Providing new insights into various industries, Big Data is no longer confined to just being a technology and rather is becoming a business enabler. The insights gleaned from Big Data are playing an integral part in providing solutions to the longstanding challenges of the insurance industry.

1. Insurance Big Data & Analytics
2. Major areas where Big Data and analytics can make a huge difference
3. The Bottom Line

Insurance Big Data & Analytics

Insurance firms undergo a process of evaluating risks of insuring a person and consequently setting up the premium for the policy. They hold great quantities of data that helps them get acquainted with the existing customers and reach new customers as well. While the industry has made immense progress by analyzing and capturing structured data, the unstructured and untapped data like social media, real-time news feeds, and other channels etc. are also considered as valuable.

In order to succeed in the ever-changing and competitive insurance industry, it’s a no-brainer that insurers must leverage Big Data and analytics. Algorithms based on Big Data can help insurance companies to meet compliance changes, minimize losses and reduce costs.

Five major areas where Big Data and analytics can make a huge difference:

1. Fraud Detection: One of the significant reasons why insurers go through each and every page of the data is to detect fraudulent claims. This process is not only carried out when the claims are registered but also at the onset and at the revival of the lapsed policies. The overburdened job of the insurers makes it difficult for them to manually gather information and sort each one of them. Predictive analysis through big data can help insurers spot the fraudulent material quickly. Methodologies in data analytics like database searches, modeling, text mining & exception reporting can be used in order to identify fraudulent claims.

2. Proper Fund Balancing: There are huge funds maintained by the insurers against potential claims. It is nearly impossible to predict the size of claims or forecast when a claim will arise. Optimization of the reserve fund is, therefore, a huge task that entails a relentless assessment of risk. Insurers, though, spend a decent amount of time in these tasks, predictive and trending analytic tools can release the pressure of this herculean task from the insurers, helping them make correct judgments about optimization and fund balancing.

3. Customer Segmentation: Calculations of the premiums are based on the claim experience of the group of customers. Usually, customers are grouped by the similar age, sex and the term of insurance. Within a particular group, it is possible that customers are present with varying levels of risk. If there is same premium for the whole group, it is possible that customers with low-risk compensate for customers with high-risk. Big data analysis, therefore, can help insurers group their customers better and calculate customized risk-based premiums.

4. Underwriting: Underwriting insurance policies is a huge task for the insurers as it requires assessing a large amount of data. Obtaining the data in the digital form from the proposer can eliminate human errors and also provide greater authenticity in the data. Analysis and processing of the data can also be automated using BI tools and presented in a suitable format to the underwriter. This process significantly accelerates the process of underwriting, further making it error free.

5. Settlement: Complex claims are the ones that take most of the time and it makes sense in putting the most experienced adjuster on that claim. Usually, claims are settled based on various factors such as age, sex, history of the applicant, duration of the insurance etc. Analyzing all factors associated with the insurance and providing claim to the customer becomes a humongous task for the adjuster. Settlement needs a fast-track process that can settle the claims quickly by analyzing the claim histories. Using data analytics, insurers can fast-track the process of claims and garner superior customer satisfaction.

Interesting #blog: 5 Key Areas in the #Insurance Industry where #BigData can make a huge Impact https://t.co/q7AqSWG79L

— TestingXperts (@TestingXperts) September 6, 2016

The Bottom Line

Even after providing major impressions in the world of finance, healthcare, and marketing, there has been a slow progress in integrating big data analysis into the insurance industry. Despite the benefits and significance of data analytics, there are hindrances following the adoption of big data by the insurance firms. One of the key challenges insurers face in adoption of big data and analytics is testing and validation of the systems being implemented.

TestingXperts promises to support global insurers with the challenges faced by them in implementation of big data and analytics initiatives. Being a specialist QA and software testing firm, we have specialist approach and tools to provide effective and automated testing solutions for big data and analytics. Get in touch with one of our test advisors here.

The post 5 Ways Big Data Analytics Impacts Insurance Industry first appeared on TestingXperts.

