptions are unavoidable, companies can help themselves by being ready for even the most unlikely circumstances. The situation can be avoided by carefully preparing for quick and correct responses, assessing potential supply chain risks, diversifying suppliers, and finally using over-the-top platforms such as TradeEdge Network to transform the value chains.

Boosting data extraction accuracy with Document Intelligence Platform

To resolve the challenges of manual data extraction and processing of documents, a time-intensive and error-prone process, companies are leveraging technologies such as optical character recognition (OCR) technology in collaboration with Artificial Intelligence (AI).

Most of the data generated in companies are either semi-structured or unstructured. Thus, it becomes a barrier to automating the business processes dealing with such documents. Consequently, it leads to resource overuse and errors in manually processed documents. This is why a Document Intelligence Platform (DIP) becomes significant for the success of businesses.

What are Document Intelligence Platforms?

Document Intelligent Platforms are technological solutions that deploy Intelligent Document Processing (IDP) frameworks to solve mundane and repetitive daily tasks. These applied solutions harness the potential of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and computer vision to automate and augment the data-handling process.

With this, the brands can quickly and accurately access pertinent data, such as invoices. Details are identified, quality control is ensured, and any errors are flagged, then remedied. According to a projection in 2019 by Gartner, 50% of global B2B invoices will be processed and cleared automatically through 2025. Additionally, 80% of these ‘will be transmitted digitally’ by 2030.

What are the data extraction challenges?

Due to the exponential growth of high-volume data, also known as ‘Big Data,’ extracting, maintaining, and following the needed data for its productive usage poses challenges. The key stumbling block is the inability to acquire data from secure, trustworthy sources faster for online research. Due to this, consistency, speed, and reliability are at stake, and thus, overlooking them leads to redundancy. Thus, managing large volumes of document data extraction will also lead to unproductive processing as it becomes more challenging to extract manually.

Here are some of the challenges during data extracting process:

Data quality: Data quality is one of the most crucial attributes in analytics. Most companies extract data from various sources to get a more precise picture of what is happening within their businesses, but this comes at a cost. Therefore, the benefits of extracting data from multiple sources might not overshadow the risks that come with poor data quality.

Absence of standardization: The information is not always in the format companies require. Organizations thus leverage software solutions to extract and process data. However, this can be expensive and time-exhausting when you’re looking for information from various sources that are not per your needs.

Lack of accessibility: Obtaining accurate data can be a formidable and expensive process. There are many reasons a company might not be able to extract data from a source effortlessly. One reason could be that the resources do not have the needed data, or it is concealed behind a high paywall.

Inadequate data: The data extraction procedure is not always flawless, as data may be misplaced due to errors or omissions during the extraction process.

Why is document intelligence the need of the hour?

The importance of document intelligence has grown exponentially over the years as it eliminates human errors by using robust verification techniques to ensure fully accurate and seamless data workflows for lasting results. These contribute to a sustainable data universe for any and every brand to utilize, as well as depend on in the future for reference. Intelligent Document Processing & Document Intelligence Platforms both synergize and synchronize efficient data processing, hence boosting profitability and ROI.

Thus, this functionality secures the future of many businesses, which can then focus on reinvesting their profits in further growth and development, thereby saving resources. As per McKinsey, at least 65% of senior management have stepped up investments in both automation and artificial intelligence since the pandemic began.

Document intelligence ensures quicker and greater data availability, with more accessible and secure information with an accountable digital trail. This promotes higher integrity, especially within sensitive data circles, handling confidential and financial information. It also enables the firms to be prepared for unexpected future events, including expansion, transition, migration, or even merging data with other entities.

Benefits of document intelligence

Versatile and adaptable: Document intelligence platforms can be integrated with other systems, making them flexible applications for use virtually anywhere. These can be deployed in various domains, be it a banking or medical setting. This transferable versatility is not only unique but also sets up a future precedent of adaptability.

Improves accuracy and speed: Documents and data can be retrieved even quicker and more accurately. It is now easier to automate document categorization, which boosts productivity and saves time and money.

Enhances credibility and reliability: Drives accuracy by using digital signing techniques to eliminate any document or data tampering. This increases the credibility and reliability of data, forgoing the need to verify results, again saving time.

Reduces human effort: Automation using AI, ML, and NLP minimizes human clerical effort to propel data processing speeds to scale quantifiably without compromising on quality.

Boosts customer satisfaction and ensures compliance: Customer service satisfaction levels increase and can be maintained to meet the expected or desired levels using analytical data (extracted via Document Intelligence Platform). It also indexes data for better searchability and compliance.

Ensures data security: Enriched security through controlled data gateways and accuracy measures, ensuring that information is always securely stored. This way, only authorized personnel have access to the correct information.

Handles ‘Big Data’ easily: Better flexibility with personalized data workflows drives capabilities and the capacity to handle big data at scale. Various applications can benefit from this, ranging from custom queries to specific data collation. The agility is what really sets DIPs from any other associated solution out there.

How can document intelligence software help data extraction accuracy?

The automated processes of extracting, processing, and identifying pertinent data underpin how DIPs operate. These channels are faster and less susceptible to tampering, hence driving greater accuracy. Classification and tracking ensure that whatever data has been processed are examined, monitored, and thus made suitable for its primary purpose. This ‘fit for purpose’ data serves the industrial need aptly and with precision-based accuracy. Hence, companies can gain data insights from Document Intelligence Platforms which deploy Intelligent Document Processing and harness the power of AI, ML, and NLP.

Some common features that one would encounter across such platforms include:

Conclusion

Finally, the evolution of smart AI, ML, deep computer vision-based learning, and NLP is why document intelligence platforms have emerged to the rescue. Without these constituents to power IDP, data extraction accuracy levels would remain at variable lows. It is now up to stakeholders to see and realize the significant role such processing software plays in driving data-based solutions further afield in our global yet interconnected arena of services. Be it healthcare, finance, retail, or even legal industries, companies can benefit from faster, more accurate, and automated data processing.