Insurance companies worldwide have been put to the test since the pandemic hit the world. With the increasing difficulty in predicting and segmenting claims, many insurers have fallen behind, allowing people with ill intentions to carry out frauds without detection. AI data analytics is the key to reducing the frequency of insurance fraud detection and the cost of fraudulent claims raised against insurance policies.

Insurance businesses now proactively leverage artificial intelligence (AI) algorithms to identify fraudulent activities or unusual claim requests. With AI and ML-based technologies, insurance companies monitor potential fraudulent activities continuously through AI data analytics and automate critical tasks, including claims management.

Role of AI data analytics in the insurance industry

As the endless amount of customer data piles up, there is no denying the fact that insurers are overwhelmed by disorganized and fragmented data that never seems good enough to be used effectively. The problem isn’t the data itself—it’s how it is analyzed, how quickly it becomes outdated, and how resources are wasted on deciphering patterns without automation.

AI helps insurance companies detect fraud as it occurs and connects data sets that would usually be siloed. The rightful implementation of AI and data analytics gives insurance organizations better control over fraud prevention. AI data analytics provides context regarding the behavior and patterns that may be new to the system. This way, insurance companies focus on customer profiles as investigators and can identify any fraudulent activities as it happens quickly. At the same time, AI data analytics continues to identify legitimate claims and offer a seamless customer experience.

As insurers gain an accurate, contextual, and holistic view of the data, they are in a position to use this information more effectively and take effective business decisions. AI-based data analytics also frees up resources responsible for handling complex data and allows them to be assigned to complex investigations.

Types of Insurance claims frauds

Unfortunately, fraud is a common phenomenon in all insurance sectors, including medical, auto, and home. Most prominent insurance companies know that insurance fraud occurs, but lack the resources to detect and investigate all potential fraudulent claims.

Among the various types of insurance frauds, the most common ones include:

Exaggerated claims:

In this case, the insured person raises the claim value unethically. For example, someone raised a claim of a stolen car for $10,000 when it was worth a fraction of that.

False claims:

During false claims, the insured person makes up a false story of an incident that never occurred, such as an accident or a theft.

Duplicate claims:

In duplicate claims, the insured person raises more than one claim from the same insurance provider for the same service date and incident.

Problems with traditional fraud detection methods

The traditional approach for fraud detection is based on developing heuristics around fraud indicators. Based on these heuristics, a decision on fraud would be made in one of the two ways. In certain scenarios, rules would be framed to define if the case needs to be sent for investigation.

In other cases, a checklist would be prepared with scores for the various fraud indicators. An aggregation of these scores, along with the value of the claim, would determine if the case needs to be sent for further investigation. The criteria for deciding indicators and the thresholds will be tested statistically and periodically recalibrated.

The challenge with the above approaches is that they rely very heavily on manual intervention, which leads to the following limitations:

• Constrained to operating with a limited set of known parameters based on heuristic knowledge – while being aware that some of the other attributes could also influence the decisions

• Inability to understand context-specific relationships between parameters (geography, customer segment, insurance sales process) that might not reflect the full scenario. Consultations with industry experts indicate that there is no ‘typical model,’ and hence challenges to determine the model specific to the context are essential.

• Recalibration of the model is a manual exercise that must be conducted periodically to reflect changing behavior and ensure that the model adapts to investigation feedback. The ability to perform this calibration is challenging.

How AI data analytics helps in fraud detection?

AI and machine learning tools help spot unusual patterns unnoticeable by the human eyes. For instance, reviewing any insurance claims compares the new claims to the existing data and helps to detect fraudulent and non-fraudulent claims. It continuously monitors customer behavior and analyzes the patterns. If a customer raises an insurance claim, AI looks for patterns first. If there are any unusual patterns detected, the system raises a flag by pointing out odd claims and highlighting them for the necessary investigation. Hence, without human intervention, it will analyze the claims and help reduce fraud.

