Insurance Analytics Use Cases & Benefits – A Business Perspective

In the digital era, every sector needs to leverage the potential of untapped data. One such industry that holds a wealth of data is the insurance business. It comprises customer information, product details & pricing information, underwriting practices, and much more.

You would agree that the rising competition, stricter regulatory & privacy policies, and ever-changing customer trends are driving insurers to digitize quickly. And insurance companies can stay competitive by not just focusing on effective data management but also converting it into useful insights for data-driven decisions.

Through this blog, we’ll talk about the power of data analytics, the benefits it offers and real-life use cases of analytics for insurance companies.  Read on!

Insurers are increasingly using the power of data to identify new growth opportunities using customer information and, in a way, protecting their businesses from potential risks. The use of business analytics in insurance help get insights about potential markets, competitors, customers, risks, regulations & plans for natural disasters.

Listed below are some of the case scenarios that highlight how the companies are leveraging this paradigm shift to achieve their business goals.

Pricing and Service Optimization

• The ability to examine risk and determine price policies becomes a central point of value creation for insurance companies. Data analytics in insurance helps actuaries to build policies better suited to dynamic business needs, market conditions, risk concentrations, and patterns.
• By analyzing consumers’ behavior, lifestyles, pricing sensitivity, and buying preferences, you can optimize and showcase more appropriate insurance products.
• Previously, it was difficult to customize policies at the individual level. However, insurance providers can now use pay-as-you-go and more dynamic pricing models based on clients’ behavioral signals, predicted risk factors, and shopping preferences.

Fraudulent Claims Detection

• The fraud cases in claim processing are a major challenge for the industry. According to various studies, 10% of all claims turn out to be fraudulent.
• Improvements in technologies have made it possible to detect suspicious claims, fraudulent activities, and behavioral patterns by leveraging predictive analytics that is subjected to further investigation.
• Whenever a claim made by a user with a history of false claims is detected, the fraud prevention system halts the claim process and suggests case investigation. Predictive modeling techniques are applied to analyze patterns in fraud and the screening of false claims.

Risk Management

• Advanced data analytics helps conduct a real-time risk analysis that enables companies to become resilient in a volatile scenario to detect, analyze and mitigate the situation and plan corrective action.
• Insurance data analytics provides the ability to integrate all your data from internal and external sources in one system to conduct comprehensive risk assessments. For instance, historical data can be collected from credit agencies, customer e-mails, user forums, third-party vendors, and social media.
• Previously, it was challenging for underwriters for evaluating the risk of insurance policies by relying on specific, predefined guidelines, basic statistical models such as profiling and scoring models, and their intuitions. Data analytics services help detect risky claims and forward them before the authorities for further analysis. Predictive analytical models assist underwriters to make informed data-driven predictions about clients’ risk profiles with improved accuracy.

Personalized Marketing Campaigns

• Advanced analytics extract valuable insights from the vast amount of database that comprises details such as user’s demographic data, hobbies, lifestyle details, interests, belief systems, and more. This helps insurance providers devise targeted campaigns for their customers through personalized offers, policies, loyalty programs, and recommendations.
• The acquired data from various digital platforms helps create a hypothesis/model to craft personalization and marketing strategies. This leads to deciding on personalizing offers, policies, prices and even marketing ads. A total of the same helps to acquire customers and in turn increases supports the insurance rate-ratio of a company.

Lifetime Value Prediction

• Customer lifetime value (CLV/CLTV) is one of the key stats that represent the profit generated by the business over the entire span of the relationship with your company. Insurance marketers can also get insights from CLV data to segment their existing user base and better understand the performance of marketing campaigns in real-time. Data analytics provides insights on revenues gained and expenses incurred predicted over the entire future relationship with a particular customer.
• Customer behavior data is used to forecast the CLV to predict the customer’s profitability for the insurance companies. This behavior-based model is utilized to forecast cross-buying and retention.
• CLV prediction provides the client’s informative insights that enable forecasting the possibility of customer behavior and attitude, policy maintenance, or a policy surrender.

For organizations managing high volumes of data, visual representation brings insights to life. It is a collation of information components across accident statistics, policyholders’ personal information, along with supporting third-party sources to prioritize different risk categories, prevent fraud losses, and optimize expense. This allows brokers and underwriters to make fast, informed decisions from their preferred location or device, speeding up decision-making overall.

Smart analytics solutions help organizations to aid in making decisions across various areas of their business. The key insurance data analytics benefits include:

• Faster Claims Analysis: Advanced analytics enables the logical connection between data and effective action. With the growing adoption of automation, changes in policies, and increases in claim data, there is an enhanced need for advanced claim analytics.
• Precise Risk Identification: Insurers conduct customers’ information verification while assessing the risks. With apt data management techniques one can create various risk classes and customers are segmented into different classes based on their profile. You can even upload social media information and criminal records if any. All this data can then be put on a solid data viz dashboard which can give you insights and clear identification of high, moderate, and lower risk profiles.
• Product Profitability Analysis: It helps investigate individual insurance products at a deeper level by analyzing the product performance and profit by distribution channel, geographic region, customer segment, and other factors. Within a BI system, it can check in real-time the product’s premium update, its lapsation, claims, and other related details.
• Revenue Comparison: Conversion ratios, expenses ratios, and retention ratios to conduct an in-depth analysis of a single product can be put on a dashboard to have thorough insight on performance. Staying abreast of these parameters helps avoid underwriting overruns and remain profitable.
• Enhanced Process Automation: Optimizes operational processes by automating data entry, compliance checks, repetitive tasks, and more. By leveraging technologies, such as RPA you can automate & improve back-office processes and customer-facing services.
• Effective Decision Making: Data visualization and decision-making tool can provide report insights to empower marketing by tracking sales & services, claims processing, underwriting, product development, and other aspects of an insurance business. There is an abundance of valuable information that insurance companies can glean by using different data visualization techniques.
• Improved Customer Satisfaction: With analytics, you can capture prospective clients’ feedback digitally, store it, and derive actionable insights on their buying behavior and lifestyle habits. Utilizing the data, you can plan better to provide them with a better experience. This is where data management & BI systems will help most.

The insurance business means a huge amount of data and huge opportunities. While the digital opportunities are abundant, the focus should be on harnessing the big data to cut down on claim handling time & costs and eliminate potential fraud.

Using Big Data analytics in combination with data visualization, the organizations can now offer personalized policies, with a risk assessment to prevent fraudulent activities and increase the efficiency of internal processes.

Rishabh Software’s specialists can help empower insurance companies globally with a range of data analytics services to boost business outcomes and drive competitive advantage. We help create data-driven investment strategies, understand market scenarios, capital management tactics, and more to make informed decisions