June 2022

How do commercial insurers develop a digital-first strategy

The insurance industry is highly disrupted following technological innovation, new entrants, and changing customer expectations regarding digital services. In order for providers to adapt to the new change, they need to embrace a digital-first strategy successfully. This embracement should extend beyond developing insurance products and deciding pricing models to altering distribution channels and revolutionizing customer services. In addition, the latest technology solutions like AI insurance make the claims process more efficient and drive cost savings.

Even though the 2020 Bain & Company report suggests the insurance market is ripe for digital transformation, AI insurance is relatively a small market as compared to other industries.¹ . Nevertheless, experts hope the same market will increase momentum and garner a revenue of $99bn by 2025.

AI in commercial insurance might be a challenge for many. However, only those companies who leverage the challenges to rethink and strategize their operations will benefit the most from digitalization in insurance, from underwriting and customer service to claims management. Despite the challenges underwriters face, AI in insurance has intersected the commercial space by automating processes for speed and efficiency, expanding the capacity for data ingestion and processing, providing data insights and intelligence, and improving risk modeling.

The potential of AI in commercial insurance

Commercial insurance processes involve a lot of paperwork and require a pool of customer data. AI in insurance can quickly address the labor and time-intensive factors involved when processing the requirements mentioned above for customers and ensure coverage on time.

When AI in commercial insurance is deployed, it can help transform all of these processes and benefit the providers in the following ways:

How do commercial insurers develop a digital-first strategy?

For commercial insurers to develop a digital-first strategy, they need to embrace digitization in all key areas, from developing insurance products and their pricing models to altering distribution channels and revolutionizing customer services. This will make claim processes more efficient and cost-effective.

Experts say that AI insurance in the commercial sector can boost revenues by up to 28%, reduce claims payouts by as much as 19%, and cut policy administration costs by as much as 72% within five years of embracing digitalization.

And to do that, commercial insurers need to shift their focus from simply selling protection to understanding what problem they’re looking to solve for a customer. The main challenge lies in the fact that those making decisions about product design are typically former underwriters or product managers, not digital experience or design owners.

There are a few steps to follow:

AI insurance creates price elasticity

As digital becomes more of the mainstream way, people can get a quote from ten different companies pretty easily on their mobile devices, particularly with auto insurance. Following a digital-first approach with AI in commercial insurance, pricing changes will be more market-based or an integrated hybrid, depending on the insurance line. With AI, the internal data and competitors’ actions are considered when deciding the pricing for claims.

Insurance becoming embedded in the distribution

Large-scale commercials are still an intermediate experience, and digital plays more of a back-office role, making the intermediary more efficient. In order for the digital-first strategy to be wholly embraced by the commercial insurance sector, experts believe digital has to evolve to where insurance is embedded into the distribution of things such as cars, homes, wearables, and smart speakers. It needs to be API-enabled further away from where insurance has traditionally been distributed or derived.

Embracing an adaptable customer service approach

When it comes to innovating around customer service, AI in insurance should help providers focus more on designed services while keeping customer preferences in mind. I Insurers must be adaptable to have a service model that accommodates how customers want to interact with them. And customer preferences are changeable. Carriers have to learn to listen to their customers and get involved in observing customers’ behavior. Assessing customer behavior manually is again a labor-time-intensive task. Insurance AI can address that provided the data are available in a digitized format.

Disrupting the claims process

There is a huge opportunity to reinvent the claims process. Claims are the single most extensive cost-saving opportunity areas across the insurance industry, be it commercial or personal, especially when it comes to going digital-first. This is where innovation through AI and Machine Learning can have some of the most significant impacts. High satisfaction in claims processing is about being fast and effortless, not necessarily in-person. With the help of AI in insurance, alongside other capabilities like robotics, providers can drive a 5% loss adjustment expense (LAE) ratio without dropping quality.

The need for privacy: Why some data must be kept private, and how Document AI can help

Business documents, be it PDF files or emails, images or videos, carry crucial insights about the company, its operations, finances, clients, and even customer data. Data by itself is a treasure chest for businesses and cybercriminals. Many companies buy customer data to expand their market reach. And the same data is often leveraged by miscreants for unlawful practices. Imagine if your competitor lands up with your company’s confidential data? Or are criminals gaining access to your financial information? Data theft is a reality, and businesses are finding ways to use technology to protect their company and customer information.

This is where Document AI comes to the rescue. It uses NLP and ML technologies to help businesses unearth hidden value from bulk documents and ensures the data is secured and safe.

What is Document AI, and how does it help businesses?

Document AI is a solution powered by ML, NLP, OCR, Computer Vision, Sentiment Analysis, Text Processing, and other capabilities to process the colossal volume of documents and extract and digitize data faster and more efficiently than humans.

It is designed to broadly cover three basic requirements of businesses, such as:

Most enterprises still work with paper documents. Paper-intensive industries like insurance, banking, and healthcare often struggle to maintain a proper record of each document; hence, the data safety question remains largely unanswered. Such documents are easily lost or misplaced, along with all the crucial data they carry. But Document AI can save loss or misplaced data by digitizing documents and protecting them from such vulnerabilities.

A typical Document AI processing or intelligence platform does the following tasks:

AI document processing solutions like XtractEdge by EdgeVerve help scale and process millions of documents across the length and breadth of an enterprise, enabling faster and better decision-making.

Such an enterprise-ready platform enables:

How does Document AI keep business data private and secure?

In a world of increasing digital connectivity, businesses and customers leave behind digital footprints or crucial personal information. Once AI has structured that data, all that information is suddenly readily indexed. However, data in an unstructured format would be of lesser value, and finding anything specific amidst a colossal volume of data would be very much like finding a needle in a haystack.

The main risk lies in the fact that AI needs data to thrive. Without inputs, an AI-powered solution will not perform at all. And there lies the potential for data theft, which can cost millions of dollars in loss for businesses. If that data includes clients or customers’ personal information, then data theft can lead to legal actions against the company that suffered a data breach.

Machines can connect the dots and disclose sensitive data that they are not supposed to know. Differential privacy – where patterns of groups are revealed without sharing individuals’ information- – is the answer to this problem.

AI for documents can help deal with broader data privacy issues by training models to understand what information is and isn’t private, then redact resources proactively, depending on who’s accessing them at a given time. Document AI can edit critical information from text, images videos – think names, locations, license plate numbers, faces, and other personal identifiers. Also, certain information could be made available only to specific departments with permission from the user, for example. At the same time, business-sensitive data could be limited to those who have signed non-disclosure agreements.

In a nutshell, a Document AI solution like XtractEdge provides a robust framework helping businesses to process bulk documents at a go without the fear of losing crucial insights to outside parties. It ensures documents stay compliant with industry regulations and accelerates the extracting process to drive effective business outcomes with improved productivity.

Evaluating the importance of a supply chain network

A supply chain network ensures a steady flow of raw materials, semi-finished or finished goods upstream, while overlooking the supply of final goods downstream to end-users via distributors and retailers. The overall profitability of a business depends mainly on how this network performs, ending with the actual delivery of goods/services.

What is a supply chain network, and how does it work?

When multiple supply chains are connected together in an intricate network overseeing the movement of materials, information, and goods and assessing the policies and factors impacting an essential supply chain, the whole arrangement is defined as a supply chain network.

Eventually, the ultimate purpose of a supply chain network is to drive value to end-users and ensure their demands are met with adequate and timely supply.

In the era of digital transformation, the same network has become more complex and widespread to source raw materials or serve the market beyond one’s geographical location. The global supply chain is a new reality that allows businesses to spread their risks evenly across a more comprehensive network.

For instance, the pandemic was a real eye-opener that demonstrated the inefficiencies in global supply chains, especially when companies failed to keep the shelves stocked up amidst panic-buying.

A typical supply chain network of any given organization comprises an upstream network of suppliers and vendors and a downstream chain of distributors, logistics, and retailers. Information flow from end-users, suppliers, distributors, and others in the network to preceding organizations. The flow of information in the near real-time directly impacts the performance of the above two networks. It includes data regarding past sales, current demands, customer preferences, and data related to external factors like the economy, a shift in weather patterns, seasonal elements, events and promotions, and others impacting the current demand for goods and services.