AI algorithms are able to easily detect suspicious patterns among millions of insurance claims, allowing the insurers to analyze the requests in real-time. Among the many benefits of AI data analytics in insurance companies, claims fraud detection is undoubtedly a groundbreaking technology enabling firms to modernize their existing processes, increase operational efficiency, and lower overhead costs.

Business outcomes for the Insurance industry with AI data analytics

Top AI Trends Transforming the Insurance Industry

Artificial Intelligence enables transformation across various sectors, and the insurance industry is no exception. Insurers leverage AI to streamline processes, deliver better customer experiences, and make more informed decisions. Let’s look at some top AI trends that are reshaping the insurance industry:

Hyper-Personalization

One of the most significant trends driven by AI is hyper-personalization. AI-powered technologies enable insurers to offer highly personalized services based on customer needs and preferences. These include customized policies, pricing models, and even communication strategies.

AI allows insurers to analyze vast amounts of data and draw insights about customer behaviors, risk profiles, and preferences. These insights can tailor products and services to each customer’s unique needs.

Data Explosion from Connected Devices

The interconnection of connected devices, often called the Internet of Things (IoT), generates vast real-time data. AI is at the forefront of helping insurers make sense of this data deluge. Each device provides valuable data points for risk assessment, pricing strategies, and claim processing, from wearables and smart home devices to connected cars and industrial sensors.

Extended Reality

Extended Reality (XR), which is the combination of Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), is another trend transforming the insurance industry. XR technologies and AI are paving the way for immersive and interactive experiences. In the insurance sector, these technologies can be used for training purposes, risk assessment, and even improving the customer experience.

Data Precision

Data is the lifeblood of the insurance industry, and AI is enhancing the precision of this data. Using machine learning algorithms, AI can sift through vast amounts of data, detect patterns, and extract precise insights. These insights can then inform underwriting decisions, pricing models, and risk management strategies. AI can analyze data to identify unusual patterns or discrepancies indicating fraudulent activity, enabling insurers to address such issues proactively.

Conclusion

Insurance frauds create alarming financial risks for Insurance companies – that’s why they should take up proactive measures to strengthen their fraud prevention methods. Technologies such as AI, ML, and predictive analytics are now being adopted aggressively in the insurance industry, helping Insurers in the overall business transformation. Effective implementation of these latest technologies offers a strong ROI in fraud analytics because these technologies enable insurance businesses to overcome fraud leakage in the claims area.

How can Tx help?

TestingXperts (Tx) is extensively helping global insurance companies in their digital transformation and allowing them to provide unmatched customer experience. Tx’s testing services portfolio and unparalleled track record have proved it a preferred technology partner for insurance clients.

TestingXperts understand the prerequisite of the industry and support you via customized testing solutions such as:

• Functional & Automated Testing

• Mobile App Testing

• RPA Testing with Bots

• Database Testing

• Integration Testing

• Security Testing

• Accessibility Testing

• Usability Testing

• AI-Based Testing

• Performance Testing

• ETL Testing

• Insurance Test Center of Excellence (TCoE)

We have extensive domain expertise across various segments of the Insurance sector like Life, Property and Casualty, Auto, Travel, etc. Our proprietary IPs & tool-agnostic test automation framework ‘Tx-Automate’ help reduce testing efforts by 30%-40%.

The post How AI Data Analytics Helps with Fraud Detection in Insurance Claims? first appeared on TestingXperts.

1. What is Predictive Analytics?
2. What is Performance Engineering?
3. Core Activities in Performance Engineering
4. Impact of Performance Bottlenecks on Business Operations
5. How Predictive Analytics Identifies Performance Bottlenecks?
6. How Can Tx Assist with Predictive Analytics for Performance Engineering?
7. Summary

What if there’s a magic mirror at work that gives a peek into the future of a software development project? The project managers, business analysts (BA), and other stakeholders could identify potential performance bottlenecks with precision before they happen. Amazing, isn’t it? According to a study, about 95% of enterprises are using AI-powered predictive analytics to navigate their marketing strategies. Predictive analytics leverage past data to determine the possibility of specific outcomes. Businesses can improve their decision-making to better plan.