1. What is Big Data Testing?
2. Why is Big Data Testing Important?
3. Challenges in Big Data Testing
4. Impact of Big Data Testing on Business Decision-making
5. Summary
6. Why Choose Tx for Big Data Testing?

Data is one of businesses’ most valuable assets, and it’s practically impossible for them to remain profitable and competitive without proper data analysis methods. Big data testing is the center of connecting data with business assets, an indispensable practice for businesses that want to leverage a vast ocean of data. In 2023 alone, the big data analytics market was $307.53 billion, projected to reach $745.15 billion by 2030. Around 97% of organizations worldwide also focus on big data and AI, as these technologies are crucial to their processes and growth.  

Despite this, around 73% of data goes unused for analytics, which signifies big data in resource utilization. Poor data quality costs the United States around $3.1 trillion annually, clearly stating the need for refined data handling and analysis. Big data is vast and complex, and the process of being a data-driven organization is filled with challenges like technical constraints, budget issues, and being uncomfortable with cultural shifts. To handle all of such issues, big data testing can enable businesses to handle the complexities of data-driven decision-making. By using testing and analysis, organizations can upgrade to new levels of efficiency, customer insights, and competitiveness. 

What is Big Data Testing?

Before starting with big data testing, let us understand big data. It refers to the large collection of data sets (structured and unstructured) collected from multiple sources. It’s difficult to process these data sets manually, so businesses require multiple databases and tools to assist with the evaluation process. Over the last decade, the volume of data has grown exponentially because of data-driven and intelligent tools like IoT devices and AI systems. Experts say that we create around 2.5 quintillion bytes of data daily. One requires a robust big data testing solution to manage and ensure the data’s security, quality, and relevancy. 

Big data testing focuses on verifying the integrity and quality of big data before businesses use it for decision-making. This is important because big data consists of large volumes from multiple sources and in varying formats. Big data testing ensures the data is reliable, complete, and accurate. One of its key aspects is data processing validation, which involves checking data once it is collected and processed accurately. As processing data volumes is complex and difficult, testing ensures that the processing algorithms work in sync to generate data output as expected.  

Another important aspect is performance testing to check whether big data applications are processing large data volumes efficiently and quickly. This ensures the systems can handle data load and perform efficiently under varying conditions. It involves testing the systems’ scalability and speed. Data quality and security testing are also significant in big data testing. Data quality involves checking data consistency, reliability, and accuracy. As data quality issues negatively impact business decisions, identifying and correcting data inaccuracies becomes crucial. Also, because of processing vast amounts of sensitive information, it is important to ensure the security of big data systems. Security testing ensures proper data encryption, access controls, and compliance with data protection regulations. 

Why is Big Data Testing Important? 

Big data testing is critical for business success in the digital business environment, where data decides decisions. It is necessary to ensure data security, reliability, and quality, refining it into a trustworthy asset for decision-making. Businesses heavily rely on data insights for growth and seamless operations, and big data testing helps maintain the integrity and value of the information used. Following are some of the factors that show why big data testing is important for businesses: 

• To ensure data accuracy for reliable business decisions. 

• Ensure data consistency and validity from multiple sources. 

• Maintaining efficient data processing and scalability. 

• Protect sensitive data and ensure compliance with data protection laws. 

• Enable businesses to adhere to data regulations and avoid legal issues. 

• Facilitate informed and data-driven business strategies. 

• Prevent costly errors and enhance operational efficiencies. 

• Better understanding and service of customer requirements. 

• Gain a competitive edge through data-driven insights. 

Challenges in Big Data Testing 

In the domain of big data, where data volume, velocity, variety, and veracity reach unprecedented levels, testing presents distinct and formidable hurdles. Effectively addressing these challenges is crucial to guarantee the dependability, precision, and efficiency of big data systems. Let’s explore the nuances of big data testing and uncover the obstacles that testers confront in this dynamic environment. 

Data Volume Overload:

Big Data systems deal with massive volumes of data, often spanning terabytes or even petabytes. Testing such colossal datasets requires specialized tools, infrastructure, and strategies to simulate real-world scenarios effectively. 

