Introduction
The insurance industry is considered as one of the high–risk sectors that navigate through claim proceedings, pricing, promotion, risk mitigation, natural perils, compliances, and other such complex tasks. As a result, the industry has always relied on statistical models and a plethora of data to facilitate the diversified decision-making process. An extraction of vast data sets has certainly made the insurance job quite monotonous and troublesome. However, the case is no longer the same as with the introduction of Data Analytics in the insurance industry, the data extraction, and analysis process has completely been streamlined and made speedy. Hence, the term here is referred to as “Insurance Risk Analytics “
Features of Insurance Risk Analytics
Insurance Risk Analytics focuses on three major aspects effective data management, the inclusion of data modeling & machine learning models and architecture, ensuring the best use of scalable architecture.
| Particulars | Description |
| Data Management | The Insurance Risk Analytics integrate, visualize, manage, and secure the data requirements of the industry and help provide them the refined and usable information. |
| Data Analytics | The AI-driven data analytics model of Risk analytics help to assess the risk capital using intelligent machine learning tools, statistical models, image processing, and other advanced tools. |
| Technical Architecture | The high computing Risk Analytics help insurance companies to get the desired results in the shortest span. This model of risk analytics is driven by scalable architecture requirements to ensure better accuracy. |
Use Cases of Insurance Risk Analytics
Risk Data Analytics has reshaped the entire insurance sector and has helped form a better claim and processing structure for both clients and the company. Furthermore, its extensive use cases have made it possible for the companies to fetch the best outcome from the available data structure.
| Particulars | Description |
| Fraud Detection | The data analytics technology helps insurance companies to detect suspicious claims using behavioral patterns and arrive at accurate results. As a result, helps the companies to develop required procedures and policies in advance. |
| Mitigating Risk in Real-Time | The volatile risk-prone insurance sector requires quick and accurate hands-on decision making which becomes possible with the timely use of advanced analytics. With this, even the minute data and details regarding the insured object and related factors can be assessed and analyzed. |
| Personalized Marketing Strategies | With the emergence of extensive digital communication, it has become important for insurance companies to establish personalized marketing strategies to best suit the needs and lifestyles of the customers. |
| Predicting Lifetime Value | The companies can easily predict the Customer Lifetime Value (CLV) using predictive analytics which further helps to determine the profitability of the various policies. It also defines the buying and retention behavior of the customers. |
| Claim Prediction | It is quite imperative that predicting the occurrence of events that are of utmost importance to the insurance companies is the core job. Advanced data analytics helps to build reliable finance models to make the best outcomes from vast data sets. |
Metrics used for Insurance Risk Analytics
The insurance-specific Key Performance Indicators (KPIs) allow managers to track the operational efficiency and develop required strategies for removing the related issues such as fraudulent claims, etc.
Lead- Focused KPIs
These KPIs act as the key indicator for lead quality and determine the success rate of the insurance KPIs.
| Particulars | Description |
| Contact Rate | This metric helps to analyze the number of leads and their interest level towards the company. Based on the first interaction with the client the company decides whether or not to move ahead with the further formalities. |
| Blind Rate | This KPI becomes extremely useful to figure out which agent is effectively closing the deals and which ones are not able to stand by the expectations. Hence, it is defined as the percentage of quotes when converted into legally binding processes. |
| Quote Rate | Quote rate helps to understand the overall performance of the staff. Here, the quotes are broken down by agents to understand the individual performance and facilitate better comparison between the leads and conversions. |
Cost – Focused KPIs
This KPI plays important role in measuring the cost associated with the sales pipeline stage. As a result, helps to optimize the pipeline expenditure at regular intervals.
| Particulars | Description |
| Cost per bind | This cost is also referred to as Cost per Acquisition and helps to look deeply into the cost involved in the policy binding and customer acquisition. |
| Cost per premium | Using the cost-driven KPIs the insurers can understand the lowest cost per acquisition in the marketing scenario. The metric segments the monthly expenses into driving revenue in respect to the premium per lead. |
| Loss Ratio | It is a most common metric that is nowhere specific to insurance companies. However, here it is used to identify the loss-making policies, fraudulent claims, natural catastrophe, inefficient underwriting process, etc. which bring losses to the company. |
| Cost per quote | It is the crucial insurance metric that agents often tend to overlook. However, it holds great importance for determining which costs are incurred while quotes are put forth. |
| Underwriting Expense Ratio | Such ratio includes the cost of selling, underwriting, and other customer services. The high rate of underwriting expense ratio signifies low productivity. Hence, the insurers focus on reducing the existence of such expense ratio. |
Examples of Insurance Risk Analytics
There are several success stories associated with the uses of Insurance Risk Analytics which have made the insurance proceedings more seamless and flawless.