What is Predictive Analytics?

Predictive analytics is a subset of data analytics that leverages previous or historical data, ML techniques, and statistical algorithms to predict future events. Enterprises need predictive analytics to find potential bottlenecks in their application’s performance. It is often linked with data science and big data. Its key concepts include:

• Analyzing current and past data to identify patterns and trends likely to recur in the future with data-driven forecasting.

• Using statistical techniques like regression analysis, time series analysis, decision trees, etc.

• Using ML algorithms like neural networks, support vector machines, and random forests to improve prediction accuracy.

• Leveraging across industries like business, finance, healthcare, and marketing.

What is Performance Engineering?

Performance engineering is a proactive approach to software development that allows businesses to ensure their application meets performance, reliability, and scalability benchmarks. Instead of post-development testing, it’s a continuous process integrated within the software development lifecycle (SDLC). Performance engineering involves designing, implementing, and testing software to meet performance metrics like response time, throughput rate, scalability, reliability, and resource usage (CPU, disk, memory, etc.). Performance engineering delivers the following benefits:

• Improved user experience by ensuring speed and responsiveness in an application under development.

• Reduced costs by identifying and fixing performance issues early, preventing rework and delays.

• Faster software development by optimizing the development process, enabling teams to deliver applications faster.

• Improved reliability by ensuring that applications are reliable and can handle varying workloads.

Core Activities in Performance Engineering

• Performance Testing: Load testing, stress testing, and endurance testing to simulate real-world conditions.

• Profiling & Monitoring: Identifying performance hotspots in code or infrastructure.

• Optimization: Tuning code, database queries, memory usage, and network calls.

• Capacity Planning: Calculating resources required as per usage.

• Architecture Design: Selecting performance-based technologies and structures from the beginning.

Impact of Performance Bottlenecks on Business Operations

Software performance bottlenecks not only frustrate users but also impact business outcomes. They can negatively impact productivity, customer satisfaction, and costs. They also create software release delays and reduce throughput value. Decision-makers must understand that the technical slowdowns can erode revenue, brand value, and operational efficiency.

Revenue Loss:

Slow applications directly affect the bottom line, especially in transactional systems like eCommerce platforms, SaaS tools, and payment gateways. For example, a one-second delay in page load time can reduce conversion rate by 7%. For high-traffic sites, that could mean millions in lost sales annually.

Customer Frustration:

In today’s tech-driven world, user expectations have evolved. A minor lag in software performance can change a user’s perception of the product’s reliability. This could lead to users abandoning apps or switching to competitors. Bottlenecks in customer-facing systems like mobile apps, websites, and APIs can increase churn rates and reduce customer loyalty.

Reduced Productivity:

Performance bottlenecks in enterprise-grade platforms/tools can slow down teams, delay project timelines, and increase frustration. For instance, a slow CRM system or development platform can cause wasted hours, resulting in productivity loss and low morale across departments.

Poor Market Performance:

Performance is a brand asset in today’s interconnected technology landscape. Crash-prone or laggy applications can no longer fit into the market, especially in highly regulated industries like finance, logistics, or healthcare. Poor performance invites negative feedback, social media backlash, and trust issues.

Increased Costs:

Performance bottlenecks cause more escalations and emergency fixes, draining time and resources. Teams spend more time on firefighting than innovating, increasing SLA penalties and infrastructure costs.

Inaccurate Forecasting:

Bottlenecks negatively affect system behavior, making data unreliable for decision-making. This level of poor visibility causes misguided investments in infrastructure, hiring, or customer growth initiatives.

How Predictive Analytics Identifies Performance Bottlenecks? 

KPIs Monitoring:

Predictive models continuously track system KPIs like CPU usage, response times, error rates, memory consumption, etc. These metrics help detect abnormal patterns indicating emerging bottlenecks. For example, a rapid increase in average response time for a microservice could mean low database query performance.

Predicting Emerging Failures Using Past Data:

Predictive models learn from past incidents to identify patterns indicating slowdowns, crashes, or breaches. These insights help the analytics team to forecast when and where such failures can repeat under comparable conditions.

