Data-driven decision making – How IDP can help business leaders

In today’s dynamic business landscape, extracting data for decision-making has become increasingly important. Companies moving toward digitization and implementing data-driven decision-making practices are well-positioned to identify prospects, combat risks, and improve their decision-making ability.

Additionally, by automating processes and analyzing data, organizations can acquire critical insights into the customers’ ever-fluctuating preferences. Thus, data-driven decision-making involves using data, analytics, and other relevant information to make informed decisions, which becomes even more critical for companies pursuing to lead the competition game and attain sustainable success. This is where the role of Intelligent Document Processing (IDP) comes into play.

What is IDP, and how can it help in data-driven decision-making?

IDP is a technological solution that uses machine learning (ML) and artificial intelligence (AI) algorithms to obtain data from semi-structured and unstructured documents and convert it into organized, functional information. This process provides end-to-end automation to document-centric business processes, helping companies to reduce manual labor, increase efficiency, and improve accuracy. Consequently, it can considerably aid organizations in making data-driven decisions by extracting valuable insights from unstructured documents like invoices, contracts, and receipts. This is specifically beneficial for finance, healthcare, and legal industries dealing with sizable volumes of unstructured data.

Hence, by converting this unstructured data into structured data, companies can make informed decisions based on real-time data, leading to greater efficiency, accuracy, and cost savings.

Intelligent Document Processing also enables organizations to spot new prospects for growth and improvement. Moreover, with the help of data-driven insights, firms can detect patterns and trends that may have gone undetected. Therefore, using these insights to yield strategic decisions, businesses can achieve superior outcomes and a competitive edge in their respective market domains. Hence, IDP is a game-changer for businesses that strive to stay competitive and succeed in today’s data-driven decision-making process.

Insightful decisions with IDP – Key benefits to consider

Increased efficiency: IDP helps automate manual and repetitive document processing tasks, reducing the time and effort required for document management. This increases productivity and efficiency, allowing employees to focus on more value-added tasks.

Improved accuracy: IDP uses advanced algorithms to process and extract data from documents, resulting in fewer errors and increased accuracy. This is especially important in industries that require high precision and compliance, such as finance, healthcare, and legal.

Enhanced data analytics: IDP can extract data from multiple documents and combine them into a single dataset, allowing for better data analysis and insights.

Cost savings: IDP reduces the need for manual data entry and document processing, resulting in business cost savings. It also helps reduce the risk of errors and compliance issues, which can be costly in fines and legal fees.

Improved customer experience: IDP can streamline processes and reduce turnaround time, resulting in a better customer experience. This can facilitate organizations distinguish themselves from competitors and foster customer loyalty.

Benefits of data-driven decision making

Accuracy: Primarily, data-driven decision-making guarantees that decisions are built on relevant and accurate data, lowering the risk of errors.

Consistency: Leveraging data and analytics to inform decisions facilitates consistency across a company, which is vital for ensuring fair-mindedness and reducing subjective decision-making risk.

Improved outcomes: By using IDP to inform decisions, organizations can better distinguish opportunities for progress and make data-driven decisions to generate better outcomes.

Better resource allocation: Data-driven decision-making enables companies to find spheres where resources can be allotted more efficiently, thus improving competence, and cutting down waste.

Competitive advantage: Organizations that use IDP are better positioned to react to market changes to gain a competitive advantage.

Improved risk management: By employing IDP solutions to identify risks, businesses can promptly cope and mitigate their negative impact.

Increased transparency: Finally, using data to inform decision-making can bring clarity, thus permitting stakeholders to know the logic behind decisions and growing trust in decision-making.

Different stages of how IDP works

Thus, IDP solutions can provide valuable insights and predictions for decision-making by recognizing patterns and trends which would otherwise get overlooked. For instance, an IDP solution may analyze customer feedback to find common complaints or improvement areas. Accordingly, this information can be used to make informed decisions about products or customer service improvements.

Technologies commonly used in IDP solutions include Natural Language Processing (NLP), and Machine Learning (ML) Optical Character Recognition (OCR). These solutions enable IDP to extract information from unstructured data resources like emails, documents, and images.

Therefore, IDP is a compelling technology that can automate, feed essential insights and predictions for decision-making, and improve operational efficacy.

Common challenges of IDP implementation

While some challenges are correlated with implementation of an IDP solution, with careful planning and execution, these concerns can be easily tackled. By investing in privacy, data quality, integration, and employee buy-in, firms can profitably implement an IDP solution and exploit the full extraction potential of their data.

Best IDP implementation practices and strategies for data-driven decision making

Intelligent Document Processing transforms how businesses handle their data and automate specific processes. Hence, to maximize the gains of this solution, it is necessary to apply it efficiently and foster strategies for using the structured data produced by IDP for data-driven decision-making.

First and foremost, it is vital to identify the individual business processes that can benefit from IDP. This primarily involves analyzing existing workflows, spotting the pain points, and deciding where IDP can add the most value. Thus, organizations can attain maximum ROI from their IDP investments by focusing on the most crucial processes for the business and having full automation potential.

Secondly, organizations should create a clear-cut strategy for using the structured data generated by IDP for data-driven decision-making. This involves building a system for collecting, evaluating, and visualizing the data to derive essential insights. With the latest technologies, companies can gain a more profound sense of their operations, detect trends and patterns, and make informed decisions that navigate business growth and success.