Data Variety Complexity:

Big Data encompasses diverse data types, including structured, semi-structured, and unstructured data from various sources such as social media, IoT devices, and sensor networks. Testing the integration, transformation, and processing of this heterogeneous data poses significant challenges. 

Data Velocity Dynamics:

The speed at which data flows into big data systems, known as velocity, can be staggering. Testing real-time data ingestion, streaming analytics, and near-instantaneous processing capabilities requires advanced testing methodologies and tools capable of handling high-speed data streams. 

Data Veracity Ambiguity:

Veracity refers to the accuracy, reliability, and trustworthiness of data. Big Data often grapples with data inconsistencies, errors, and uncertainties, stemming from disparate sources and data quality issues. Testing the veracity of data involves detecting and mitigating anomalies, ensuring data integrity and reliability. 

Infrastructure Scalability:

Big Data systems typically operate on distributed computing frameworks like Hadoop and Spark, leveraging clusters of interconnected nodes. Testing the scalability and elasticity of these infrastructures to handle increasing data volumes and user loads is crucial for maintaining optimal performance and responsiveness. 

Complex Data Processing Algorithms:

Big Data analytics rely on sophisticated algorithms for data processing, transformation, and analysis. Testing the accuracy, efficiency, and scalability of these algorithms across diverse datasets and use cases requires robust testing frameworks and domain expertise. 

Resource Constraints and Costs:

Testing big data systems often entails substantial resource requirements, including computer resources, storage, and network bandwidth. Managing these resources efficiently while minimizing costs poses a significant challenge for testing teams. 

Regulatory Compliance and Security:

Big Data systems must adhere to regulatory compliance standards such as GDPR, HIPAA, and PCI-DSS, safeguarding sensitive data and ensuring privacy and security. Testing for compliance, data protection mechanisms, and vulnerability assessments is essential but complex. 

Navigating these challenges requires a holistic approach to big data testing, encompassing advanced testing methodologies, automation, collaboration between development and testing teams, and continuous learning and adaptation. By addressing these challenges head-on, organizations can unlock the full potential of big data and drive innovation, insights, and business value. 

How does Big Data Testing Work? 

Big data testing is a strategic approach containing a sequence of testing techniques addressing specific components of the big data environment. It is a critical process that involves various techniques and steps to facilitate reliable, secure, and accurate data sets for better decision-making. Let’s take a look at the key phases involved in effective big data testing: 

Data Validation:

This phase verifies data accuracy and completeness before loading it into the system. It checks data at the source, during transfer, and when it lands at the destination, i.e., database or warehouse.  

Data Quality Testing:

After data validation comes the data quality testing. It involves checking for data consistency, reliability, and accuracy to meet the expected standards and formats, making it suitable for business decision-making and analysis. 

Performance Testing:

Big data systems process and analyze large datasets continuously. Performance testing evaluates the system’s scalability and speed, ensuring it handles the data loads within the defined timeframes. 

Process Validation Testing:

QA teams verify the algorithm or logic that processes the data in this step. This ensures data transformation, aggregation, and summarization accuracy and efficiency to deliver the expected outcomes. 

Output Validation Testing:

The next step is to validate the output data to ensure the system’s output is accurate and in accordance with the expected results. The process involves comparing output with source data to ensure consistency. 

Security Testing:

Big data consists of sensitive information that needs proper security measures. Security testing involves testing a system’s security measures to protect data from cyber threats and unauthorized access. 

Integration Testing:

Data flows through multiple systems in the big data ecosystem. Integration testing ensures different systems work in sync seamlessly and data integrity is maintained throughout the process. 

User Acceptance Testing:

The final step in big data testing where end users validate the data and system. User acceptance testing ensures the system meets the requirements and is ready to deploy. 

Impact of Big Data Testing on Business Decision-making 

Big data testing is important for businesses to ensure the quality and accuracy of the data used for informed decision-making. Organizations can easily identify and address inconsistencies and errors faster by testing their data, facilitating better-informed decisions, and improving efficiency. Following are some of the factors that define how it impacts business decision-making: 

Improved Data Accuracy:

One of the primary benefits of big data testing is that it allows businesses to avoid costly errors. Businesses can make better decisions by identifying and rectifying inconsistencies and flaws in inaccurate or deceptive information. Businesses can save time, resources, and money on processes that won’t sync or work to produce the necessary results. 

Risk Reduction:

Big data testing mitigates risks associated with data-driven decisions by identifying inconsistencies and inaccuracies in data. This data-handling approach reduces risks associated with costly mistakes due to data bugs, thus ensuring secure and streamlined business operations. 

Business Model Optimization:

Every organization wants to profit from big data, and thanks to tech innovations, it has become much easier to collect massive amounts of data from online and offline sources. Businesses can use this data to personalize the customer experience, implement predictive models to analyze behavior and improve customer loyalty programs. However, to do so, businesses must validate the big data before using it.  

Regulatory Compliance:

It ensures that businesses comply with data privacy and protection laws. This is highly important as organizations work under a complex web of global and regional data laws, and not adhering to them could result in legal penalties and damage to brand reputation. 

Insightful Decision-making:

Businesses can easily distinguish between helpful and worthless data using big data testing. Unnecessary data can impact business choices, resulting in losses. By validating big data and implementing big data testing methodologies, businesses can improve their decision-making process efficiency and assist them in making better judgments. 

Cost Management:

It enables organizations to prevent high costs associated with buggy data. Businesses can prevent costly mistakes by ensuring data accuracy, like investing in wrong assets or allocating resources based on incorrect data analysis.  

Summary 

Big data testing is a necessary business requirement in today’s data-driven environment. It ensures data accuracy, reliability, and security for effective decision-making. The process involves data validation, quality testing, performance testing, and security assessment, vital for ensuring data integrity. It also facilitates compliance with data regulations and enhances operational efficiency and strategic planning. By continuously adapting testing methodologies to a dynamic big data ecosystem, businesses can unlock the true potential of data-driven insights for effective decision-making. 

Why Choose Tx for Big Data Testing? 

Tx has extensive experience in analytics testing, big data engagements, and addressing unique challenges of big data analytics testing. We ensure our big data testing solution is adequately automated and scalable to meet your business needs. Our testing approach will give you the following benefits: 

• Performance/security testing for extensive test validation and coverage. 

• Audit/data quality report for thoroughly validating data quality in big data systems. 

• Customized approach to ensure data accuracy at various phases of big data processing. 

• Partnership with QuerySurge and other tools to automate the verification process. 

• Highly skilled professionals in big data for designing test strategies and project execution. 

• Compare data from source files and data stores to the target big data store. 

• We possess an in-house tool capable of automating the entire validation process, spanning from the backend data sources to the frontend endpoints, whether it be a dashboard, database, or frontend application. 

To know more, contact our QA experts now.

The post How does Big Data Testing Optimize Business Operations? first appeared on TestingXperts.

Contents

1.  4 major benefits of software testing in the healthcare industry
2.  Testing Healthcare Applications Challenges
3. How TestingXperts Can Help!?

Digitalization, being one of the major revolutions seen by this industry, has been helping experts to deliver their services even from a distance. The growing number of healthcare products, such as wearables and the applications associated with them, have created a huge market and had a remarkable effect on the economy.

In fact, over the course of relatively few years, the healthcare and insurance markets have seen rapid, fundamental changes. Some of the changes have been listed below. Thus, healthcare products have the prerequisite to provide precision and accuracy, which has created the need for an extensive healthcare application testing. Software testing is a concept that covers all aspects that can individually impact the quality of the product.

4 major benefits of software testing in the healthcare industry:

Application Security

The sensitive data of patients, including their health info, requires a lot of protection. Healthcare application, if not given proper security, can lead to major breaches. In order to make the application secure and fail-safe, security testing is imperative. It helps in making the application sustainable and foolproof in all sorts of challenging situations.

Effective Execution and Expertise

In order to serve the patients better and effectively, expertise and efficiency are much needed in healthcare systems. Seeing the complex nature of the healthcare applications, it is important to ensure that they function smoothly without any disruptions. Software testing ensures that the application works seamlessly, providing a comprehensive experience to the users.

Big Data Handling

Healthcare industry comprises of a huge amount of data, containing extensive patient information and their respective health conditions. This data holds high importance to the healthcare institutions, as it helps in developing the right strategy and producing relevant products. Big data solutions help in providing informed decisions involving disease cure, research and development, and much more. It is imperative that this data gets tested for a flawless implementation – leading to expected outcomes.

Usability Testing

Usability Testing is crucial to building various user scenarios and features that are faced by the doctors and other staff during their working hours. In usability testing, the same instances are emulated and checked in real-time as well as virtually. These days, automation has made this task easier for testers as it simplifies the whole process. Usability testing also helps in enhancing the user interface and overall experience of the healthcare applications.

However, the new monitoring tools and approaches are posing unexpected challenges and making it more and more difficult to test the healthcare applications. Given below are some challenges that are regularly faced while testing healthcare applications.

Testing Healthcare Applications Challenges

1. Domain and System Understanding

Testers should have appropriate knowledge of the functionality, the clinical usage and the environment in which the software will be used while testing healthcare applications.

2. Security and Vulnerabilities

The healthcare application requires proper security to keep the patient’s records and other confidential data safe. The health app also contains financial data which needs proper security. Testers need to keep the data safe and secure to prevent any hack.

3. Cross Dependency of Software

Complex software has various layers and components. Any change in one component or layer can lead to cause side effects on the other. Testers must ensure that there are no side effects on the other layers whenever any change is made.

Testing in the healthcare industry requires agility and more experienced testers. They must be rigorous and experienced in regards to data integrity, data scrubbing and database queries. In addition, they must be able to leverage automation to generate data and AI to alleviate the risks associated with testing in the healthcare industry.

How TestingXperts Can Help!?

The healthcare industry has entered the dynamic era of new regulations and therapies that require a change in the line of work. TestingXperts, with its excellent domain knowledge, assures clients of one-stop testing services. Our strong QA and test automation capabilities, ideal for testing their enterprise applications and technology infrastructure, is helping our clients in producing world-class health applications.

Our in-house test labs have the necessary talent and infrastructure capable of performing multifaceted and complex testing activities, including R&D software testing, healthcare billing software testing and necessary IT improvements for clinical systems. We are carrying out global healthcare software testing, supporting regulatory requirements and reducing testing costs across the world.

The post 4 Major Benefits of Software Testing in the Healthcare Industry first appeared on TestingXperts.

1. Big Data Analytics in Healthcare
2. 7 key benefits of using Big Data Analytics in Healthcare
3. Improves patient healthcare
4. Predicts patients at higher risk quickly & efficiently
5. Eases patient diagnostics with EHRs
6. Ensures to reduce overall healthcare costs
7. Delivers greater insights into patient cohorts
8. Enables improved healthcare with fitness devices
9. Generates real-time alerting
10. Conclusion

In today’s world, it is a fact that digitization has been adopted across industries such as telecom, banking, pharmaceuticals, healthcare, FinTech, EdTech, etc. The adaptation of digitization of healthcare has more significance than other sectors as it contains the medical data related to patients, their diagnosis, treatment schedules, imaging results etc. which generates huge volumes of data which has been collectively named as big data.

Big Data Analytics in Healthcare

Typically, this big data refers to the abundant health data amassed from numerous ways of electronic health records (EHRs), medical imaging, genomic sequencing, X-rays, and diagnostic reports, etc.

This statistical analysis or the big data analytics helps to predict hidden information and this healthcare analytics has the potential to deliver numerous advantages to patients.

This big data analytics in turn helps to improve patient care and also improve the quality of life in an effective way. Some of the major sources of big data for healthcare include generic databases, wearable devices, EHRs, smart phones data, various patient portals, various fitness devices, to name some. However, it is evident to note that with the diversity of this healthcare data due to its formats, types, and context, it is really difficult to merge into conventional databases.

Big data Analytics Stats

According to Allied Market Research, Big data analytics in healthcare is the complex process of examining big data to discover information including hidden patterns, market trends, unknown correlations, and customer preferences, which can help organizations to make informed clinical and business decisions.