Automated Anomaly Detection:

Advanced anomaly detection leverages ML algorithms to flag patterns indicating abnormal system behavior before they affect application performance. Predictive models catch subtle warning signs like latency dips, increased garbage data collection, and retry rates, common bottleneck indicators. Early alerts will enable teams to investigate and address issues before they escalate.

Predicting Load and Capacity Constraints:

Predictive analytics simulate future resource usage trends to develop infrastructure requirements. It covers load growth, user concurrency, and resource utilization, enabling businesses to identify when components will be overburdened.

Proactive Action and Optimization:

When a bottleneck is detected, the final stage is proactive action. Performance engineers use predictive insights to reallocate resources, refactor inefficient code, adjust caching strategies, schedule hotfixes, and fine-tune CI/CD workflows to improve test performance. This helps enterprises prevent failures and ensure smoother releases, and better UX.

How Can Tx Assist with Predictive Analytics for Performance Engineering?

At Tx, we leverage ML models and AI-powered data analysis to monitor trends, detect anomalies, and predict performance issues. Our approach involves continuously analyzing real-time and previous metrics to provide early warning signs and potential issues. We allow your teams to shift from reactive firefighting to strategic optimization. We leverage top-of-the-line observability and application performance monitoring tools like Prometheus, Grafana, Datadog, New Relic, and AWS CloudWatch.

Tx offers real-time alerts with its Tx-Insights dashboards, indicating performance risks (if any). Key features include:

• Customized dashboards displaying forecast trends and application health.

• Predictive alerts for security alerts, performance dips, and more.

• Visual root cause indicators to prioritize actions.

By combining predictive analytics with integration and actionable insights, Tx can become your strategic provider of performance engineering services. We can assist you in delivering resilient, high-performing applications at scale.

Summary

Predictive analytics empower performance engineering by identifying software bottlenecks before they occur. Analyzing historical and real-time system data helps teams prevent failures, optimize resources, and ensure smoother releases. Performance bottlenecks can affect user experience, business operations, and brand reputation. Tx optimizes this process by integrating predictive insights with popular monitoring tools, enabling proactive performance management through real-time alerts and intelligent dashboards. We help enterprises deliver scalable, high-performing applications with greater speed and reliability.

The post Predictive analytics in Performance Engineering: Identifying Bottlenecks Before They Happen first appeared on TestingXperts.

1. Retail Analytics and its Importance for eCommerce Business
2. Types of Retail Data Analytics
3. 5 eCommerce and Retail Analytics Use Cases
4. Retail Analytics Best Practices for eCommerce Business
5. Summary
6. How can Tx help with Retail Data Analytics?

The retail industry has rapidly transformed in recent years due to technological advancements like AI/ML, RPA, etc. Evolving tech innovations, changing consumer demands, and global competition force retailers to remain competitive for business success. Especially in eCommerce retail, business owners must focus on technology, CX, and security. They must use their years of experience to decide which item to enlist at what location and the target audience, analyze geographical market conditions, and determine what type of marketing campaign would draw more business.

Although retailers often listen to their instincts, it is no longer impactful in the digital business age, especially in the eCommerce industry, where profit margins are very narrow. Consumers have numerous options, and market conditions vary based on demand. This is where retail analytics comes into play.

Retail data analytics software can help eCommerce retailers make decisions and drive growth. According to McKinsey, businesses that integrate modern AI and deep learning techniques (part of advanced analytics) can experience enhanced performance by 30% to 128%, depending upon the quality of data available and specific applications. Retail analytics allows retailers to understand consumer behavior, optimize inventory, personalize marketing efforts, and enhance CX, vital for upscaling eCommerce operations.

Retail Analytics and its Importance for eCommerce Business

Retail analytics is the process of collecting, analyzing, and interpreting data derived from various sources and putting it through the analytics engines. The data sources include cash registers, POS devices, ERP, inventory and supply chain data, pricing, discounts, financial ledgers, etc.

It uses software to analyze data collected from all these sources and deliver insights about consumer behaviour, shopping trends, inventory counts, etc., to the retailers. It can also improve the decision-making process about inventory, marketing campaigns, pricing, eCommerce store operations, merchandising, etc., by implementing predictive algorithms against internal and external repositories.

Retail analytics also helps measure user loyalty levels, predict demand forecasts, identify buying patterns, and optimize inventory layout. Thus, retailers can offer personalized discounts on their eCommerce platforms, which could result in higher average cart sizes and frequent site visits.

Following are some of the factors that show why retail analytics is important for eCommerce businesses:

• eCommerce retailers can track and analyze customer interactions across various digital platforms. It will identify patterns and trends in consumer behavior, such as peak shopping hours, customer preferences, and popular products being sold. Retailers can use this data to tailor marketing efforts and product offerings to meet customer needs better.

• Retail analytics insights enable eCommerce businesses to create effective marketing campaigns. For example, retailers can tailor targeted cross-selling strategies by analyzing the frequently viewed product. Also, by analyzing the user journey, they can optimize advertising costs by focusing on effective touchpoints and channels.

• It helps in streamlining operations by monitoring inventory levels and sales data. Retailers can manage demand forecasts and stock levels to reduce holding costs and prevent out-of-stock situations.

• It helps improve sales growth by better understanding consumer and market needs. Analytics allow businesses to make data-driven decisions to facilitate sales improvement.

Types of Retail Data Analytics

Retail data analytics is divided into five main types, each serving a specific purpose and providing insights into various retail operations. Having an understanding of these types would allow eCommerce businesses to utilize data for decision-making and create strategies:

Descriptive Analytics:

Descriptive Analytics is the foundation of retail data analytics. It includes summarizing historical data in an understandable format so that retailers can analyze past events. It enables businesses to track metrics like average transaction size, total sales, and yearly growth. This builds a solid foundation for understanding patterns and trends that affect business performance.

Diagnostic Analytics:

Diagnostic analytics allows eCommerce retailers to identify and analyze issues responsible for low performance. It combines data from multiple sources and uses techniques like drill-down, data discovery, cause-and-effect analysis, and correlation. For example, if sales drop in a particular quarter, diagnostic analytics will help reveal the cause, which could be a stock outage, pricing error, etc.

Predictive Analytics:

It helps retailers predict future events based on variables such as weather, supply chain disruptions, global market trends, economic trends, and competitive pressure. Predictive analytics is also known as what-if analysis. For instance, what action should be taken if a product runs out of stock due to certain actions like discount offers, short-duration rise in demand, etc.?

Prescriptive Analytics:

Prescriptive analytics involves utilizing AI and Big Data to analyze predictive analytics outcomes and create recommended actions, such as increasing customer satisfaction, improving CX, or maximizing profits. This type of analytics is necessary in decision-making processes.

Customer Analytics:

It is a subset of retail analytics that focuses on collecting and analyzing data related to customer behavior, demographics, and shopping needs. eCommerce businesses can use this data to tailor their marketing strategies, improve customer engagement, and optimize retention strategies.

5 eCommerce and Retail Analytics Use Cases

Retail analytics applications can transform eCommerce operations, making them more customer-centric and data-driven.

Following are some of the compelling retail analytics use cases to enhance eCommerce operations:

Using Heatmaps to Improve Website Layout:

One of the coolest features of business intelligence tools is Heatmaps. These maps provide a visual representation of the website UI or store layout. The darker elements represent the higher traffic, or the spaces users clicked the most. The lighter elements represent lesser traffic or spaces where users clicked very little.

Heatmaps can enable eCommerce retailers to analyze customer behaviors and make better business decisions. For instance, eCommerce retailers can analyze what is interactive to their customers and why and where the bounce rate is high. What is working for the business, and what is not? eCommerce retailers can use this data to create a better business strategy.

