Month: June 2022

Document-intensive workflows are usually time and labor-intensive and, if handled manually, can delay the processing of claims and policies indefinitely, leaving the workforce with minimal bandwidth to take up new issues. And manual document processing and extracting information often leave behind a trail of human error. So, returning to those documents to address those errors or re-connecting with customers for more information that was initially overlooked only adds to the delay.

According to reports, unstructured documents are growing by 55% to 65% every year. This translates to millions of documents. Hence, managing and storing these documents is a huge challenge for an insurance provider. How can commercial insurers address this problem?

Document AI in insurance

According to reports, more than 80% of documents that are processed by insurance firms contain mostly unstructured data.

Hence, Document AI in insurance can be the best bet considering the massive volume of documents waiting to be handled on a daily basis. AI in insurance cuts down the long waiting times for customers by bringing down the long hours of reviewing documents, which are initially handled manually.

A purpose-built Document AI platform with advanced capabilities like Machine Learning and Deep Learning helps organizations quickly scan, analyze and understand documents, emulating human understanding of files and data as closely as possible. For instance, XtractEdge is a cloud-based solution that easily tackles complex multi-document data, making it consumption ready to unlock the latent business value.

Insurance processing challenges and remedies

In the insurance industry, processing new policy requests typically take time. The reason is that insurers have to collate large amounts of data from multiple documents and sources. Additionally, they have to use proper guidelines, codes, and modifiers. Each application is reviewed for accuracy and accepted or declined. Then, requesting additional or missing information in certain instances is necessary, which prolongs the process. Hence, lengthy processing time and faulty human judgments occasionally impact the customer experience.

The employees must work with unstructured data, which adds to their problems. Statistics suggest that  85-90% of these documents require manual effort to extract, validate and structure the data.

Semi-structured and unstructured documents have the following characteristics: –

Document AI in insurance solves the pain points of managing semi-structured and unstructured documents.

Conclusion

As companies prepare for the surge in growth in these markets, there will be an increase in the dependence on emerging technologies and data sources to drive efficiency, enhance productivity and expand capabilities across the organization. Document AI in insurance is just the tip of the iceberg, but its benefits for the insurance sector, especially for the employees and customers, are manifold. It expertly handles and solves the document-centric complexities previously faced by insurers.

A full-scale enterprise-wide digital transformation is no longer considered a choice. Companies rebranding themselves on the basis of complete digitization see it as a necessity. Even prior to the global pandemic, companies were under pressure to cut overhead costs through Automation. RPA was a good fix for the typical, high volume, repetitive, transactional work, but it was only for a short while and limited business use cases. Moreover, it is not scalable; hence, companies searched for an intelligent option – Cognitive Machine Reading (CMR).

What is Cognitive Machine Reading, and what are its examples?

Robotic Process Automation helps companies effortlessly handle a high volume of recurring tasks that usually take 100 people to complete manually. Speed and precision were achieved at scale, but RPA scope is limited to rule-based tasks only. Nevertheless, RPA was a true harbinger of new technological advancements providing companies with more options to explore. i

Cognitive Machine Reading, for instance, offers an opportunity to shift that work to intelligent technology. It is an AI-enabled technology solution that can extract the content, respond, read an attachment, extract the data, and automate process execution.

More often than not, businesses mistake scanned images for digitized documents. However, little do they know that the main distinction lies in how consumable the data is. For example, a PDF is a digital file but is less consumable in decision-making. The only solution is to digitize data accurately by implementing CMR at the point of entry to digitize data and then integrating automated decision-making and execution.

How does CMR drive end-to-end enterprise process automation?

Cognitive Machine Reading has the ability to successfully ingest a broad range of data, from tables, checkboxes, handwriting, cursive, images, and signatures. This capability of CMR opens the door to continuous and uninterrupted end-to-end process automation.

CMR is integrated into leading IA platforms and existing enterprise omnichannel solutions, enabling the processing of large amounts of data and workflows in a fraction of the time typically required.

Usually, critical business decisions are made using unstructured data, which comprises nearly 80% of essential enterprise information. Optical Character Recognition relies heavily on templates and zones to scan for data in documents. It needs document consistency. Unlike OCR, Cognitive Machine Reading can easily capture granular insights from various file types and unstructured data.

