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Risk assessment in insurance is not something new; it serves as the bedrock for insurance companies in terms of analyzing risks linked to every individual policy. Before delving deeper into the transformation of these legacy processes, it is important to know what insurance company risk assessment stands for. The procedure is also known as underwriting and is a method deployed by insurance companies for the evaluation and assessment of risks attached to insurance policies. This helps in calculating the right premium amount for the insured individual. There are several risks linked to insurance including morbidity and mortality rate fluctuations, disasters, etc. Hence, the insurance risk assessment process goes through several methodologies including stress testing, parametric, simulation, stochastic models, benchmarking, deterministic and many others. Risk management is the fulcrum of the industry, with insurance companies accounting for every possible factor to create high and low risk profiles for policy holders. The risk level also influences the premiums on these policies. Insurance companies also collect massive data on prospective policy holders and the objects that are being insured. Data mining-based statistical tools and frameworks are now being leveraged for working out risk levels. How Technology Is Changing The Game When it comes to business insurance risk assessment, several reports confirm that most companies are now looking at big data analytics and other insurance risk assessment software for augmenting their underwriting systems. When it comes to underwriting, the following steps are usually covered: With this premise at the forefront, here is how predictive analytics is transforming the entire picture: Predictive modeling enables the creation of models with mathematical/statistical tools. These illuminate future performance of policies, offering insurers a detailed analysis of risks involved in the process through inherent data patterns. These models can be neatly added to applications. How It Benefits The Whole Insurance Ecosystem Predictive and data analytics enable superior risk assessment while helping underwriters get automated outcomes for better business decisions. Insurance companies can leverage predictive modeling and analytics for more effectiveness and consistency in the process. This will not only help them lower costs, but also enhance overall client experiences while ensuring sizable business development simultaneously. Insurance firms will benefit from lower processing time for applications as well. At the same time, insurance companies can forecast risks well in advance. This equates to faster identification of potential problem-areas and mitigating the same in advance to save money as well. Risk assessment can also help them customize their policy offerings for customers based on a better understanding of intrinsic factors and risk levels. When it comes to risk assessment and other analytics, Indus Net Technologies(INT) offers varied solutions for insurers. It ensures cutting-edge big data analytics enabling decision making and better performance across metrics like customer retention, cross-selling/up-selling, claims and fraud. INT ensures efficient processes and outcomes for insurers, helping with the following: Other solutions include m-commerce/e-commerce portals, API integration, lead-capturing portals, renewal, claims and quote & buy mobile apps, core insurance processing mechanism, and portals for managing brokers. All in all, INT. assumes the role of an end-to-end solution, while taking care of risk assessment in insurance with advanced analytics-driven solutions. About the author: Dipak Singh is a thought leader and data cruncher. Currently, he heads the Analytics Wings at INT. To know more do check out his LinkedIn profile here.
It is no secret that the global insurance industry is characterized by cut-throat competition and aggressive marketing outreaches to onboard more new customers. At the same time, despite adopting aggressive sales-funnel strategies, insurance fails to build that fabled consumer connect. Customers mostly remain confused about choosing the right insurance company and plan amidst a thousand options. Most companies also focus on lower prices as their key selling points. This leads to a scenario where insurance companies emphasize more managing costs/prices, lowering customer engagement and service quality, and enhancing their customer churn simultaneously. What is the best way for the insurance sector to acquire more new customers without adopting a price-first approach? What is the best solution for building better customer engagement strategies and relationships throughout the process? The answer lies in customer segmentation in insurance. Without suitable customer segmentation datasets, it is near impossible to tailor services/offerings to suit particular customer types or demographics. This is needed for ensuring that personalized touch and a broader service range to attract and retain consumers while understanding what they require in terms of assistance. What Is Customer Segmentation? By customer segmentation, we mean the practice of sub-dividing customers into multiple groups. Each group shares its own set of features, attributes, objectives, and requirements. This is the ideal way for insurance companies to come up with tailored products, services, and solutions for each group, ensuring better engagement for a wider range of customer groups. Types Of Customer Segmentation The conventional segmentation formula includes categorizing consumers based on parameters like gender, age, employment nature, income, and life stage among others. However, in a hotly contested insurance sector, there is a need to revamp the entire perspective towards segmentation with a customer-centric operational model, one that continually finds out newer parameters and keeps changing the framework likewise. Sounds confusing? While a specific consumer group may have some similar traits or attributes, it does not always equate to the same objectives, needs, and perspectives amongst individuals. Today’s customer is a smarter one, looking for hyper-personalization as per his/her specific requirements and stage of life. This is only possible with dedicated market research and ever-evolving segmentation. Why It’s A Win-Win Proposition Is customer segmentation in insurance worth it? It does have long-term advantages for insurers. Once segments are built, they can help in generating hyper-personalized solutions for a wider number of consumers. Segmentation makes it more convenient to build marketing material and insurance products alike. Insurance companies benefit from putting a vast chunk of consumers into more manageable and operational groups which helps them launch campaigns directed at specific audiences on time. This fosters better engagement, leading to superior product branding and higher customer satisfaction at the same time. It also helps insurance companies up-sell products or get more renewals into the door. Marketing campaigns may also be automated for tapping specific consumer segments with suitable solutions, based on the achievement of newer and bigger landmarks/milestones. And that is only the tip of the iceberg; segmentation helps insurers identify more prospects who share similar needs, traits, and features. Hence, many un-leveraged segments may be unearthed as a result. They can also focus on products that have a specific need/demand in the market, avoiding the expenditure of more energy/resources on those that do not cater to any specific segment/group. Segmentation can help in finding trends in the market while stimulating better strategies to cater to existing demand. Insurers can come up with segment-wise offers for enhanced retention and higher customer satisfaction, chalk out segment-wise pricing strategies and get more people to recommend their businesses to like-minded people. Behaviour-related consumer data (group-wise) also help insurers identify possibilities of risks or opportunities and come up with timely responses. For example, suppose a consumer group is identified through data, which has a bad experience with getting their claims processed, and in this case, the insurer can follow up with a timely message which states that these consumers can call the helpline for getting future claims resolved quickly or coming up with any other mechanism that nips potential issues in the bud before customers switch to other companies. This is only one out of several such possibilities and case studies that may arise in this regard. Data-driven segmentation results in what we call actionable insights, leading to higher customer acquisition, retention, and better marketing for insurance companies. Many technological tools and frameworks can enable evolving segmentation of consumers and they are being used by insurance companies today. However, that is a story for another day. About the author: Dipak Singh is a thought leader and data cruncher, currently, he heads the Analytics Wings at INT. To know more do check out his LinkedIn profile here.
High employee retention figures are naturally end-objectives for people-centric businesses. Contemporary organisations prioritize employee retention and the reasons are not far behind. Imagine a scenario where there is a high employee turnover ratio. There is not only a constant state of flux, but a financial impact too. Hiring and training costs go up exponentially for new resources, productivity suffers and with major positions remaining vacant for longer durations, it becomes excessively strenuous for the existing workforce. It is not that companies do not make efforts to address the problem. Many of them conduct internal surveys and feedback sessions. They also gather data and insights on welfare measures. Yet, reports that employee survey information may not always be an accurate indicator of the ground reality. A Harvard Business Review survey even reports that 58% of employees will trust strangers more than their superiors at work. Also, many entities fail to understand the reasons behind high employee turnover. 