Inventory Management:

Effective inventory management is crucial for handling supply chain efficiency and reducing costs. eCommerce businesses can predict product demand using retail analytics, allowing them to optimize inventory levels. Retail analytics helps predict product demand, allowing businesses to optimize their inventory levels and prevent overstocking and understocking. This, in turn, ensures popular products are always available in eCommerce stores.

Predictive neural networks for Fraud Detection:

Deep neural networks help in fraud detection activity to avoid bad user experience. These are highly efficient in identifying fraudulent behavior, flagging it, and predicting any type of fraud. Incidents like return or delivery scams, consumer rights abuse, and credit risks can damage the retailer’s reputation. Predictive neural networks in retail analytics can safeguard against such frauds.

Price Optimization:

Retail analytics enable retailers to optimize their pricing strategies. It uses competitors’ data to compare prices, consumer demand, and cost dynamics, enabling eCommerce businesses to set prices that maximize profits and remain appealing to customers. To ensure accurate competitor pricing insights across different geographies, many retailers use ISP proxies. These tools allow businesses to conduct price monitoring & localized search tracking without triggering anti-bot systems. Dynamic pricing models can adjust prices in real time depending on demand and inventory levels, which is crucial during sales or promotional periods.

CLV Module:

One of the major aspects of retail analytics is customer lifetime value (CLV) analytics. eCommerce retailers spend a lot on customer retention and acquisition activities. The CLV modules analyze the user behavior from their first transaction data to the most recent one to provide a comprehensive overview of the CX and what areas of the website need optimization. It also helps enhance the usability and customer satisfaction index, improving loyalty and business enhancement.

Retail Analytics Best Practices for eCommerce Business

Retail analytics can significantly improve eCommerce platforms’ decision-making and operational efficiency when implemented effectively. Following are some of the key best practices to ensure retail analytics can deliver the best outcomes for eCommerce businesses:

• Have rigorous data quality management processes to clean, validate, and standardize incoming data. It will prevent errors and ensure decisions are based on reliable information.

• Retail analytics incorporates data from multiple sources. Integrating customer data from transaction records, service interactions, etc., can provide a detailed view of the customer journey and help uncover deeper insights for informed decision-making.

• Implement real-time analytics to give eCommerce businesses a competitive edge by facilitating immediate responses to customer behavior and market conditions, such as launching targeted marketing campaigns and adjusting prices.

• Use analytical tools and technologies like predictive analytics, ML, and AI to enhance insights obtained from data. These tools can identify patterns and predict trends missed by traditional analysis methods.

• Regularly review and update analytics strategies to adapt to new market conditions and technologies. Continuous learning from past analytics results would help in optimizing future tactics.

Summary

Retail analytics is vital in the eCommerce & retail company, leveraging advanced technologies like AI and ML to drive growth and fulfill consumer demands. It allows businesses to understand customer behaviour, optimize inventory, enhance marketing efforts, and create targeted discount offers. It uses detailed data analysis from various sources, such as POS devices, customer interactions, and inventory systems. It also aids in making informed decisions, from customer engagement to designing pricing strategies, ensuring businesses stay competitive in the digital market.

How can Tx help with Retail Data Analytics?

At Tx, we understand that the complex eCommerce retail architect must be more dynamic and data-driven in the current digital environment. Our expertise in next-gen IT solutions like RPA, AI, and ML enables businesses to solve the complexities of their modern data infrastructure.

Partnering with Tx will give you the following benefits:

• Customized testing approach to ensure data quality at various phases of the ETL processes. We have partnered with QuerySurge to automate the Extract, Transform, and Load verification process.

• Thorough data sanity checks to verify data integrity, ensuring your analytics are based on reliable and precise information.

• Implement data visualization techniques to clarify complex insights, making it easier for businesses to make informed decisions quickly.

• Strong emphasis on compliance and security protocols during the data analytics process.

• Enable modernization of legacy data systems to ensure your analytics infrastructure can handle current data demands efficiently.

• Ability to monitor key performance metrics and adapt to evolving market conditions and consumer trends.

To know more, contact our experts now.

The post Importance of Retail Analytics in the eCommerce Industry first appeared on TestingXperts.

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. 

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