CMR benefits companies in the following ways:

For an insurance company, customer data is its biggest asset. Hence, information collection and processing form a vital part of their businesses. Unfortunately, the nature of data collected is riddled with its unique challenges, such as high human dependency and inaccuracy in data collection, how the documents are processed, or the time taken for each document processing. It is a cumbersome task for the employees who stand to benefit significantly from tech-based solutions like Document AI.

Document AI in insurance expertly handles the time, cost, and labor-intensive factors when processing bulk customer documents, completing them at record speed with zero errors or downtime.

How AI-driven Document Digitization is transforming Life Insurance

As mentioned earlier, processing bulk customer documents is time and labor-intensive, and the cost of managing the same data is immense. Even though many organizations are still reluctant to accept the change and continue with paper-based inputs, studies say otherwise.

Document AI in insurance addresses the cost factor of storing and managing data effectively and contributes in different ways. For instance, processing large volumes of semi-structured and unstructured documents requires significant manual efforts. Experts believe that insurers should address this problem with the right strategic approach. And that strategic approach is none other than a comprehensive, user-friendly, cloud-based Document AI platform.

AI in insurance focuses on three key areas:

Document AI in insurance: Key business use cases

Digitize application form intake: Document AI in insurance caters to the intake of application forms, including handwritten notes from agents and the field sales team. It can efficiently process handwritten notes, checkboxes and tables, and many more.

Underwriting review: Review and digitize various underwriting submission forms such as scanned forms, images, unstructured documents, and fraud detection.

E-Delivery of documents: Helps create e-Delivery packages for the digital delivery of policy documents, including Doc to Digital capabilities for assembling various customer documents.

Digitize service form: Document AI manages service forms using OCR-enabled dynamic form recognition and content extraction, such as handwritten sections, checkboxes, and tables.

Digitize claims submission form: Reviewing and extracting content from claims submission documents, including forms and unstructured documents. Its key capabilities range from key-value pair and image extraction to intent extraction and new issue fraud detection.

XtractEdge: One Document AI product for unlimited variety of insurance documents

XtractEdge uses advanced capabilities like Machine Learning and Deep Learning-based techniques, flexible data management, and analytics pipelines to tackle complex multi-document data easily. For instance,  XtractEdge Platform enabled an American health insurer to achieve 90% accuracy with information extraction from multiple claim request forms and supporting documents and considerably reduced claims processing time. This makes its consumption ready to unlock the latent business value.

In a nutshell, Document AI in insurance is the way to make insurers future-ready and easily handle bulk claims by intelligently negating document processing complexities.

Commercial insurance underwriters are a valued resource. Sadly, they spend time on non-critical activities like data collection and reconciliation. As per the McKinsey report, 30-40% of underwriters’ time is spent on administrative tasks, such as collecting data or manually executing analyses. Without data standardization, meeting every client’s varied needs on time becomes difficult. However, Intelligent Document Processing can help underwriters easily reconcile data from various sources at record speed when considering risk exposures, changing threat landscape, new business models, and industry trends to process submissions.

What is Intelligent Document Processing?

Intelligent document processing uses Artificial Intelligence, OCR, and other tech capabilities to extract data from unstructured documents and convert them into structured, usable, and consumable information. Intelligent Document Processing tools provide end-to-end automation to document-centric businesses, like commercial insurance.

In the commercial insurance landscape, the brokers and underwriters have to work with data needed for processing applications, usually shared in various document formats and layouts. The data can be available inside emails, forms, and attachments, coming in different formats, templates, images, graphs, tables, and handwritten notes. Adding to the complexity are the variations existing in the sources themselves, like using different nomenclatures or different ways applied to depict the same data. And then, there is the massive volume of documents that need to be processed daily, which creates a challenge of its own. Underwriters must process each document and extract and assess the veracity of the information shared. Hence, their task is quite cumbersome, timely, and labor-intensive. No wonder expert underwriters are an expensive resource.