Evaluating the importance of a supply chain network

A supply chain network is based on two models, namely:

The push model: This is a marketing-oriented model commencing after the demand for specific goods and services arises in the market. For example, hand sanitizers and face masks during the pandemic.

The pull model: This is a customer-oriented model, and production begins immediately after an order is placed for specific items. For example, the restaurant industry mostly follows the pull model.

What is supply chain network optimization, and how does technology help?

A supply chain is a web of workflows, often subjected to disruptions by multiple variables. Hence, supply chain network optimization is an all-encompassing endeavor. It gives a comprehensive overview of the company’s technology, tools, and resources to ensure customers get what they want, where, and when.

With the use of new-age tech-based solutions powered by Automation and AI, businesses can optimize every aspect of their network when responding to demand signals swiftly to minimize lost sales.

This is where TradeEdge Network, a proven multi-enterprise, over-the-top platform, comes in. It creates opportunities for swift action by connecting businesses in a peer-to-peer network that can respond quickly to products, services, and information needs. That way, businesses can stay competitive and maintain a network of the most satisfied and highly engaged customers.

The key elements to scaling your automation program

Connected automation or Intelligent Automation, is the new normal enabling businesses to fulfill their objective – fostering end-to-end automation. Even though Robotic Process Automation is just a baby step towards the same objective, it is a harbinger of new technologies. And experts are working around such capabilities to expand automation adoption in all the key business use cases; in short, building a connected enterprise.

What is connected automation?

Connected automation is an amalgamation of new-age technologies like Artificial Intelligence, Analytics, Optical Character Recognition, Intelligent Character Recognition and Process Mining to create a connected enterprise landscape. Here, end-to-end business processes are made to think, learn and adapt on their own while keeping humans in the loop.

Intelligent Automation has been deployed in many industries recently; for instance, the automobile factory can accelerate production and minimize human errors with its capabilities.

A few of its benefits are mentioned below:

The key challenges of scaling the value of automation programs

The initial attempts at deploying a few RPA bots in specific use cases did nothing more than automate recurring tasks. But, on a closer look, the size and complexity of the use cases grew two to ten times what RPA is capable of alone. It has been noted that the challenge of integration is not a trivial one. The technology that can address the “stall points” or stop signs for traditional task automation is separate and complex.

The problem arises when companies deploy tactical automation in finite cases. In the absence of a clear path for strategic expansion, spreading automation to other key areas becomes quite impossible. After a few of these solutions are in place, the energy behind the program begins to drop. The use cases get too hard to automate at important points in the process.

Here are a few barriers organization face when scaling automation:

How to scale the automation program to realize true value?

In order for businesses to realize the full value of automation programs, a few key elements could be taken into consideration. Such elements include:

Unattended automation can be very powerful indeed when it meets the right kind of work, work that does not encounter a document or voice exchange. It doesn’t need to bail out to a human for direction or to clear an exception state. However, the elements mentioned above are the must-haves to ensure companies fully leverage Intelligent Automation’s potential.

In a survey report by SSON and EdgeVerve, 39% of respondents cited that increasing the number of bots deployed can truly scale automation’s full value. On the other hand, 58% of respondents think increasing the number of processes covered can ensure complete end-to-end automation. First, however, businesses need to develop AI-based decision support to achieve connected automation.

Conclusion

Deploying a few RPA bots in limited use cases will neither demonstrate the true value nor enable end-to-end connected automation. Instead, enterprises need to extend their automation enablers to core business use cases and leverage the other capabilities effectively. Also, having a complete roadmap to map out the automation implementation is a must-have. That’s how businesses can scale the true value of automation programs and enjoy the benefits optimally.

Case study: How AssistEdge Discover scales business processes enterprise-wide

Process discovery helps enterprises map existing business processes to understand how each process works. It identifies various nuances and subtle variations in processes and recognizes the pattern in each task within which the process is rendered. Such information is crucial for businesses looking to deploy end-to-end business process automation. Process discovery identifies the suitable candidates for automation and unearths hidden opportunities that prove valuable for companies moving forward.