Finally, enterprises should ensure that their IDP implementation aligns with their broader digital transformation objectives. This encompasses integrating IDP with other solutions, such as RPA, and creating an innovation and continuous improvement culture. Hence, by accepting digital transformation and leveraging the full promise of IDP, organizations can stay ahead of the curve in today’s data-driven business landscape. Validating this, a survey by Gartner indicates that 89% of Board of Directors (BoDs)reveal that digitization have been embedded in all of their business growth strategies with only 35% of BoDs achieving or are on track to attaining digital transformation goals which highlight the increasing importance of data-driven decision making in today’s business landscape.

Therefore, implementing IDP requires a strategic tactic recognizing good business processes, breeding a data-driven decision-making process, and aligning IDP applications with comprehensive digital renovation targets. Thus, by adopting these best practices, companies can unravel the full potential of IDP technology and acquire a competitive edge in today’s swiftly evolving business ecosphere.

Digitization with XtractEdge

XtractEdge, EdgeVerve’s Document AI platform, enables enterprises to unlock unlimited possibilities by gleaning insights from myriad unstructured data to deliver measurable business value.

For instance, a US-based banking client leveraged XtractEdge to digitize over 25000 plus loan applications in a week, that too during the pandemic and a record flow of Paycheck Protection Program (PPP) Small Business Administration (SBA) associated loan applications.

Thus, implementing XtractEdge improved the efficiency of the US bank’s loan processing operations by automating several manual processes that helped the client achieve data accuracy of about 90% This resulted in faster turnaround times for loan approvals and enhanced operational efficiency. Additionally, the automated process provided greater transparency and visibility into the loan application status for customers, leading to an improved customer experience.

Moreover, the IDP solution enabled the US bank to capture more loan business and generate additional revenues by processing loan applications more quickly and accurately. The reduced manual intermediation and human errors also decreased operational costs and increased profitability. Hence, by leveraging IDP, US Bank was able to make data-driven decisions that resulted in better outcomes for the bank and its customers.

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Final words

Considering the efficiency IDP can generate in data-driven decision-making, it will be rational to acknowledge that IDP is an effective solution with numerous benefits for businesses. This is because IDP can process vast amounts of data quickly and accurately, thus increasing the speed and accuracy of decision-making. Apart from this, it also offers insights into trends and patterns that might not be evident through traditional analysis, allowing businesses to yield more informed decisions. Furthermore, IDP can help enterprises to classify potential risks and opportunities, improving productivity and customer satisfaction.

Thus, embracing IDP technology is fundamental to improving business decision-making and achieving success in today’s data-driven business environment.

Maximizing the benefits of automation in insurance operations

The insurance industry is confronting various challenges in the present business landscape to keep pace with changing customer expectations, the emergence of new technologies, and intensifying competition. To navigate these challenges effectively and maintain a competitive edge, insurers must prioritize automation in their business operations.

Automation is a cornerstone of success for the insurance industry, and adopting these automated systems allows insurers to remain competitive, provide personalized customer experiences, and enhance operational excellence. Gartner’s recent survey of insurance CIOs and technology executives shows that insurance companies prioritize customer experience and operational excellence  through digitization in 2023. They use digital technologies to optimize operations and service quality, highlighting the significance of digitalization in enhancing customer satisfaction In today’s rapidly changing business environment, embracing automation is a strategic imperative for insurers looking to succeed and thrive.

Automation in insurance operations: Where the companies stand today

The insurance industry has quickly recognized the potential of automation in streamlining operations and improving customer service. Today, companies are actively investing in automation technologies like RPA, AI, and ML to automate various insurance processes such as claims processing, underwriting, policy issuance, and customer service. A report by Allied Market Research indicates that AI in the insurance market is expected to experience a remarkable 32.56% CAGR growth from 2022 to 2031, reaching a projected value of $45.74 billion by 2031.

Despite the benefits of automation, insurance companies, especially commercial insurance, still face a lot of challenges in adopting automation technologies. One of the main concerns is the need for skilled personnel to manage and maintain these technologies. Additionally, there may be resistance to change from employees who fear the potential loss of jobs. Another challenge is data security and regulatory compliance, as automation may require access to sensitive customer data. Moreover, legacy systems may not be compatible with newer automation technologies, making integrating them into existing systems difficult.

Low automation can have a negative impact on the bottom line of insurance companies. Manual processes are prone to errors, leading to higher claims costs, reduced productivity, and increased staffing costs. This can result in lower profitability and a weakened competitive position. In contrast, automation can improve efficiency, reduce costs, and boost customer experience, leading to increased revenue and profitability and a stronger competitive position in the market. As such, insurance companies need to embrace automation and ensure they overcome the challenges to reap the benefits.

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Benefits of automation in insurance

Strategies to maximize the benefits of automation in insurance

Identify the right areas for automation: Before implementing automation, insurance companies should identify the areas where automation can provide the most value. This can include processes that are repetitive, time-consuming, or prone to errors. Insurance companies can maximize the benefits of automation by focusing on these areas.

Choose the right technology: Insurance companies should select the right technology that fits their needs. This can include software that is easy to use, integrates with existing systems, and offers customization options. The technology should also be scalable and adaptable to changing business needs.

Invest in training: It is crucial to invest in training for employees to ensure they are fortified with the skills and knowledge required to use the new technology effectively. This includes training on how to use the technology, as well as training on how to adapt to new processes and workflows.