Hence, variety, variations, veracity, and volume of documents are critical challenges in the commercial insurance sector. Document processing can intelligently and effortlessly address the above-mentioned issues at record speed, without any delay or committing errors. Such a tech-based solution can be a blessing in disguise for the sector, especially for commercial insurance underwriters.

How does IDP address underwriting challenges?

Apart from the above-mentioned document-related challenges, the underwriters are faced with other challenges about incomplete and unstandardized information, delaying accurate and timely decision-making. The challenges broadly cover the following:

Hence, decision-making about pricing, unnecessary risks, and incorrect rejections, adversely impacting customer experience and insurer profitability, are primarily flawed.

In order for insurers to improve underwriters’ productivity for business transformation, they need to:

Commercial insurers are increasingly adopting Intelligent Document Processing solutions to support underwriters with somewhat structured data. But, simply converting documents into data is not good enough to enhance underwriters’ productivity. The focus should be beyond simple data extraction; in other words, orchestrating processes and delivering value.

Hence, a holistic, data-driven transformation of the entire process with the help of Intelligent Document Processing tools should offer the following values:

Intelligent Document Processing: Transforming the future of underwriting

Intelligent Document Processing offers a standardized process for commercial insurers to process documents and take action quickly.

However, any change is never short of roadblocks and complexities. Intelligent Document Processing solutions cannot be implemented indiscriminately without high request volumes, organizational inertia, and lack of readiness of the team. Hence, commercial insurers should ascertain sufficient submissions to enable the transformation and the willingness of resources to embrace the change openly. Otherwise, such roadblocks would nullify the benefits of IDP implementation completely.

In a nutshell, Intelligent Document Processing can help standardize, collate, collect, validate, and analyze data, enabling the underwriters to move from high bandwidth, low-impact activities to high-value activities like accurate and timely pricing. That said, businesses should understand that technology adoption does not mean underwriters will become obsolete. Instead, Intelligent Document Processing is a fitting example of a human-machine partnership where intuition and analysis create excellent synergies.

Past responses to crises have created a significant shift from low-cost labor to higher-value work. Businesses have realized that nothing short of digitization can secure anti-fragility in the present. Besides having a digital workforce for resilience and survival, companies also need data digitization. Recently, new advancements have been made in the field of data science and technology to ensure an intelligent approach to capturing granular insights with the help of Robotic Process Automation (RPA) and Intelligent Automation (IA) to uncover patterns and disparities existing within the organization.

What is data digitization?

According to SME Robert Garagiola, Senior Director for Global Enterprise Data, enterprise data is stored and organized within proper repositories, databases, data marts, lakes, and warehouses. However, some information is available, unstructured, and scattered, across multiple digital formats, like emails and pdfs. Unstructured data, including images, handwriting, signatures, and mobile content, pose a challenge to capturing and digitizing data. And many data are found in non-digital paper-based documents.

Data digitization is the process of converting company information and documents from analog to digital, making data easily accessible and consumable by humans and machines alike.

There is a gap existing between what you believe to be data digitization and what it actually is. For instance, information present in PDFs is digitized data but not in a consumable format at a point in time when you need to make crucial decisions. So, information hidden inside videos, documents, emails, pdfs, and spreadsheets flowing about everywhere for decision-making are examples of unstructured, unconsumable digitized data.

Data digitization offers the following capabilities: Cognitive Machine Reading (CMR) and Machine Learning to classify document data across dozens of languages and lead to end-to-end process automation. Contrarily, Optical Character Recognition (OCR) captures only structured data.

Why is data digitization important?

Automation and AI require fewer resources to offer the following benefits:

In order for businesses to achieve end-to-end process automation, they need analytics-based insights to evolve in today’s markets. But both these technologies thrive in the presence of structured, digitized data.

Sadly, most enterprise data is present in an unstructured and non-digitized format. As per studies, the most significant challenge arises from getting data in a structured, consumable layout when transitioning to intelligent process automation.

Only those areas achieve the highest levels of success when characterized by structured data. The remaining processes involve large quantities of unstructured documents, such as onboarding forms for HR, driver’s licenses for claims, or financial reports for financial spreading.