89% of organisations (as per the OfficeVibe.com report) feel that this happens due to the desire of employees to earn more. Yet, the same study shows how just 12% leave jobs due to lower salaries. What is the core problem? Rising employee turnover and for reasons more than financial. What can be a probable solution? Integrating technological solutions that are intelligent and nip the issue in the bud. Predicting turnover is the best way to enable preventive measures. How Predictive Analytics Can Help Bad decisions result from inaccurate or wrong data. Companies may enhance retention measures through depending on analytics that bypasses human feelings. How is this possible? Instead of asking employees about their happiness quotient, companies should account for key variables which determine the same. For predicting turnover, look for values consistently applicable throughout the organisation. They include the following: Promotions Rewards & Benefits Former Reviews & Appraisals Historical Pay Figures Usage of Sick Leave This data is already present with the company. You can also calculate variables like estimated time to commute to work, etc (from address information in the payroll system). Once data is available, you can get it analyzed thereafter. You will then find out the core reasons contributing more towards employee turnover. Tapping Existing Data And Other Moves Companies already have a lot of data as mentioned. Employee surveys should be carefully analyzed. The data should be accurate and more on the factual side. Relying on facts is better than emotions. Predictive analytics may be further optimized through integrating a data-based managerial outlook. Managers can optimally respond on the basis of data and variables which influence predictive analytics. Companies should create personalized analytics-driven strategies and blueprints for every division. Managers should be skilled in data interpretation and learn to respond accordingly. Whenever you can predict employee turnover, you can enhance your capabilities in tackling the issue. You may be successful in reducing the turnover figures through emphasizing on the specific employee types who are more likely to put in their papers. What Companies Can Also Do Your company can also go for a strategy revamp in order to fix these issues. Mainstream policy changes and relaxations may help enhance the chances of more employees staying on at the organisation. It will help reduce the costs of turnover accordingly. Employees may not always leave jobs for getting higher salaries elsewhere. Hence, turnover ratios can be lowered without paying higher salaries in most cases. Data and predictive analytics can thus help the company save more money on training, hiring and payroll costs. Survey results can only add to predictive analytics without being the core strategy. Companies may also look to enhance their overall working environments, cultures and productivity levels. Many a time, companies feel that higher salaries are equivalent to employee wellbeing. Yet, in such scenarios, they may skip other variables including work-life balance, work-from-home policies, lack of leave options, etc. This may lead to the same culture at work, leading to employees quitting in spite of earning handsomely. Integrating predictive analytics into organisational frameworks helps in reducing sudden reactions and decisions, scaling up the chances of successful employee retention across levels. Hence, if you are looking to positively transform employee retention figures, predictive analytics is the way forward. About the author: Dipak Singh is a thought leader and data cruncher, currently, he is working as Lead Data Scientist at INT. To know more do checkout his LinkedIn profile here.
Every day, more than 2.5 quintillion bytes of data are generated that are mostly unstructured and “some 40% to 50% of analysts spend their time wrangling the data, rather than finding meaningful insights”. But, these data need to be analysed and tracked to assess risk and inform fraud by the insurance industries. Moreover, Big data is not anymore a buzzword rather, it has become imperative for the insurance industry. With market dynamics evolving at a tremendous pace, insurance industries are expected to stay aligned with the data-rich world. But, Quantiphi report suggests that “80 per cent of data received by underwriters is unstructured”. Mostly these are in the form of forms, email, pdf and images. Therefore extracting meaningful data from it leads to a huge processing time which lowers the efficiency of the underwriting team. Thus with opportunities arises challenges! As big data is transforming the core concepts of the insurance sector simultaneously, it is also facing challenges that prevent experiencing the full potential of the data insights. Here are most faced top two challenges and their imperative solutions to deal with them: Challenge 1: Confluence of unstructured data and legacy system prevents from making actionable insights In a traditional insurance system, there is a barrier to seamless integration among different data depositories. It is often noted that each business has its own way of capturing data which they fail to communicate or share with other business units. Therefore, preventing insurance companies from realising the full potential of data. Solution: Build an integrated single platform that integrates new and existing data sources and makes data actionable by leveraging advanced analytical tools Challenge 2: Deployed actionable data insights only for the product level and not at a customer level Often customer insights are lost in silos more because they are scattered across the functional lines of the process. Also, there is a lack of predefined terms on customer insights; thus, insurance companies fail to recognise customers at different stages of the policy life cycle. Also, other business units fail to convey the insight for a particular customer to the other business unit, which further leads to an increase in expenses. Solution: Build customer-centric analytics solution for precise marketing, customer retention and increasing profit. This will make each business unit enhance the customer value across the policy lifecycle. Data itself has no value. To become information it needs to be processed and analysed thoroughly. With data generation increasing at an alarming rate, it becomes important to deploy new strategies and tools to make data work through actions. Learn how INT., with its proposition, is aiding and reshaping the underwriting landscape with an intense focus on customer experience.