The global big data analytics in healthcare market size was valued at $16.87 billion in 2017, and is projected to reach $67.82 billion by 2025, growing at a CAGR of 19.1% from 2018 to 2025.

Hence, by using new technological improvements like Big data analytics, the enormous volumes of big data in healthcare are converted into useful, and actionable information that continues to deliver great insights to doctors, healthcare providers and patients in an effective way. The usage of big data analytics in healthcare brings about lot of positive and life-saving patient outcomes. Leveraging big data analytics in healthcare brings in many more benefits which have been detailed below.

7 key benefits of using Big Data Analytics in Healthcare

1. Improves patient healthcare:

The significant benefit from the knowledge derived from big data analytics delivers greater clinical insights to various healthcare providers. These cutting-edge analytics improves patient care in the healthcare system as this data facilitates doctors to prescribe effective treatment and make clinical decisions more accurate further eliminating any ambiguity involved in the treatment.

Big data analytics seems to bring in a change in healthcare which is moving towards bringing in better patient outcomes as the data is used to discover which practices are most effective for patients.

2. Predicts patients at higher risk quickly & efficiently:

While considering the population-wide data for a particular area, specifically predictive analytics pinpoints which segment of the patients are at higher risk for diseases and hints for early intervention to protect them. This sort of prediction is more suitable for depictions with respect to certain chronic diseases.

Predictive analytics is drawn by aggregating data that are related to a variety of factors which includes patient’s medical history, demographic area data, socio-economic profile data, patient’s comorbidities existing in the area, etc.

3. Eases patient diagnostics with EHRs:

This is the most widespread application of big data in enabling effective patient diagnostics with every patient having their own electronic health records (EHRs). This EHRs includes the demographics, medical history, allergies of patients, diagnostic test results of current and previous illnesses along with other details.

These EH records are shared through secured information systems and are easily accessible by doctors and other healthcare professionals. They can access these files and the personal data cannot be modified but the diagnostics and treatment can be updated by the doctors. The EHRs can also trigger notifications alerting the patients about their forthcoming doctors or diagnostic visits and even track their prescriptions.

4. Ensures to reduce overall healthcare costs:

Healthcare providers can leverage the electronic health records (EHRs) which significantly helps to identify large patterns that lead to a greater understanding of patient’s health patterns. This in turn can essentially help cut costs by reducing unnecessary care or hospitalization.

Typically, the greater insights this analytics data gives to physicians truly translates to better patient care. This data also pinpoints them for shorter hospital stays, and in some cases for fewer admissions or re-admissions. This further helps patients with healthcare cost reduction due to lesser hospitalizations.

Moreover, by using predictive analytics, the data helps to estimate individual patient costs and helps to maximize healthcare efficiency enormously by carefully planning the treatment.

5. Delivers greater insights into patient cohorts:

By analyzing healthcare big data, it draws a greater insight into patient cohorts that are at greatest risk for various illnesses, and in a way helps to take out some proactive prevention measures.

Interestingly, this sort of analytics data can effectively be used to educate, inform and carefully motivate patients to take the responsibility for their own wellbeing. Moreover, by bringing over the clinical data together, it helps to bring in more effectiveness into the patient treatment plans which ensures better patient outcomes.

6. Enables improved healthcare with fitness devices:

Today, many consumer fitness products are available like Fitbit, and Apple Watch, etc. which keep a track on the physical activity levels of the users. The data thus collected by numerous devices used by people is sent to cloud servers, which is categorically being used by physicians to know the overall health and can even accordingly plan for individual’s wellness programs.

This analytical fitness products user data is analyzed which can be accessed by physicians to know about their physical activity levels and the data can also be used to know about specific health-related trends.

7. Generates real-time alerting:

There are specific medical healthcare decision support software that analyzes medical data on the spot that deliver real-time alerting to help healthcare providers who in turn use that real-time data to deliver better prescriptive decisions.

Doctors, in order to reduce the patient visits to hospitals are insisting on patients to use wearables which will collect patient’s health data continuously and sends the data to cloud. This data is accessed by doctors to prescribe medicines based on the results and values.

Conclusion

In today’s competitive world, latest technologies like big data analytics, artificial intelligence, machine learning are being used by healthcare organizations to gain real-time patient insights with huge data available in place.

Specifically, by using big data analytics in healthcare, it empowers with actionable insights on patient’s data and outcomes and ensures to reduce overall healthcare costs, predict high risk patients more quickly, generate real-time alerting and so on.

Healthcare solutions providers must ensure that their applications are high-performing and delivering great customer experience by enabling end-to-end digital testing of these healthcare solutions by leveraging next-gen testing services (www.testingxperts.com).

The post 7 Key Benefits of Big Data Analytics in Healthcare first appeared on TestingXperts.

To stay ahead of the curve, nsurance companies across the world have started embracing newer technologies including mobile and cloud-based options. Insurance companies globally are taking all possible measures to deploy innovative technology for developing business processes and streamlining legacy applications. Let us explore top technology trends being adopted lately.

The insurance industry is executing a modernized workflow, which demands a considerable expansion in the use of technology and that can only be managed by means of hybrid and on-premise cloud infrastructure. The cloud-based infrastructure is guaranteeing flexibility to clients. Besides, it is also helping the insurance sector to comply with the evolving regulatory standards within the industry.

The current application driven economy is all about data. Insurance is an industry that is data-driven which produces huge amounts of structured and unstructured data. Thus, insurance companies are relying on the Internet of Things (IOT) to gather more and more data relating to the behavior of their clients. On the other hand, Big Data and Analytics help insurers take important decisions based on the analysis of gathered data.

Gartner, Inc. forecasts that “8.4 billion connected things will be in use worldwide in 2017, up 31 percent from 2016, and will reach 20.4 billion by 2020. Total spending on endpoints and services will reach almost $2 trillion in 2017.

Regionally, Greater China, North America and Western Europe are driving the use of connected things and the three regions together will represent 67 percent of the overall Internet of Things (IoT) installed base in 2017”

Source: Gartner, Press Release “Gartner Says 8.4 Billion Connected “Things” Will Be in Use in 2017, Up 31 Percent From 2016”, February 7, 2017.

AI has been helping insurance companies develop systems that require manual processing and human intelligence. The advent of Artificial Intelligence in the insurance industry has made it easier for insurance agents to deliver efficient and flawless automation of existing claim processes. In the upcoming days, Artificial Intelligence will be more disruptive and will be used to evaluate and recognize emerging risks.

Gartner predicts that by 2020, AI will be a top five investment priority for more than 30 percent of CIOs.

Source: Gartner, Press Release “Gartner Says AI Technologies Will Be in Almost Every New Software Product by 2020” July 18, 2017

The key to moving towards a digital environment and improving customer service is to automate workflows where required. While standard direct interaction may not be so common between insurance entities, however, their relationship with the client is as important. Intrinsically, insurers need more time to interact with their clients and less time scrolling down documents and sorting papers. This is where automation of workflows helps.

To implement these technology initiatives in the existing and continuously growing technology environment, software testing is imperative.  To be able to cope with the cumulative intricacies of systems and applications, insurance companies need a reliable testing partner.

Conclusion

TestingXperts (Tx) is extensively helping global insurers in their digital transformation and helping them deliver improved customer experience and gain competitive advantage. Tx’s testing services portfolio and unparalleled track record have proved it as a preferred technology partner for the Insurance clients. Connect with us to know more about our testing services specific to the insurance industry.

The post Top 4 Technology Trends Transforming the Insurance Industry first appeared on TestingXperts.

The evolution of big data has been extraordinary, but it has led to the rise of bad data as well. In this infographic, we will discuss the importance of Big Data Testing in 2017.

Download this Infographic in PDF

The post Significance of Big Data Testing in 2017 first appeared on TestingXperts.

A TCoE is constituted of diverse testing processes, people, and tools operating as a shared services function to provide software testing services with maximum benefits across the entire test organization.

An Infographic on our Testing Center of Excellence (TCoE)

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