Emphasize data management: Automation generates a lot of data, and it is vital to have a robust data management strategy in place. This includes having the proper data infrastructure, analytics tools, and governance policies.

Continuously monitor and improve: Automation is not a one-time event but a continuous improvement process. Therefore, insurance companies should monitor the effectiveness of automation regularly and make adjustments as needed. This can include identifying bottlenecks, streamlining processes, and optimizing workflows.

The importance of identifying the right areas for automation cannot be exaggerated. Insurance companies can maximize the benefits of automation while minimizing costs by focusing on areas that provide the most value. This includes identifying areas where automation can improve efficiency, accuracy, customer satisfaction, and risk management.

Technology is crucial in maximizing the benefits of automation in insurance operations. The right technology can automate repetitive tasks, reduce errors, improve data management, and provide insights into customer behavior. This leads to better decision-making, increased efficiency, and enhanced customer experiences. Additionally, technology can help insurance companies to stay competitive by adapting to changing market trends and customer needs.

Automation with XtractEdge (A case study)

XtractEdge, EdgeVerve’s Document AI platform, helped an insurance company to automate its claims processing workflow by implementing an AI and machine learning-based solution. The solution automated tasks such as data entry, validation, and fraud detection, resulting in faster processing times, improved accuracy, and cost savings.

The implementation of XtractEdge resulted in a 50% reduction in claims processing time, a 40% reduction in manual errors, and a 35% reduction in processing costs, according to the company. In addition, the solution improved customer satisfaction and reduced the staff needed for claims processing, resulting in significant cost savings for the insurance company.

In general, XtractEdge’s solution helped the insurance company to streamline its claims processing workflow, reduce costs, and improve customer satisfaction by leveraging the benefits of automation using AI and machine learning algorithms.

The future of automation in insurance operations

Automation is swiftly becoming a critical component of insurance operations. Insurance companies are streamlining their processes by adopting automation technologies, improving the customer experience and operational efficiency. Claims processing, underwriting, and RPA are potential areas for automation in insurance.

Automating claims processing can help insurance companies reduce processing times, improve accuracy, and minimize costs. In addition, advancements in artificial intelligence and machine learning are making it possible to automate even more complex claims processes, such as medical records analysis or accident reconstruction.

Underwriting is another area where automation can have a significant impact. Automating data collection and analysis can lead to more accurate risk assessments and pricing decisions, resulting in more profitable underwriting portfolios. Automation can also speed up the underwriting process, resulting in faster policy issuance and better customer experience.

Robotic process automation (RPA) is an area of automation that can significantly impact insurance operations. Thus, RPA can automate recurring and manual tasks, such as data entry, document processing, and claims processing. By automating these tasks, insurance companies can enhance overall productivity and free their employees to focus on more complex tasks requiring human expertise and decision-making. According to Allied Market Research, the RPA market (2021) in insurance is expected to reach $1.2 billion by 2031, growing at a CAGR of 28.3% from 2022 to 2031, indicating a strong demand for RPA solutions in the industry.

Insurance companies that adopt automation technologies can significantly improve their efficiency, accuracy, and cost savings, ultimately giving them a reasonable business gain in the market. As advancements in AI, machine learning, and RPA evolve, we expect to see even more positive impacts on the insurance industry.

AI in supply chains – Unlocking the potential of an omnichannel ecosystem

The challenges in supply chain seem endless ever since the pandemic; with its rippling effect felt far and deep into the network. The changing geo-political situations, inflation, general economic meltdown, and natural disasters striking countries worldwide are key disruptors, causing labor and material shortages, equipment unavailability, and elevated freight rates.

According to experts, supply chain problems would continue for another year or two. Granular visibility in the network is one of the primary ways to help companies stay prepared for any upcoming disruption. Digital solutions powered by AI and Automation are harnessed by organizations to collect, analyze, and interpret high-quality, up-to-the-minute data and gain end-to-end visibility into their value network. Also, an omnichannel supply chain strategy supports a customer-centric network, end-to-end planning and information flow, connected operation, and a transparent distribution system. An omnichannel model keeps organizations ahead of the market challenges while delivering consistent and seamless services to meet customer demand. It eliminates siloes and provides a ground-level view of sales and distribution channels – essentials for success in the competitive market.

What does an omnichannel supply chain mean?

An omnichannel chain is a multichannel supply chain that uses different channels to serve customers for a superior experience. It merges several sales, warehousing, and other operating systems into a single, one-touch integration, collectively catering to online and in-store requirements. Typically, a retail store/boutique can sell goods through e-stores, in-person, and social media platforms like Instagram.

An omnichannel supply chain ecosystem can win if there is an end-to-end collaboration among all stakeholders, suppliers, and customers. And companies can conveniently meet changing customer requirements only when adequate information is shared along the entire value network.

However, migrating the entire network operations on a unified platform is much more complicated than it sounds:

Omnichannel supply chain solutions – Things to know

The journey from traditional brick-and-mortar store selling to a hybrid physical and virtual store selling model has been challenging for businesses. The primary challenge arose from the need to satisfy two different markets with multiple and sometimes conflicting requirements. A poor understanding of the new model would only add to their list of troubles. Therefore, complete knowledge of a few important facts related to omnichannel supply chains will make the transition easy and seamless.

Omni vs. Multichannel – Are they synonymous?

Multichannel and omnichannel are similar terms often used interchangeably but are different. Multichannel means leveraging multiple supply chains to satisfy unique shopping experiences, where each channel is separate and distinct. There are unique online catalogs and pricing. And each store operates as a separate entity. Contrarily, omnichannel supply chains usually operate with a single network. The online catalog and its prices are similar to physical stores and share information about online stock levels and stocks in nearby stores. It allows customers to visit stores and checkout items, but they can purchase online.

Simple migration – Or complete revamp?

An omni channel supply chain should be flexible to allow the supply of bulk orders to stores and individual items to online customers. Therefore, restructuring the entire network is needed to support important decisions regarding inventory storage, processing, and shipping orders. An omnichannel operation works with multiple regional distribution centers and often relies on third-party distributors.

Omnichannel means an integrated system

An omni channel supply chain will not perform in the absence of an integrated software platform. It requires every network element to be connected, including other operating areas such as marketing, sales, procurement, and logistics. Omnichannel supply chain solutions support complete network visibility, enhance forecast capabilities, accommodate adequate inventory planning and order management, and allow owners to adopt fast stock replenishment strategies.

Say goodbye to siloes

In order to support the objectives mentioned above, functional siloes need to be broken down. And an omnichannel setup does exactly that. It eliminates any possibilities of division in functions or individuals controlling the supply of information, materials, or sales. It paves the way for a transparent supply chain ecosystem, allowing customers the freedom and flexibility to browse online, visit local stores, and place orders when and where they want. With unhindered sharing of information, network agility and responsiveness improved.

Increased customer expectations

An omnichannel supply chain increases the ability to initiate purchasing process from a mobile platform, continue using a browser, and complete the payment in a physical store. Furthermore, customers can easily return items purchased online to the nearest store and receive a refund immediately. This fosters customer loyalty by providing a pleasant, convenient, and stress-free buying experience.

The role of AI in omnichannel supply chains

A gradual shift in shopping preference from physical outlets to e-commerce was already underway, which doubled in the last couple of years. Initially, many ventures were reluctant or slowed in adopting the transition. However, they are stepping up their technology investments to meet the needs of their buyers. Apart from solidifying their online presence, they are focusing on strengthening the value network to provide a convenient shopping experience. Hence, technologies like AI and automation are increasingly leveraged to enable a complete migration of operations without disrupting the general flow of goods and sales.

Here, an omnichannel supply chain plays a critical role, providing the stakeholders with much-needed visibility into what is happening at the supplier/distributor end and how they can keep up with the sudden rise in customer demand. And AI plays its part by breaking the data siloes and capturing ground insights in real-time, so suppliers, owners, and distributors are always ready with their contingencies regardless of changes in demand, sudden price spikes in transportation, accidental blockage in distribution, and shortage of labor. Multi-enterprise visibility into each source gives more control to businesses and assists them in adequate inventory management and curbing lead times. The primary objective of an AI-enabled omnichannel supply chain for e-commerce is to improve customer journeys. So, all the efforts to improve the entire demand-value network are directed towards ensuring customers get what they want (in terms of quantity, price, and quality), where they want, and when.

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Benefits of AI in supply chain operations

Primary AI applications in omnichannel supply chains

To support the omnichannel experience in a supply chain ecosystem, AI and related capabilities drive strategic improvements at key processes in operation. The following is a list of applications exemplifying AI’s contribution to changing the entire value network for businesses:

AI-enabled demand forecasting

Forecasting and planning stocks for matching demand have become more intricate in the post-pandemic era. Disruptions arising from time to time at different areas in the network also shifted sourcing locations closer to where the manufacturing units are. Customers, too, are exhibiting other buying behaviors, focusing more on local, sustainable produce and conscious purchase. Therefore, anticipating long-term demand has taken a back step. Instead, companies are redirecting their efforts to capture early demand signals in the short term. An omnichannel supply chain supported by AI technology enables owners to understand precisely what is happening in the market (at various locations), what customers prefer more, and where they are procuring their goods/services from earlier. Furthermore, big brands are challenged by local sellers because customers have shifted their preferences to cost-effective local purchases. Therefore, AI-demand forecasting is leveraged as the best bet to stay ahead of the market and competition simultaneously.

AI-enabled inventory management

The empty shelves due to the pandemic exposed incompetent demand forecasting, planning, and inventory management methods. One of the major causes would be poor visibility into what was happening at the supplier’s end. Omnichannel supply chain solutions supported by AI capability enable businesses to anticipate market demand and correctly prepare inventories accordingly. Stock-out or overstock situations are duly avoided, saving dollars in storage costs, and averting expensive losses due to damage or wastage of excess inventories. E-commerce and retailers are best benefitted since their shelves need to be stocked up to retain customers while ensuring perishable items don’t run out of shelf life before they are sold out.

AI-enabled logistics management

Labor shortages and exorbitant freight charges added to the high price customers had to bear to get their daily essentials delivered to their doorstep. In addition, the Suez Canal blockage disrupted the entire distribution network, eventually delaying services and deliveries. This eye-opening event, however, proved a catalyst for innovative technologies to find their firm footing here. For instance, AI-powered routing in omnichannel supply chain solutions enables owners to extract insights from various data sources and plan alternate travel routes. This allows goods to be transported from another route when the original is clogged with traffic.

AI-enabled customer experience

Here, numerous datasets and tech-enabled solutions make it possible to understand who customers are and what they intend to do. Therefore, enterprises can uncover ways and optimize their supply chains with omnichannel capabilities to serve them better by doing everything near real-time.