Choose the right premium, build the right marketing campaign and elevate the value! – insurers are facing challenges maintaining a balance between providing enhanced customer experience and operating at the profit margin. When we talk about the underwriting process and the traditional method, we can see much human intervention and manual paperwork. It can only make the process gruesome! Reports suggest that the key challenges insurance companies face are binding the data and advanced technological capabilities into one to build value in the Insurance Value Chain. A formation of a successful strategy occurs when insurers can identify the business value generated by the ML and how it can be aligned across the business domain. Solutioning to this challenge, today, insurers are joining hands with software development partners to bring a radical change in the sector through early adoption. When we talk about the insurance value chain, we understand the end-to-end process from product development to underwriting and claim processing. As ML is an integral part of data science, so is underwriting for insurance. The ongoing crisis has reinforced the urgency to modernise the underwriting process. The companies that are adopting end to end digitisation of the underwriting process are the ones that are overcoming slowing down factors and modernising the customer journey in the underwriting process. Let us see how the analytics can be leveraged in the underwriting process from reporting to binding policy: Descriptive analytics: Claims are deeply studied and patterns are identified. Based on past historical data, descriptive analytics flags if any new trends emerge. Predictive analytics: As the process moves, underwriters use predictive analytics to evaluate the pricing competitiveness. It also alerts the underwriter through its risk scoring and assessing model. Prescriptive analytics: Further underwriter deploys prescriptive analytics to build a model based on the future economic scenario and predicts the future risk of the policies. It applies the advanced statistical model to recommend solutions such as automated underwriting in case of the most predictable risks. Recently machine learning is leveraged in the underwriting process, thus we have deeply studied the customer journey in the underwriting process to understand how it has improved and provided a seamless experience. Based on it, we have allotted the data science model, which can be leveraged by the insurers to effectively understand the journey and use it to the advantage of it. Submitting the TIFF/JPEG format form: When insurers confirm about digitally submitting the claim documents, they mean they are submitting image format documents. Data scientist deploys tools and models to parse the data and build a structured form. Analysing the risk: It becomes essential for the underwriter to get a granular view of the risk based on the historical risk and cost drivers. Underwriters are deploying a machine learning model. Based on the data generated from the social media platforms, historical data, and data from a third-party platform, the model assesses and scores the risk accordingly. Also, the classification model segments the customers based on their likings, motivation to purchase, etc., which further helps evaluate the risk and quote the premium well. Reviewing Rates and options: The rates depend upon the actuaries, and actuaries rely on the risk scoring model. Predictive analytics plays a more significant role when quoting the premium. Collection of the correct data, such as the likelihood of rash driving, sickness, defaulting, and other external data sources, are essential for the risk assessment. After the evaluation, if the underwriters find that the claim outcome is within the risk parameters, underwriters can easily quote the premium without much complexity. Through predictive analytics, underwriters are empowered with confidence due to certainty of risk. To minimise the underwriting risk, there should be well-defined risk parameters by the underwriters. Predictive analytics is providing statistical reliability and a stable rule-based method for improving pricing decisions. It is also helping insurance companies to perform well at the margin during adverse underwriting environments and at INT. we provide end to end guidance so that our partners effectively manage the dashboard and use the analytics built on an advanced technological model. Book us for the demo!