Manual process discovery challenges and remedies

Enterprises often face challenges pertaining to several process complexities. Maintaining vast operations across several countries with differences in local procedures, languages, and legal requirements can be pretty nerve-wracking. Process variations created by non-standardization limit visibility and significantly increase the cost of operations. In order for companies to identify such variations in existing business processes, they need a clear and granular understanding of how various users across countries approach the same process. This process mapping would be a mammoth task with an extensive scope if conducted manually. Language barriers would also severely limit the team’s capacity to identify process variations manually.

This is where process discovery plays a crucial role. It offers the following solutions:

For instance, AssistEdge Discover, AI powered solution by EdgVerve, helped a leading global beverage company with operations across 20+ countries optimize and automate two processes – cash collection and route settlement and billing processes. AssistEdge Discover helped the client identify improvement opportunities in record time, saving over a million dollars in cost and 65,000 person-hours in efforts.

Benefits of process discovery tools

Besides reducing manual intervention and eliminating unnecessary steps, process discovery provides granular visibility into the as-is processes. It identifies critical business areas requiring improvement and where automation can significantly add value. Enormous costs and effort reduction translate into accelerated revenue recognition.

Apart from the ones mentioned aforehand, process discovery tools have untold benefits for organizations looking for end-to-end business process automation. Citing the same example of the global beverage company, the key benefits are driven by AssistEdge Discover transcend into the following:

Conclusion

Process discovery intelligently addresses the time-labor and cost-intensive factors when identifying business use cases for automation. It not only ensures that suitable candidates are selected; instead, it helps enterprises find an optimized way of catering to the same tasks with speed and precision.

Automation is the foundation for digital transformation and a key driver of improving how companies initially perform. But to reach that stage, they need to identify the subtle variations existing in the legacy approach to executing various roles within its operations., Process discovery unearth hidden opportunities in business processes faster and more accurately. With the help of process discovery solutions like AssistEdge Discover, the whole endeavor can be completed at record speed without disrupting the general workflows.

Five business cases and benefits of AI in document management

Businesses have a gold mine of valuable data at their finger tips, yet many fail to optimize the total value of such insights. The legacy approach to extracting data from various documents during decision-making often leaves subtle inputs negligible to human eyes. Those granular details can make all the difference if pulled on time. Hence, resorting to new-age technologies can prove a blessing in disguise.

AI in document management caters to document extraction, processing, and comprehension and stitching crucial data together using computer vision models, NLP models, and information retrieval technology into a single data pipeline for a simple unified experience across all documents. Integrating the digitized information extracted from documents into enterprise processes and workflow ensures easy data availability and accessibility.

A data is a rich source of information: pictures, audio, infographics – even video or speech. AI document analysis does an incredible job of finding such critical information, regardless of formats and layouts, and making it available, as and when needed. It saves a lot of person-hours and labor for extracting data. It is faster, effortless, and error-free.

How does AI in document management work?

There is no doubt in the fact that we have come a long way with AI. According to a recent report from analyst firm Omdia, the global AI market was valued at $16.4 billion in 2019 and is expected to reach the $100 billion mark by 2025.

Today, AI, powered by other tech capabilities, has the ability to scan text and the basic contextual understanding of font size and spacing. It can read the text in different languages and the hidden sentiments behind those words using Sentiment Analysis. Its multi-modal image extraction combines OCR and computer vision to extract pertinent information and turn it into structured data.

Most importantly, a comprehensive AI document management system handles a massive volume of documents daily to provide a holistic interpretation of the text and images faster than any average human, without any downtime or error.

Five business cases for AI document analysis

Time saved: The sheer amount of admin hours spent extracting inputs from documents, digitizing and analyzing them reduced the overall productivity of the business, delayed workflows, and resulted in incompetent decision-making. For instance, an insurance company struggles with the management of a complex range of policies and exclusions, and so also a healthcare company with thousands of handwritten doctor’s notes. AI in document management comes as a blessing, helping employees save time and money for their company.
Making the most of your employees’ skills: With less time spent in the ‘needle in a haystack search for pertinent data, employees find more bandwidth to use their skills in more value-added roles for which they were hired initially. Having been freed from labor-time-intensive repetitive tasks, employees discovered their morale boosted, and their productivity increased.
An improved customer experience: With structured data easily searchable, decision-making is more insight-driven. Better data-based decisions and strategies transcend into increased customer service. For example, setting up a customized policy might once have taken hours to complete in an insurance company. At the same time, all the information about a customer’s unique circumstances was collated and evaluated. With their credit history, demographics, policy options, and possible exclusionary risks highlighted in a searchable database, a bespoke policy could be set up in a single phone call.
Improved document security: Data breaches are a daily occurrence. Hence, security has never been more critical. AI in document management can help with extensive scanning of sensitive information and automatically redacting it as and when required. The same systems can be on the lookout for unusual activity, warning you of a possible data breach before it happens.
Unexpected business insights: Unlike humans, AI excels with data. When millions of business documents are fed into the Artificial Intelligence document management platform, unexpected insights are extracted, processed, and analyzed in seconds.

Conclusion

AI in document management is very much a reality, and many businesses are already finding ways to leverage the potential of such unique software solutions. One of the companies at the forefront of the Document AI revolution is EdgeVerve. With XtractEdge, a comprehensive suite of Document AI platform & products, enterprises can unlock business value from enterprise data, regardless of complexity or domain specificity.

Demand sensing solution: Important elements, benefits, and key points to consider when predicting future sales

Traditional demand planning and forecasting methods fall short of accurately predicting the future. On top of everything, external forces play shape and reshape demand frequently. With the advent of global networks of suppliers and distributors, businesses are constantly challenged to timely meet the evolving demands of hyper-local customers. Driving a perfect balance between surplus and adequate inventories in the absence of quality demand signals is a challenge for many.

Experts believe granular visibility and actionable insights can only drive business growth in the face of precedented and unprecedented challenges. And it starts with demand sensing.

What is demand sensing?

Meeting immediate market demand is the key to winning more customers and staying ahead in the competition. The existing methods have been proven wrong during the global pandemic, alienating customers from brands when the latter failed to keep the shelves stocked amidst panic buying.

Enterprises today are looking to predict future demand based on current trends as closely as possible. Demand sensing is probably that missing piece of the whole puzzle. It leverages advanced technologies like Artificial Intelligence, Machine Learning, to analyze the data captured from various touchpoints to create visibility of the changing patterns in the market demand for goods and services.

Accurate forecasts eventually translate into better business decisions, improved inventory management, reduced operating costs, and in achieving critical business objectives.

How is demand sensing done and what are the key elements?

Historical data might be the ideal starting point for forecasting future sales. But old data rarely considers the current trends, which cumulate into more significant problems faced by businesses today. And one of the reasons is the increasingly shorter lifespan of products. Also, customers today are spoiled for choices, which can be cited as another reason why a robust tech-based forecasting solution is needed.

Demand sensing solution is tasked with reducing or overcoming latency issues. Anticipating short-term demand changes in the market with the help of AI-powered demand sensing solutions can overcome such problems by reducing the time between the occurrence of an event and its response.

Therefore, organizations can witness the most significant returns from demand sensing, which roughly comprises six essential parts or elements:

Sell-in data: You cannot predict the future without adequate data. Demand sensing uses daily sell-in granular data to forecast demand for shorter periods.

Sell-out data: Downstream sell-out data at the buyer’s end, PoS, or channel data can identify the sudden change in demand trends and provide timely warnings so businesses can seal the time gap between planning and executing to match demand.

Appropriate product history: Historic data is the foundation for predicting future demand. But not all past data are useful for catching demand signals. Hence, narrowing your database to a specific period is the best bet. On the other hand, too-old data or those belonging to periods that do not correlate with contemporary demands should be best avoided.

Internal trends: These trends highlight certain sales patterns of a specific product or group of products. Sales patterns of such products vary, going upwards at a certain point in the year while dipping at other times. For instance, demand for winter garments remains high during the fall and early winter. However, their demand drops during the spring and summer months. Such seasonal variations and sales patterns are valuable insights for predicting future sales.