Roadblocks in omnichannel supply chain – AI-related challenges and remedies

Data restrictions: Obviously, these constraints exist to prevent information leakage. However, the same restrictions can prevent the proper functioning of an AI-powered omnichannel model. Minimizing restrictions allows easy real-time data integration into processes and systems while allowing digital business networks to maintain ‘single versions of the truth’ so operations can occur using the most available information.

Disconnected system: Fragmented systems are a significant challenge in any company. Technology can vary locally and globally within the same company and between partners. Therefore, a proper framework and adequate infrastructure should be constructed uniformly across the company and its locations before considering implementation.

Operational costs: At times, introducing an intelligent software system can sometimes exceed the allocated budget. However, this is a one-time investment. So accurate implementation and maintenance will guarantee higher returns. Furthermore, when sourcing AI software solutions from third-party vendors, the market is spoiled for choices. Hence, finding a vendor within your budget is possible, offering a customized solution that aligns with your business objectives.

The final takeaway

The primary objective of the omnichannel approach is to offer customers a superior shopping experience matching their time, quality, quantity, price, and convenience preferences. And that requires migrating the value network and its operations to a unified platform for end-to-end visibility. Hence, the concept of building an omnichannel supply chain is gaining importance presently. After all, the market we knew no longer exists now, and customers have changed their buying decisions to sustain the tumultuous post-pandemic era. Therefore, when the market and buyers are changing, businesses must adapt fast and stay available exactly where customers are and when. This is the key to staying ahead and competitive in a saturated ecosystem.

Claims process automation: Say goodbye to manual claims processing

Claims processing is an important service in the insurance sector, where insurers receive, investigate, and process a claim/theft report submitted by a policyholder. Like any other loan/insurance applications, claims also involve much paperwork, which is time and labor-intensive, thereby delaying claims settlements. Despite the HIPAA regulations stating that all standard transactions like claims should be submitted electronically, processing and submitting claims manually are still a reality for numerous unavoidable reasons. And, surprisingly, a large portion of customers still hesitate to present their claims digitally as they prefer human contact. However, experts believe that claims process automation will eventually take control over the claims market by 50% or more. New millennials and Gen Z will dominate half of the world’s adult population by 2030, and they prefer less physical interaction and more digital services.

What is claims processing?

Claims processing is a crucial business process involving obtaining all the necessary information about policyholders to determine the appropriate amount to be paid on a given claim. In addition, it specifies an insurance company’s liability for each claim.

The entire claims process is intricate, requiring complex data from internal and external sources for further assessment, approval, classification, payment calculation, and claims settlement. However, as stated earlier, the manual approach is time-taking and prone to error, resulting in inaccurate claim terms and ineffective customer service. Further, claims processing workflows are engineered solely for adjuster efficiency rather than maximizing user experience. As a result, without realizing it, insurers are losing profitable business opportunities while handling and processing claims traditionally.

Therefore, claims process automation is the best way forward, as it helps insurers to support speedy resolutions, clear communications about claims’ status, and human touch in customer service – the prerequisites of modern users.

Automation and its role in claims insurance, finance, and healthcare

According to experts’ predictions, claims processing would continue as a primary function for carriers. However, the need for efficiency and accuracy of operations, deliverables, and customer support is increasing steadily among insurers looking to eliminate the risk of litigation or fraud with claimants. The downside of manual processing wrong claims experience can lead to poor customer satisfaction and higher costs for insurers. That’s why insurers must look beyond cost savings for more data-driven and analytical customer service. Also, to preserve the growing expectation for the human touch in insurance claims, insurers need to bolster their technology infrastructure to settle claims faster, with high accuracy, and at lower costs. Therefore, manual workflows would be replaced by advanced technologies like automation and AI. Insurance claims automation leverages advanced algorithms to accelerate claims routing for greater efficiency and accuracy while curating a personalized customer experience.

An integrated claims process automation also provides data to fight and prevent fraudulent activities. With advanced technologies like IoT and new data sources at their fingertips, insurers are better positioned to monitor risk, trigger interventions, and mitigate challenges at scale. At the same time, automated claims processing relieves workers from labor-intensive low-value tasks so they can speed up case resolutions and payments.

The problem with manual claims processing

Manually processing claims is tedious, comprising numerous low-value recurring tasks like creating vendor profiles and collecting unstructured data from disparate sources. This approach increases the complexities of workflows and gives birth to process inefficiencies leading to a higher dependency on human resources. In addition, the manual method gives rise to processing backlogs and various other challenges, impacting daily operations. Lack of governance and cohesiveness, lengthy processing time, hurdles in data extraction and claim verification, higher operating costs, and poor compliance are some of the critical challenges of manual claim processing. All these factors culminate in poor customer support service and an increasing number of dissatisfied customers.

Manual claims processing and its business impact

Providers still residing in the ancient era are losing their market hold to rising competition from InsurTechs and other non-traditional players threatening the former’s existence Hence, there is growing pressure on insurance companies to start thinking beyond incremental improvements. Unfortunately, claims providers are not sophisticated enough to harness the wide variety of data available to improve the customer experience.