Are you facing trouble in cross-selling your add-on insurance products? Indeed, it is not a rare challenge that the insurance industry is facing. For the last five years, reports suggested that insurance companies fail to achieve a high conversion rate as they fail to identify customers who are most likely to purchase the product. It is often found that insurance companies, despite having ten products, end up acquiring a customer base for the top two products. It becomes a concerning situation when the rest of the eight products fail to achieve the break-even point. Sustainable revenue growth cannot be achieved if an insurance company fails to increase existing customer “share-of-wallet”. Advancement of Propensity Model In light of the dripping conversion rate, insurance companies are leveraging propensity modelling for precision marketing. The underlying success of cross-selling products to your existing customer base is by offering them a relevant product. In this process, insurance companies aim to raise customer value. Propensity modelling is leveraged to identify the future behaviour of the customer based on past data. We can also define it as a statistical scorecard used to identify the customer segment who will most likely respond to the offer. Let us dig in more to understand the functioning of propensity modelling in the insurance industry. While you were offering motor insurance to your customer, you would also like to offer a few more add on products such as a “zero-depreciation” cover; passenger protects cover, engine and gearbox cover and many more. Now, if you randomly select a customer group and aggressively offer these multiple products, there is a high chance that your effort goes in vain. In these scenarios, propensity modelling comes handy. In propensity modelling, due to its mathematical approach to conclude and predict future customer behaviour, it has been proved to be highly efficient in identifying the right customer group for direct marketing, over here insurers are also trying to achieve growth in upselling and cross-sell of their products. Mechanism of Propensity Model In the propensity model, approaching individual customers are substituted with customer segment with similar behaviour. With a statistical model, AI runs through the complex mathematical data and maps the customer with identical behaviour. In this way, it forms a group of the customer of similar liking. When insurers approach these customer segments by offering them the relevant products, the propensity of buying the product increases and insurers achieve high conversion rates. The propensity model deals with a high volume of customer data and a machine learning model that helps them predict with high accuracy. Therefore, the propensity in the insurance industry works with customer demographic data, their transactional data, psychographic and personality information. What to note when looking for the right propensity model? The true effectiveness of the propensity model can be achieved if it can be advanced with the newer data, can generate more significant predicted outcomes and deploy in a structured manner. Here is the list of the characteristics of the propensity model when we are looking for it to deploy in our insurance industry. Look for its scalability A propensity model must be scalable. In such unprecedented times, customers are coping with the deadly virus, and resources in the insurance industry are limited. It is a waste if the offers are made randomly. Thus, the propensity model must be scalable as it should generate huge volumes of predicted outcomes, enhancing precision marketing. Look for a structured framework When we talk about generating a huge volume of the predicted outcome, we also have to consider that it should be understandable, actionable and measurable. An outcome that fails to give actionable insight makes the framework weak. For the insurance industry to map down the customer segment backed with the data must also help insurers to understand which products should be pitched into the clients. How can it be advantageous for the Insurance Industry? When we talk about the business impact that the propensity model can bring to the insurance industry, we have to take note of the following: Increase Customer Life Time Value Customer lifetime value is the expected relationship with the customer in the future, and micro segmenting customers and deploying cross-sell campaigns from the propensity model can increase it. Increased accuracy in identifying potential targets in cross-selling With cross-sell propensity model, insurers get an accurate picture of the customer preference. Analytic companies can deploy a decision tree model powered with AI, helping deliver transparent pictures to the insurers through a comprehensive dashboard. This unearths the powerful insight for a better direct targeting campaign. Deploy Propensity Model to Cross-sell right product to the right customer In the insurance industry, the risk is vast for both the insurer and insured. Understanding the true value of insurance is cloaked under various risk. As customers worry about complex underwriting process and at the same time, insurers worry about low penetration of lined products. With the propensity model, insurers generate propensity score for customers, which helps in reducing wastage of resources through relevant marketing to the relevant customers for the relevant product. We have seen advancement in analytics and how it has been helping the insurance companies amidst the challenging time. Here are few listed business impacts we have noted among our clients after deploying cross-sell propensity model: A comprehensive data platform has helped in getting easy access to insightful customer data, thus enhancing the effectiveness of cross-selling and up-sell. As the conversion rate increases, the rejection rate decreases; therefore, the cost is optimised as cost per conversion drops. It becomes easier to achieve analytics maturity as now insurance companies are breaking the data silos and getting an actionable insight through data-driven strategy.