External data and demand casuals: External elements can seriously impact market demand, most of which are quite unpredictable in nature. A demand sensing solution offers integrated demand-correlated variables to create forecasts that can very well respond to a wide range of future events, both known and unknown. So, be it stock market fluctuations or weather shifts, competitors’ promotions or viral social media trends, new product introductions, and other external factors, your forecasting can keep your business future-ready always.

Events and promotions: Various events and promotions also substantially impact the future demand for goods and services. They can increase sales of specific products or introduce new items altogether. Demand sensing can help businesses stay prepared with adequate stocks to meet the sudden surge in demand for certain items triggered by events and promotions.

Benefits of demand sensing

TradeEdge Demand Sensing Solution

TradeEdge Demand Sensing solution by EdgeVerve provides granular insights to help businesses with actionable decision-making pertaining to predicting future demands and driving business growth at scale.

In a nutshell, the demand sensing solution helps enterprises overcome the barriers to growth, from lack of visibility to consumer offtake to enhancing forecast accuracy. Since product lifecycles are getting smaller and consumer preferences are changing faster, old legacy approaches to predicting future sales no longer meet the eye. Hence, an intelligent solution is needed, and demand sensing is the one.

AI in global supply chain: An intelligent way to transform how businesses predict future demand

Even though humans might have to wait for another decade before AI technology reaches full maturity, it couldn’t have emerged at a more crucial time when the global supply chain was trying to cope with the aftermath of the pandemic. Following the introduction of AI to the worldwide supply chain network, nearly 61% of businesses reportedly witnessed a massive decline in costs connected to the supply-demand chain, wherein 53% of others saw a spike in revenue, as per reports.

Leveraging AI technologies in the global supply chain enables end-to-end visibility and transparency, which is imperative when game-changing decisions are needed to impact the supplier-distributor-retailer relationships with the business and its end-users.

What is a global supply chain, and how does it benefit from AI?

The global supply chain is an intricate web of different players, each enacting a significant role. Existing silos in the network can deter the smooth flow of information from one player to another, thereby hindering adequate decision-making pertaining to improved customer services. This results in the loss of customers, loss of business, and poor profit for all. AI in the global supply chain breaks those silos and improves visibility, enabling better decision-making and building resilience in the face of global crises.

Global supply chain issues and challenges

According to experts, global supply chains confront three critical challenges: labor shortages, equipment unavailability, and global bottlenecks causing a rippling effect. Some of the challenges are:

Black swan events: These events are unpredictable, and hence, any preparations for them aforehand are nearly impossible. The pandemic, for instance, is a black swan event. Such occurrences in the global supply chain are a constant reminder of why companies need smarter technology to help them solidify their immunity in the face of precedented and unprecedented challenges.

Material scarcity: The pandemic of 2020 was an eye-opening event, exposing the shortcomings of existing forecasting and demand planning methods. With the international borders shut down indefinitely, companies grappled to source essential raw materials and other parts from the global supplier network.

Port congestion: Even though the demand for goods remains high, port capacity remains fixed. Freight ships have to wait in the queue to unload their cargo once they reach their destination. But safety protocols of the pandemic increased the waiting time indefinitely, resulting in item shortages at the retail outlets.

Traditional demand planning techniques: Existing demand planning techniques proved inadequate in the face of the global pandemic. Hence, most companies couldn’t predict how much to stock or manufacture to meet the demand at any given time. The result: empty shelves in the supermarkets and essential items going out of stock following mass panic buying.

Such bottlenecks can be overcome with the help of intelligent technological solutions. Hence, AI in the global supply chain is gradually becoming the need of the hour.

How does AI in the global supply chain work?

AI supply chain solutions optimize the pool of data existing within operations and beyond in the global supply chain, including broader economic indicators, to foster autonomous planning and make predictive analysis based on historical data as well. A smart combination of predictive AI and human oversight can transform the supply chain and daily distribution operations.

AI in the global supply chain helps with:

AI is currently the best bet for businesses working with an extensive network of suppliers and distributors spread across the world. And AI supply chain solutions are the only antidote to successfully overriding global supply chain disruptions.

Evaluating the role of AI data analytics in the insurance value chain

Like other industries, the insurance sector has witnessed the impact of AI first-hand. Notably, in commercial insurance, AI has automated processes, driving speed and efficiency and expanding data ingestion and processing capacity.

New tech capabilities like Artificial Intelligence, Machine Learning, Deep Learning, and computing are increasingly deployed to enhance decision-making and productivity, lower costs, and optimize the customer experience. These tech-based solutions are changing the data analytics landscape in significant ways. AI data analytics provide greater depth and assurance in the analysis and predictions.

According to research, the insurance sector is gradually shifting from its current state of ‘detect and repair’ to ‘predict and prevent’ with the help of big data analytics and other tech-based solutions as it approaches 2030.¹

Evaluating the role of AI data analytics in the insurance value chain

When it comes to AI data analytics capabilities, there are a plethora of options to explore, especially for insurance providers. Across the insurance landscape, more accurate and detailed models are trained on diverse datasets to derive in-depth analytics used for predictions. Further, a new set of big analytics tools are applied to extract new types of data that were never available to the insurance businesses before.

For example, live driver feedback related to automotive insurance helps with better fraud detection at a much larger scale and based on subtle patterns. Then, there are live data collections via mobile apps, along with the more traditional statistical models considering larger and more complex data sources, Natural Language Processing for text mining, and many more.

Taking the same automotive insurance as an example, insurance providers are better positioned to ascertain the punctual and dynamic riskiness of who drives what, how, and in which context. With the help of big data analytics, the former can improve risk segmentation and selection and help make the world more resilient.

Insurance providers expect AI data analytics to help them anticipate risks in a timely way to boost risk predictions and enhance risk assessment. Quantifying big data analytics is pivotal in drawing useful meaning from those extracted insights. Insurance providers use such insights across insurance types and risk profiles to understand the landscape and make smarter choices for entering into profitable endeavors.

Challenges and benefits of AI data analytics for insurance providers

New technologies like AI and ML thrive in the presence of extensive, structured data and are helpful for applications that work with unstructured data.

However, insurers also face challenges when implementing AI into their use cases like any other business. One of them is the ethical challenge. Secondly, AI systems are prone to introducing bias in decision-making. The only way to get rid of bias is by making these systems transparent to allow for better scrutiny and address any bias present in the processes.

As mentioned earlier, AI systems require large amounts of high-quality, relevant data. And the insurance sector works with pools of data that should be treated with care, and with the help of AI data analytics, insights can be drawn out without so much as human bias discoloring the outcomes.

Once the initial challenges are addressed, AI data analytics can help insurance providers in multiple ways.

Conclusion

The insurance industry can experience the untold benefits of using AI data analytics to serve customers better and gain an edge in the market over other players, a few of which have been elucidated before. Experimenting with new-age technologies in the insurance sector is still on, and businesses are finding different ways to leverage them to handle more complex processes. And this is where AI data analytics plays a significant role.

A comprehensive guide on the global supply chain: Examples, benefits, challenges, and solution

What is the global supply chain?

The global supply chain refers to the worldwide network businesses use to produce goods and services to meet the demand of local and overseas customers. This complex web is subjected to frequent disruptions and changes following precedented and unprecedented challenges, like the pandemic of 2020, for instance. Companies working closely with a global supply chain network couldn’t agree more – the issues are real and more intricate than handling a linear supply chain close to one’s location.

Global supply chain examples: food and beverage, mining, oil and gas, electronics, consumer goods, and textile are a few sectors that thrive with a global network of supply chains.

Current supply chain challenges faced by organizations:

How does a global supply chain strategy benefit businesses?

Having a more comprehensive network of suppliers and distributors can benefit businesses in various ways. One of them is the privilege of spreading the risk and immune your enterprise from plausible disruptions, which are primarily unprecedented in nature, like natural disasters.

Other advantages of working with a worldwide network include:

How to overcome the global supply chain crisis?

Many businesses have ramped up their investments in AI solutions to override the current crisis and transform the value chain. For instance, TradeEdge Network, a proven multi-enterprise, over-the-top platform by EdgeVerve, connects businesses for enhanced visibility and resilience for their product, services, and information needs.

Benefits of leveraging TradeEdge Network:

Connecting businesses beyond their direct customers and suppliers to fill demand-supply gaps in real-time to maximize fulfillment