Today, speed, efficiency, and transparency are critical for quality claims experiences, and tech-native claimants expect service initiated from mobile devices and completed within seconds. The inability to accommodate popular choices is causing an existential crisis for insurers. And over-reliance on manual efforts is simply exaggerating the current challenges. As stated earlier, handling process requests in bulk manually takes work. The complexity of datasets delays the whole process, creating backlogs and an increasing number of dissatisfied customers. This affects process efficiency and employee productivity while driving growth opportunities for the business. Like many other areas in insurance, the claims process requires immediate automation attention as poor documentation, delayed application, inadequate insurance coverage, and insufficient knowledge of providers aroused out of manual processing prove detrimental to a business’s health.

Claims process automation – How is this an effective solution?

Claims are the most visible, customer-facing function in the insurance sector. Poor claims experience can damage the provider in terms of cost and brand reputation. In the digital era, where social media influences a buyer’s investment, purchase, and security preferences, the risks for insurers are further amplified when dissatisfied claimants share their bad experiences on social and customer review sites. Their reviews, in turn, influence other prospects’ choices as well. However, the same platform can be used to gauge customer needs and take necessary actions.

Insurance claims automation leverages advanced technologies, data, and analytics to accelerate the auditing and recording of applications and their approvals for speedy resolutions while meeting compliance standards effectively. Further, a combination of AI, automation, data, and human intelligence allows staff to work more productively and effectively to respond to unprecedented surges in claims promptly. Digital improvements and data in the claims process can help insurers with the following:

Therefore, claims process automation and data-driven and analytical experience can easily balance financial performance and customer satisfaction for insurers.

Roadblocks to claims process automation

The pandemic catalyzed innovation and improvement, a testing ground for insurers to put their technology investments on a fast track. Companies pivoted overnight to transform their operation dramatically and embrace the new norm of remote work and customer engagement, overcoming cultural, structural, and other barriers.

Today, claims process automation is no longer a privilege; it is necessary to meet the needs of digitally native customers, who will make up nearly half of the adult population by 2030. Until then, carriers must balance the varying expectations of young adults with those of Gen Xers and baby boomers not comfortable with digital interactions. Therefore, insurers are still confronted with numerous roadblocks when fully automating and digitalizing the claims process.

Resistance to change: For every change to occur, initiators must overcome the mental blockage of people toward transformation. The biased approach to technological advancements, especially for automation and AI, has stemmed from the general paranoia of technology replacing humans. Such resistance is detrimental to business growth and prosperity. Therefore, a good session should be arranged to educate employees on the potential benefits and career advances courtesy of new-age solutions.

Lack of technical know-how: A biased mindset is the birthchild of inadequate knowledge about innovations and technologies. Skills enhancements are crucial for advanced software solutions to run effectively and reap the benefits. Therefore, insurers should enhance their in-house staff’s skills and technical knowledge, which would be comparatively more cost-effective than hiring tech experts separately.

Absence of adequate IT infrastructure: Just elevating employees’ skills and technical understanding will not be enough if proper IT infrastructure is lacking. Hence, the first step towards strategic change should be building an in-house IT team responsible for integrating new software systems, educating employees about the software basics, monitoring its performance, and providing initial support during breakdown.

Data privacy and security concerns: Data privacy is a primary concern when implementing an automated software system and feeding it with confidential data. For instance, claims process automation requires processing, evaluating, and recording customers’/clients’ data for approving insurance covers. Any vulnerability existing in the system can lead to data leakage, costing the company’s trust, reputation, and revenue as penalty fees. Thus, automated platforms from providers like EdgeVerve, provide security assurance apart from accelerating the long-haul claims process.

Benefits of claims process automation

Claims process automation can help insurers overcome numerous challenges their staff faces, from documentation to record maintenance, claims approval, final processing, etc. Automation curbs settlement timelines and assists staff in crafting stellar customer experiences. Therefore, the benefits can be summed up in the following points:

Strategic steps to successfully implement claims automation

The steps to implement claims process automation would be similar to any software system integration. Here is a list of strategic steps to follow to initiate an automation pilot project:

Step 1: Define your automation objectives

Identify and shortlist reasons why you need an automated software system for the claims process.

Step 2: Decide your budget

Understand how much return you can obtain from an investment and decide your technology budget accordingly.

Step 3: Verify vendors before selection

Learn about the shortlisted vendors by checking clients’ online reviews, matching their offerings against the price, and taking your pick.

Step 4: Employee buy-in

Get your employees on board with the change and educate them accordingly.

Step 5: Pick a pilot project

Start by automating claims for smaller clients and understand how claims process automation works and the benefits you obtained before testing on more prominent clients.

Step 6: Prepare your datasets

An automated system works best in the presence of data. So, take the help of automation and AI technologies to capture, extract, and create your database.

Step 7: Build your IT infrastructure

The in-house IT team should be well-equipped to offer the necessary backup during system breakdown before the support team from the vendor arrives.

Step 8: Regular software monitoring

The in-house staff and the IT department are responsible for continuously monitoring the new platform for bugs and fixing them accordingly.

Step 9: Evaluate and scale

Observe the new software system’s changes and evaluate its performance and overall impact before deciding to scale automation to other vital areas.

The future of claims process automation

The future of claims process automation looks promising, with more data and analytics and advanced technology solutions redefining how claims are received, processed, approved, and settled. With cutting-edge solutions like IoT and Blockchain making inroads into the insurance sector, the whole process would be further streamlined, much to the satisfaction of both employees and customers. A few technology trends to look out for would be:

The key takeaway

Change is constant, and the evolving technology market can attest to this statement. So, it is given that digital native customers would expect services matching their conveyance and expectations. Therefore, claims process automation has become the need of the hour, and insurers can no longer steer clear of the change. However, its success depends very much on the type of software solution selected.