Data digitization solutions

The main objective of data digitization is to read, recognize, and convert data in an integrated way. As digitized, structured data leads to frictionless downstream processing, using the same Automation can refine downstream activities. Since 81% of respondents in a survey considered end-to-end process integration a top priority for shared services, end-to-end workflows, and data digitization are considered a critical trend.

In order to achieve that, businesses should intelligently leverage AI and associated capabilities like ML and other cognitive solutions to recognize patterns in documents and classify them without any human involvement. Once patterns are identified, Machine Vision and ML models can be trained to extract data. Cognitive Machine Reading (CMR) can also be effectively utilized here. CMR uses pattern-matching via content-based object retrieval methods to digitize a full range of data formats, extract and structure data, apply business rules and enable rapid downstream processing.

In a nutshell, data digitization is the means for businesses to achieve end-to-end process automation and make insightful decision-making. Data is the solid foundation on which an organization can thrive. But, raw data has no value unless it is made consumable for AI and Automation to work on. Hence, it is crucial to understand the distinction between consumable and non-consumable data and whether they are structured and digitized. With the right capabilities and digitization solutions, businesses can solve the problem of data unavailability when and where needed.

AI in the insurance industry is growing rapidly, and experts believe the market value will reach $6.92 billion by 2028, growing at a CAGR of 24.05%. The new tech capabilities like Artificial Intelligence, Machine Learning, Deep Learning, Robotic Process Automation, Computer Vision, and Natural Language Processing can potentially restructure the legacy approach to managing the insurance lifecycle, from customer procurement to claim processing. The change is driven by the growing need to offer hyper-personalized insurance services to end-users. The need for speed and accuracy are probably other factors influencing the growth of AI insurance.

Factors driving AI Insurance

Omnichannel CX becomes a reality in insurance

The insurance sector is at the lower end of the scale when it comes to digitalization. Many challenges prevent a digital future with a truly omnichannel customer experience offering. However, the pandemic has proved that the digital front is the only way forward as more and more customers turn to online shopfronts to conduct their business. There is no alternative if insurance providers want to stay competitive with their customer experience.

Another important factor, according to Munich RE, is that the true potential of digitization only becomes apparent if it is consistently implemented along the value chain. To realize that insurers have to navigate complex legacy system landscapes with unlinked datasets in sales, the management of insurance applications and claims processing, major anti-selection risks from overly generalized automated solutions, and serious regulatory obstacles.

Digitization has enormous potential when it comes to creating efficient processes, reducing costs, and innovating new product offerings.

Data and analytics

Data has been utilized in underwriting. But, recently, insurers have been striving for more sophisticated data analytics to improve the customer experience by better customer segmentation and targeted offers, enhancing risk assessment in underwriting, reducing the cost of claims, and identifying new sources of sustainable growth.

Unfortunately, certain issues are plaguing the industry, such as data analytics solutions not being embedded into business processes, the value of data analytics solutions not being defined or not measured structurally, and a lack of company-wide vision and strategy. In order to profit from AI in insurance, they need solid data analytics capability first. And with AI comes even more powerful data analytics potential, creating additional pressures to adopt data analytics solutions quickly.

The underwriter as a decision scientist

Experts believe that the future of underwriting will be transformatively driven by talent and technology. Eventually, the underwriter will evolve from a mere risk assessor to a decision scientist. The need for improved decision-making and loss ratios can be met by multiple statistically-based models and codified, heuristic underwriting rules to support sophisticated analytics and rules-based decisions. This will drive higher levels of productivity. Predictive modeling and Machine Learning capabilities are needed for a more granular level in analytical and transactional models, enabling the process of underwriting to move into sales, retention, and CX, as well as early detection and analysis of anomalies and nuances to improve the precision of models and rules.

Challenges and remedies offered by AI insurance

Presently, the insurance sector is grappling with many challenges, much of which was triggered by the pandemic. Some of these issues include the following:

AI in the insurance industry comes as a blessing helping businesses to address such bottlenecks and improve the profitability of their offerings. XtractEdge Commercial Insurance enables commercial insurers to improve Underwriter productivity and response time by offering a complete view of the right information across the New Business and Underwriting lifecycle.

With Document AI solutions like XtractEdge, insurers can witness: