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Technology is money. If you can create insurance software that can improve your risk assessment, speed up claims processing, or personalize an insurance offer to your customer, you're one step and a few billion dollars ahead of your competition.
Progressive was an early adopter of telematics and usage-based insurance pricing models. In the late 1990s, they introduced Autograph, a program that used in-vehicle tracking devices to monitor driving behavior.
In 2015, John Hancock launched a pioneering program that incentivized policyholders to live healthier lifestyles by tracking fitness data from wearable devices.
In this article, we'll show you, how data analytics grows more and more important in the insurance industry, and how Decerto's Agent Portal, insurance software, can help you embrace it.
Risk and Underwriting
For years, the insurance industry has used historical data and actuarial tables in risk evaluation process to set premiums. But with big data analytics solutions the process is being flipped on its head, so insurance organizations can make more accurate predictions and fairer pricing. By analyzing massive datasets that include customer demographics, behavior, claims history and external factors insurance company can get to know individual risk profiles.
Digitized underwriting can significantly boost an insurer's performance. Even industry leaders can experience substantial improvements, such as a 3 to 5 percentage point reduction in loss ratios, a 10 to 15 percent increase in new business premiums, and a 5 to 10 percent jump in retention rates for profitable segments. This is by identifying patterns that correlate certain behaviors or characteristics with higher claims likelihood.
For instance, predictive modeling analytics can flag risks associated with certain occupations, lifestyles or even driving habits. This means insurers can price more accurately, lower premiums for lower-risk individuals, and adjust premiums for those with higher-risk profiles. Fairer pricing benefits consumers who pay less and insurers who mitigate losses by having premiums reflect the risk they are taking on.
By using advanced algorithms and machine learning models, insurers can automate and simplify the underwriting process, reduce manual effort, and increase efficiency. This speeds up the decision-making process and minimizes the opportunity for human error, resulting in more consistent and objective risk assessment, competitive advantage, and overall business growth.
Customer Experience In Insurance Industry
The insurance sector is becoming more customer-focused, and advanced data analytics is the key to delivering personal and delightful experiences. By analyzing customer data, insurers can get valuable insights into individual preferences, needs, and customer behavior. This means they can tailor insurance products, insurance services, and communications to each customer’s unique needs.
An Accenture study found 80% of insurance customers are more likely to buy a product or service from a provider that offers personal experiences.
Known use cases of analytics are:
- Lemonade: Uses AI-powered chatbots and machine learning algorithms to handle claims processing and customer inquiries, significantly reducing response times and increasing customer experience.
- Allstate: Employs predictive analytics to identify customers at risk of churning and proactively engages with them to retain their business.
- Geico: Utilizes big data to segment customers and offer tailored insurance products, maximizing customer lifetime value.
- Oscar Health: Leverages data analytics to offer personalized health insurance plans and proactively engage with members to improve their health outcomes.
Analytics can also be used to simplify the claims process, which is the most important part of insurance customer satisfaction. A McKinsey report found insurers who have adopted digital claims processing have seen 25-30% reduction in claims processing costs and 30-70% increase in customer satisfaction.
By automating claims intake, assessment and settlement insurers can reduce the time it takes to process claims and get faster payouts and higher customer satisfaction. Analyzing claims data can also help identify trends and patterns and allow insurers to pre-empt issues and improve the overall claims experience for their customers.
Fighting Fraud with Analytics
Insurance fraud is a persistent and costly problem for both insurers and honest policyholders. According to the FBI non-health insurance fraud costs an estimated $40 billion a year and everyone pays more in premiums – each American family pays $400 to $700 additionally .
But insurance data analytics is emerging as a powerful tool in the fight against fraud. Advanced algorithms, machine learning and anomaly detection can analyze vast amounts of data to identify patterns and anomalies that may indicate fraud.