XtractEdge Platform from EdgeVerve can easily integrate with existing enterprise applications and tags along with various advanced added features, customization, and tech support, enabling insurers to scale up their claims process. In addition, the platform is known to process millions of documents at a time. And it offers robust security features to prevent any data breach. With InsurTechs making headway into the market, claims process automation is the only way for insurers to stay ahead of the competition curve while catering to their client’s needs per the latter’s preference and conveyance.

Invoice data extraction: Everything you need to know

Extracting data and processing invoices are critical business functions, uniform for every organization, regardless of industry or niche. It is an integral part of every enterprise’s account payable department. A systematic approach to data capture from invoices removes backlog and transaction errors. Information about the invoice number, supplier name, address, amount, etc., is duly drawn out of the physical/digital invoices for further validation against sales receipts and POS before initiating payments. Finally, the figures are updated in the ERP/accounting system. Therefore, in simple words, invoice data extraction functions much like traditional paper ledgers.

What is invoice data extraction?

The global market economy produces approximately 550 billion invoices every year, which is expected to quadruple by 2035. These legally enforceable invoices are legit evidence of business between two entities and serve as a formal request for payment from clients/customers. Invoices are generated in the event of goods delivery from sellers to customers or during the purchase of items from suppliers. Such paper documents carry valuable business insights, including pre-decided and agreed-upon terms and conditions, the number of goods exchanged and the amount to be paid/received, names of vendors, dates of transactions, and other details. This information is essential, enabling buyers and sellers to track and record their payments and amounts owed easily. Therefore, timely extraction of such data and updating in a centralized accounting system helps businesses later in generating financial reports or building informed strategies for their growth and expansion,

Invoice data extraction is simply the process of drawing out data hidden inside invoices to ensure their availability as and when needed, for example, generating future invoices concerning the same parties, extending business contracts, renewing the terms, or simply closing the account.

How does invoice data extraction work?

A typical invoice comprises the following information:

Some invoices can be more detailed, consisting of other equally essential items such as terms and conditions outlined for the reference of concerned parties. All this data must be updated in the company’s account payable system without errors or omissions. Now, in the face of numerous invoices generated and processed daily, extracting and updating information can be a long-haul endeavor, exceptionally when executed in an old-fashioned manual way. The chances of mistakes remain high, and the subsequent challenges are numerous.

A few such challenges posed by manual invoice data extraction and processing would include the following:

Contrarily, an automated and AI-powered invoice data extraction software solution can be a blessing in disguise for both the AP team and clients. It eliminates human intervention and handles the entire process of generating invoices, processing them for approval and sharing with parties, extracting data, and updating the centralized system in seconds or minutes. The software tool follows the detailed instructions you give and pulls data directly from invoices without spending time typing, copying, and pasting each data separately. The platform also supports various formats for invoicing, enabling automated retrieving, reading, validation, and posting of invoices to the enterprise’s ERP system.

Nine primary benefits of invoice data extraction

From the business standpoint, an automated invoice data extraction process has its own set of benefits, from eliminating paperwork to reducing hours of manual work and improving employee productivity. Here’s a list of essential benefits of using an intelligent solution for invoices:

Enables a touchless, paperless process: In the presence of digital solutions, the need for paper-based invoices is omitted from the entire process. This approach supports a touchless, paperless, environmental-friendly, and labor-free business operation, which is the need of the hour.

Reduces storage cost: Paper-based invoices require extra space for storage. But digitizing the entire processing and invoice data extraction workflow eliminates the overhead cost incurred from purchasing papers and physical folders in bulk and the cost of storing them for later use.

Optimizes time, cost, and labor: As mentioned, digital invoicing removes the cost of purchasing papers, folders, and storage units. In addition, with lesser human involvement, productive hours and resources are better utilized for the company’s greater good.

Increases staff productivity: Any manual, long-haul, repetitive task involving extracting and entering datasets in volumes can quickly reduce the overall productivity of workers. By automating data extraction from invoices and other related tasks, employees are freed from the burden and focus on other critical, value-driven tasks.

Eliminates human errors: Time-intensive, manual tasks, especially related to data extraction and documentation, are vulnerable to human errors. Missing crucial data, overlooking fine nuances, or omitting critical insights during invoice data extraction can prove highly costly for the business, from issuing duplicate payments to missing payments, ruining relationships with partners/clients, and damaging the company’s reputation in the market.

Shortens invoice approval cycles: With a digital platform shouldering the whole invoice operation, numerous back-and-forth email exchanges are duly avoided. Furthermore, when all the digital invoices are available for quick access on a centralized ERP system, concerned department heads can easily verify and approve invoices without delay.

Elevates data quality: Minimal human intervention in invoice data extraction eliminates any possibility of missed or wrongly deduced data. So, the insights extracted by the automated system are high in quality, accuracy, and reliability.

Omits chances of unpaid or delayed invoices: Specific invoice data extraction software solutions share notifications to respective departments, alerting them of invoices requiring approval for processing and payment. In addition, the heads can easily access past records of earlier invoices to validate the accuracy of the current ones and approve and process payments as fast as possible. Such features standard in most intelligent software solutions negate any chance of missing/unpaid invoices. As a result, vendors get paid on time, and enterprises receive the services and products to continue their operations.

Streamlines the whole process: The operation of processing, documenting, approving invoices, and extracting data from them is streamlined and accelerated. Hence, the subsequent processes are sped up, ensuring efficient and effortless delivery of business objectives.

Critical invoice data and how they help businesses

Invoices are business records allowing companies to get paid for their services. These documents are legal evidence of agreements between parties and their attempt to respect those terms. Invoices are mediums used for requesting clients to make payments on time, maintain an account of sales and supplies, track business inventory, predict future revenue, and monitor business income for tax purposes. In addition, they help record business transactions.

An invoice comprises a range of datasets, including invoice number, supplier name and address, project details, PO number, and other critical details. As stated above, this information helps track goods and services obtained from vendors and suppliers. Hence, timely extraction and maintenance of each data in a centralized system help businesses stay informed, strategic, and competitive.

Invoice data extraction benefits businesses in million ways, for instance:

Seven steps to implement invoice data extraction

Invoice data extraction software solution works like any other tech-enabled data extraction platform. However, a one-size-fits-all approach doesn’t apply in most enterprise scenarios because of document complexities and domain specificities. Regardless, a purpose-built document extraction, processing, and comprehension platform powered by AI, Machine, and Deep Learning capabilities should be the ideal option.

For instance, the XtractEdge Platform can efficiently structure complex multi-document data and make it consumption-ready, ensuring owners unlock limitless business value quickly. As mentioned, the invoice data extraction platform functions like other document-related ones, and their successful implementation involves careful planning and identifying the best fit worthy of your investments. The following is a list of essential steps to ensure your implementation process goes as planned.

Step 1: Define your software objectives

Why do you need an intelligent software solution for your invoices? This should be your first question before rounding up vendors’ names and details. Understanding why you need technology’s help for invoice data extraction or whether an automated platform with basic features could suffice your needs can help you pick the right solution. Next, list essential requirements, from the volume of documents and data entering your system daily to types of documents, the minimum number of fields to extract, and how to organize documents or data for future purposes. All these elements factor into your decision-making and likely narrow your options.

Step 2: Decide your budget

In the case of invoices, hours of manual work can be saved, costs for papers and files eliminated, and expensive human errors avoided. The market has many free, paid, or semi-paid solution providers. So, take the step of comparing each vendor for their software features and prices before you make a purchase decision.

Step 3: Verify vendors before selection

Besides comparing vendors’ software features and price, verifying directly from their clients is essential. Obviously, you need help picking up the phone and questioning one of their buyers. Instead, spend time reading their online reviews to understand what other buyers think of the products.

Step 4: Pick a small pilot project

Never try to automate an entire process using a software solution. In the case of invoice data extraction as well, start small and then gradually scale transformation evenly across the length and breadth of your organization. So, start by determining the number of invoices leaving or entering the enterprise’s system, where they are coming from or heading to, and where the bottlenecks lie in the process earlier. This helps identify which steps in the invoice data extraction consume the most time, increase costs and errors, and create unnecessary delays. Therefore, implement the tool only for small suppliers, see the difference, and scale the change across all other suppliers and partners, big or small.

Step 5: Prepare your datasets

Now, AI and automation systems will only work with concrete datasets. So, identify what kind of data the platform needs to perform a task like extracting invoice data. Then, create rules and feed appropriate data into the system to enable seamless, hassle-free performance.

Step 6: Strengthen your IT infrastructure

Now, many intelligent software solutions fail post-implementation for multiple reasons. Two reasons would be – more software understanding among employees and poor IT infrastructure for maintenance and repair. For example, sourcing an invoice data extraction solution from a third-party vendor guarantees ongoing customer support, but if resources involved with the system have no basic understanding of how the software works, the latter will fail to perform. Therefore, work on these areas before you seriously change your operations.

Step 7: Evaluate and scale

Every software solution requires continuous evaluation, monitoring, and timely maintenance to perform as expected. And the tools are good enough to be implemented in more critical business areas. So, an IT team should be there as backup, monitoring the software solution, testing for bugs, and remediating issues before the tech support from the vendor reaches your door.

The future of invoice data extraction

Data and analytics are the future — considered the key to unlocking competitive advantage for businesses in the market. And invoices are taken as one of the primary sources for business data, but sadly, unstructured data in invoices and other documents continue to grow at 62% per year. This poses a significant challenge for decision-makers without adequate tools and technologies. Therefore, the future of data extraction, not just for invoices but for various other sources, would be significantly impacted by Big Data and advanced technologies supporting the extraction, storage, and accessibility of such data volumes. Big Data is nothing more than datasets comprising a greater variety of information, arriving in increasing volumes and with more velocity, promising game-changing benefits for businesses. But, they need the support of adequate technologies to help extract detailed insights from unstructured datasets and store them in a centralized place.

Therefore, advanced predictive analytics, Artificial Intelligence, and its extended capabilities like ML, Deep Learning, and others, as well as more intelligent documents and invoice data extraction tools, are likely to dominate the mainstream, enabling businesses to leverage data and unleash endless possibilities for their organizations.

The final takeaway

Invoice data extraction software solution powered by AI capabilities has become the need of the hour. Since they contain crucial information, timely extraction has become imperative for data-driven decision-making – a key to staying ahead of the competition curve. Therefore, a comprehensive guide on invoice data extraction tools could provide a good head start in the same direction.