Data Management in Insurance: The Right Digital Solution Optimizes Revenue Boosting Insights
Use RPA and predictive analytics to get the most out of your data
- The use of predictive analytics has transformed the way insurance companies do business
- The system has become automated, data-driven, and predictive to facilitate improvements in pricing, product optimization, customer risk and fraud, and the user experience
- There are intangible value benefits that shouldn’t be underestimated
- Tangible benefits include focused growth, efficiency, and results in underwriting that improve the loss ratio via actionable insights
- The future holds exciting new advances, including data gathering from an influx of connected devices, robotics, and open-source and shared data.
Artificial Intelligence (AI) and Robotic Process Automation (RPA) are transforming the way insurance companies work. These technologies are moving processes from a traditional rule-based system to one that is data-driven, automated, and predictive, providing a better understanding of both the individual customer and patterns that anticipate trends.
Executives must look to insurtech solutions for the tools to optimize data management in insurance. These solutions also offer powerful ways to use predictive analytics for pricing, product optimization, customer risk and fraud, agent fraud and policy manipulation, and to provide an optimized user experience.
CEOs, CTOs, and CIOs can use the insights gained through predictive analytics to spur innovation and create individualized products that also reduce risk. To steer your company toward continued success, you need to know what’s coming. These are the predictive tools that will enable you to tap into the power of today’s technology for a bright future and an increased ROI.
Optimizing Data Management in Insurance
Big-data-dependent solutions tap into a myriad of resources that allow technology-driven insurance companies to outpace their competitors on almost every front.
To ensure fiscal health now and going forward, there must be a digital transformation that capitalizes on business intelligence. For a competitive edge, big data doesn’t just have to be utilized; it must be managed.
The level of analysis needed to process huge amounts of information is too large to be handled manually and requires the use of intelligent digital applications. Those involved in data management in insurance should look to “event stream processing” systems to effortlessly harness data from every sector of their organization’s information ecosystem.
Pricing and product optimization using predictive analytics and AI
By using historical data relating to cost, claims, risk, and profit and applying predictive analytics algorithms to them, insurance companies can optimize processes in ways that are impossible without AI and RPA. For example, premiums can be adjusted on the fly, monitoring variables that help accurately predict risk and using that analysis to instantly optimize the presentation of relevant products to customers.
Customized policies at the individual level then become possible with prepaid and adaptive pricing models based on real-time customer buying preferences, behavioral signals, and predicted risk.
Predictive analytics increase the accuracy of individual projections to establish a more precise understanding of the individual customer. Larger trends can also be foreseen by extracting patterns from the data on a bigger scale. With the real-time optimization offered by AI and RPA applied to data analysis, predictive models are constantly refreshed with updated data, something that can’t be achieved without it.
Underwriting and Predictive Modeling
Cognitive insight is a key benefit of predictive analytics, allowing underwriters to leverage information about the more complex portions of the process. When predictive analytics are run at the beginning of the process, they offer unparalleled visibility into specific risks, such as alerts about additional factors that may have been missed using the traditional underwriting process.
Predictive models and risk analytics mean underwriters have an automated, data-driven result to aid decision-making. Predictive analytics can be used during underwriting to upsell, cross-sell, and focus on customization and selling because the risk assessment is automated with AI and RPA.
Controlling new customer risk and claims fraud
Annual losses related to insurance fraud are estimated at more than $40 billion. In predictive analytics, insurers have a new weapon – they can crunch data to connect user behavior and historical customer records to discover fraudulent actions and suspicious behavior.
RPA, which mimics most human-computer interactions, can validate coverage and analyze claims to detect fraud. In addition, these AI-driven intelligent bots can help construct a litigation package in case of legal action.
The role of RPA in customer satisfaction
Delivering customer service experiences that meet today’s e-commerce expectations keeps satisfaction rates high and churn rates low. RPA and predictive analytics are two tools that help meet that challenge
The underpinning of a good customer experience is an automated process that eliminates manual, error-prone, and expensive procedures. With RPA, processes that once took weeks can be done in minutes with minimal human intervention.
Quickly solve problems and strengthen relationships: RPA automates repetitive, low-value tasks that interfere with core activities and reveals data at the right steps in the process. It lets customer-facing staff focus on increasing customer value, solving problems, and improving relationships. Forrester reports that 44% of data and analytics decision-makers whose companies adopted automation are using RPA-fueled customer service bots.
Get actionable insights: Because RPA reduces manual errors, it leads to the collection of high-quality data. RPA bots also interact with any legacy systems to unearth and extract data that was too labor-intensive to be done by humans.
Improve data management in insurance and analytics: Access to error-free, accurate data from disparate sources improves analytics quality and leads to better decision-making. Another plus is that it improves employee productivity. This insurtech solution gives the ability to mine broader and more reliable data sets that reveal previously undiscovered insights, and these can then be used to create and monetize innovative services.
Predictive analytics and AI enhance the customer experience
Predictable behavior analytics provide the fuel to create engaging and dynamic experiences for customers while improving customer retention by predicting their needs and patterns.
The use of multiple variables in predictive analytics calculates customer satisfaction and boost customer retention, and can be used to:
Identify churn customers: Churn modeling is used in predictive customer analytics to identify at-risk customers and discover ways to retain them.