For example machine learning algorithms can be trained on historical claims data to recognize patterns of fraudulent behavior. By analyzing variables such as claim frequency, types of injuries, medical provider networks and social media activity these algorithms can identify suspicious claims that need further investigation.
Simplify and Improve Operations
Beyond risk assessment, customer experience and fraud detection, data analytics is also transforming the operational efficiency of many insurance companies. By applying analytics to various processes, insurers can simplify workflows, automate manual tasks and optimize resource allocation. This reduces costs and frees up time for insurance professionals to focus on more complex and strategic initiatives.
Claims is one area where analytics can make a big impact. By automating tasks such as data entry, document verification and initial claims assessment, insurers can reduce processing time and improve customer satisfaction.
A McKinsey study found automation could reduce claims processing costs by 30%. This is by automating repetitive tasks, reducing manual errors and faster decision-making.
Analytics can also be used to optimize policy administration processes such as policy renewals, premium calculations and customer communications. By identifying inefficiencies and bottlenecks insurers can simplify these processes and reduce costs and improve customer experience.
And analytics can also be applied to marketing and sales. By analyzing customer data insurers can identify target segments, personalize marketing campaigns and optimize lead generation. This data driven approach can improve marketing ROI and lead to more sales and customer acquisition.
Artificial Intelligence in Insurance Business
While data analytics has already made a big impact in the insurance sector, the rise of artificial intelligence (AI) will accelerate this even further. AI with its ability to learn from data, identify patterns and make predictions will change every part of the insurance value chain.
In customer service, AI powered chatbots and virtual assistants are already being used to handle routine queries, provide instant quotes and guide customers through the policy selection process. This improves customer experience by providing 24/7 support and reduces the workload on human agents so they can focus on more complex tasks. A Juniper Research study predicted chatbots will save over $8 billion per year by 2023 in the insurance industry alone. Aforementioned Lemonade uses chatbots to process claims as well.
AI is also changing the claims process. By using natural language processing (NLP) and image recognition AI powered systems can automatically analyze claims documents, extract relevant information and assess the validity of claims. This can reduce the time and resources required to process claims and get faster payouts and better customer satisfaction. AI algorithms can also detect fraudulent claims more accurately and reduce losses for insurers.
And AI is also being used to improve risk assessment and underwriting. By analyzing more data sources such as social media, news articles and even weather patterns AI algorithms can provide a more complete risk profile and allow insurers to make better underwriting decisions. This means fairer pricing for consumers and better risk management for insurers.
The Agent Portal: Modern Insurance Software
Here at Decerto, we believe that technology is the future of every business, especially as data-driven industry insurance business. Advanced analytics at the highest level may mean, that improving one piece of the process just by 1%, results in billions of revenue.
360° Customer View
The Agent Portal consolidates customer data from various sources, providing agents with a comprehensive view of each client's insurance needs and preferences. By understanding customer profiles, insurance agents can tailor personalized offers, increasing the likelihood of successful sales and cross-selling opportunities.
Real-time Sales Dashboards
The insurance software's intuitive dashboards offer real-time insights into sales performance, highlighting key metrics like conversion rates, policy types sold, and revenue generated. This data-driven approach allows agents to identify trends, adjust strategies, and optimize sales efforts for maximum impact.
Data-driven Lead Generation
This insurance software can be integrated with lead generation tools, providing insurance agents with a steady stream of qualified leads based on specific criteria. By targeting the right prospects, agents can focus their efforts on high-potential clients, improving conversion rates and overall sales performance.
Post-sales Analytics and Customer Retention
The Agent Portal, insurance software, doesn't stop at closing deals; it continues to provide valuable insights into post-sales activities. Agents can track policy renewals, claims history, and customer satisfaction levels, allowing them to proactively address issues and retain customers for the long term.
Simplified Reporting and Data Analysis
The Agent Portal's intuitive interface simplifies the process of generating reports and analyzing sales data. Insurance agents can easily identify areas for improvement, track progress towards goals, and make data-driven decisions to drive revenue growth.
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