| Allianz Insurance in the Czech Republic has successfully saved USD4.5 million each year by reducing the quantum of fraudulent claims payments. This significant saving is the result of the effective use of Advanced Analytics which helped to predict such cases well in advance and take required measures accordingly. |
| Fukoku Mutual Life, an insurance provider uses the AI-driven analytics technique to access all the medical files and process claims faster. As a result, it has helped the organization improve staff’s productivity level by 30%. |
| Lemonade which is an InsureTech Startup relies on big data analytics and machine learning models to ease the end–to–end insurance tasks. As a result, it allowed the company to undercut the cost involvement, speed the customer acquisition, and ensure longer customer engagement by making the use of AI- Chatbots. |
| Tokio Marine, an auto–insurance company has recently deployed an AI-driven computer Vision system to examine and fix the damaged vehicle at the initial stage. As a result, the insurer witnessed a significantly shorter processing time for appraisal and customer satisfaction processes. |
| EY Insurance Industry has facilitated the tech-enabled process – Optical Character Recognition to improve the operational efficiency of the agents and other staff. This activity has increased the state of automation and has also resulted in 80% cost savings for individual processes. |
| AXA CZ/SK uses deep learning algorithms to improve the data ecosystems in their company. As a result, the use of profound technology has improved case acceptance and potential market accuracy by 25% and 83% respectively. Additionally, has also led to the reduction of throughput time for underwriting by 10 times. |
| Anadolu Sigorta, a Turkish Insurer used Artificial Intelligence to detect the frauds more effectively which earlier took more than two weeks if done manually. Hence, with the exemplary switch towards predictive system company was able to realize a significant ROI of 210% in merely a year with over $5.7 million savings coming from an AI-based Fraud detection system |
| SARA Assicurazioni and Automobile Club used Artificial Intelligence to facilitate the Car Accidents Insurance process. Here, the company entices drivers to install the ADAS system in exchange for which they received the 20% discount on insurance premium. As a result, it reduced the rate of personal liability claims due to accidents by 4 to 25%. |
Software used for Insurance Risk Analytics
There are several tech stacks in the data analytics industry that have smoothened the journey of insurance companies in the long run.
| Particulars | Description |
| Insured Mine | It is the digital platform that enables Independent Agents and Carriers to provide Omni-channel experience to their clients. Its basic features include CRM, Task Manager, Marketing Automation, Opportunities Management, and other such Dashboard. As a result, helps the organization to expand their business with better leads, engagement, and conversions. |
| IVANS Markets | This online tool allows insurers to easily market their insurance products’ details to the relevant agents and facilitate the required proceedings. Such Data analytics software shows the search views, profile clicks, and more details which help insurers to gauge the agent demand achieving desired targets. |
| Tableau Insurance Analytics | This is the renowned Data Analytic software that helps Insurance industries to make visual analytics for complex data sets. Hence, it plays a crucial role in quickly exploring real-time problems and reacting to them at a faster pace. |
| PerfectQuote | One of its kind Group Insurance CPQ assists brokers and general agents to analyze and sell insurance policies with the least flaws and barriers. Largely, it is used in medical and ancillary product lines due to its powerful contribution modeling, also making it the countable choice of top insurers across the world. |
| APT Insurance Analytics | Using this profound technology insurance companies can easily read the signal and identify their impact on the insurance metrics. Such metrics include premium, claim amount, the requirement for new policies, and building statistical clarity. |
| Risk Match | It is an insurance intelligence solution that allows agencies and brokerages to stay competitive in the industry by providing comprehensive data available along with required insights about the same. |
| TerraClaim | It is a Risk Management and Automation solution that is suitable for all lines of insurance and provides full-fledged claim management and benchmarking platform built on Artificial Intelligence. |
| Zelros | Zelros is the first AI-driven software that is specifically dedicated to the insurance distributor for assisting them in sales and digital marketing activities. Moreover, it helps insurance personnel to create seamless and personalized experiences for the customers. |
| Earnix | This leading technology provider focuses on mission-critical systems for insurers and banks across the world. Using these business entities can provide personalized products in a smarter, faster, and safer manner. In just a minute, Earnix allows the creation of data for analytics and valuable insights. |
The Future of Insurance Risk Analytics
According to the reports of EY Insurance Outlook 2021, the customers now prefer buying insurance online whereas customer online preferences for Auto insurance are at 69%. Additionally, 61% of customers prefer purchasing health insurance online wherein 58% consider life insurance online. As a result, it becomes understandable that online modes need to well –versed with the latest technologies like AI-driven data analytics. Moreover, the extensive use of such technologies not just speeds the process but also improves operational efficiency, accuracy, and accessibility and enables a cost-effective product approach. Hence, Insurance Risk Analytics has a long way to go in this completely digitalized era.
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