Anticipate needs to personalize content: A decision tree model can help your company galvanize individual purchasing instincts. Predictive analysis simulates the customer journey toward a specific product. This helps marketing develop strategies that reach customers at the right times on the best channels.
Streamline with self-service: Self-service comes into its own with predictive analytics as well. Using data related to the specific customer’s behavior, geography, social, and account data, AI-enabled chatbots provide an automated and seamless buying experience. Chatbots are rapidly becoming standard in the insurance industry. According to a survey of 1,000 business leaders, they are the leading application of AI used today.
The bottom-line benefits
The use of predictive analytics through an insurtech solution provides intangible value, such as novel answers or courses of action, that will improve internal operations as well as customer engagement, conversion, and loyalty. Not to mention increased data security, improved collaboration, and the availability of data anytime, anywhere.
There are tangible benefits as well, provided by focused growth, efficiency, and results in underwriting that improve the loss ratio via actionable insights.
Growth: Growth can be driven through identification of new market segments, adding profitable customers based on analytic insights, and designing engagement models that are segment-specific to increase customer satisfaction.
Efficiency: Efficiencies are gained through process automation, which can help drive down costs and supply real-time data to underwriting and claims.
Increased customer satisfaction: There is no more important factor in weighing the impact of a digital transformation investment than the customer experience because customer satisfaction is directly tied to a company’s bottom line. Research says that companies that improve their customer retention by just 5% see a 25% to 95% jump in profits.
It’s significant that a recent analysis revealed that an entirely digital carrier outperformed every other insurer’s online conversions by a wide margin, including beating a major rival by a huge margin. The winning carrier has a digital-first mindset that infuses everything from marketing to onboarding to service after the sale, giving customers the seamless experience they want.
To keep customers, insurers must meticulously track the quality of digital engagements, best accomplished through predictive analysis driven by AI. A subpar experience leaves customers less satisfied with their insurers. Seamlessness at every stage of customer digital interaction can make the difference between stable revenue growth and a serious hit not only to revenue but company growth.
Leaders can judge the efficacy of their investment on internal operations by measuring operational efficiency and staff productivity. One insurer moved more than 200 applications to the public cloud and saw a 20% to 30% decrease in the cost of running them.
That type of operational improvement means more resources and employees to devote to product, service, and revenue stream development. An increase in revenue per employee is an excellent gauge of improved productivity and another boost to the bottom line.
In today’s world where digital is ubiquitous, insurers need to rethink the tools they use to measure success – this is where predictive analytics and AI show significant value.
The Future of Technology for Insurers
These technologies have already become part of a huge tech-driven shift in the way insurers do business, but more changes are yet to come. Those who embrace change will be those who arm themselves for ongoing success.
In the next decade, there are three trends that are coupled with or enabled by AI technology, including predictive analytics, that will shape the insurance industry:
1. Increasing data from connected devices
We’ll be seeing a huge increase in the number of connected devices, with experts estimating there will be a trillion of these devices by 2025. The rapid penetration of current devices such as cars, fitness trackers, smartphones, smartwatches, and home assistants will continue. And it will be joined by growing new categories, including appliances, clothing, eyewear, medical devices, and even shoes.
The deluge of new data from these devices means insurers can develop a deeper understanding of their customers, creating innovation in the areas of products, personalized pricing, and real-time service through effective insurance data management.
2. Rise of the robots
Insurers will need to understand how robotics in everyday life will make changes to risk pools, alter customer expectations, and provide new product and channel development. For example, P&C risk assessments will look very different with 3-D printed buildings. AI will help integrate this type of information into risk models more quickly and optimize them much faster than we could have 10 years ago.
3. Open-source and shared data
It’s predicted that open-source protocols will enable data to be shared and used across industries.
Ecosystems to share data for multiple use cases will be created by public and private entities coming together under a common framework of regulation and cybersecurity.
High-performing, profitable insurance companies turn data into actionable insights by unifying disparate sources of information from inside and outside their company. They place themselves in a better position to conduct the comprehensive data analysis that improves customer service, data security, data accuracy, and the ability to capitalize on emerging trends.
Usable data is, and will continue to be, generated by e-commerce transactions, bill payment, form submissions, phone calls, website visits, chat messages, and text messages.
Truly, the future is now. Equipping yourself for the changing insurance ecosystem is paramount, and one of the sharpest tools in your digital transformational chest is agility.
As Amit Zavery, VP and Head of Platform, Google Cloud said, “Think of digital transformation less as a technology project to be finished than as a state of perpetual agility, always ready to evolve for whatever customers want next, and you’ll be pointed down the right path.”
RD Global is Your Partner in Digital Transformation
RD Global empowers insurance companies with high-impact technology solutions rooted in a 5-star customer experience. Our unrivaled team of in-house technical experts can solve your most complex digital challenges with state-of-the-art custom insurtech software to power your digital transformation.
We’ll help you to reach your goals through the use of data management and AI with predictive analytics that provide the insights you need.
Only the digitally strong will survive and prosper. Schedule a discovery call to learn how RG Global can partner with you for success now and in the future
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