The Evolving Landscape of Insurance Analytics
Empowering insurers to identify risk and severity of each claim, and enabling them to channelize their efforts towards mitigating the severity.
Traditionally a data-driven industry, the tech intervention in insurance, over the past decade, has changed the dynamics of the industry drastically. The rapidly evolving insurance ecosystem can no longer depend upon conventional ways for underwriting claims, offering hyper-personalized insurance covers, and preventing frauds. An expanding customer base and the ever evolving customer need is one of the reasons behind the shift.
Anyone who fathoms the potential data-driven insights, also understands that data is a gold mine for businesses across sectors. It has the potential to bring a seismic shift in the insurance sector too, giving rise to Insurance Analytics that helps insurers get the most out of data.
With the help of artificial intelligence and machine learning, insurance analytics will enhance the end-customer experience through data curation, insight-based transformations, and optimize business outcomes from structured and unstructured data.
Future tech will aid in revealing the behavior patterns, demographics, and characteristics of customers for insurers to target marketing potential. Insurers can then further customize their insurance plans that are best suited and more personalized for individual customers.
The use of data mining, edge computing, and augmented and predictive analytics will pave a new path for a productive and future-ready insurance landscape. According to the McKinsey report ‘Insurance 2030’ – The Impact of AI on the future of insurance estimated that AI and its related technologies will have a seismic impact on all aspects of the insurance industry, from distribution to underwriting and pricing to claims. Here is how tech intervention is changing the insurance landscape all throughout.
A Stitch in Time Saves Nine
Predicting losses for the policies is an essential skill to have for insurers of the future. Identifying these risks early in time empowers insurers to strategize and execute preventive measures before the risk occurs. With machine learning, insurers can save on a lot of resources spent on underwriting and streamlining the whole process. Insurers can
use rich analytics and pattern prediction capabilities to detect credit risks with higher accuracy, a perk that manual efforts cannot promise.
Expediting Claims Processing
The first step to transforming claims processing is to map the customer’s journey and derive its relevance in larger customer experiences. Using legacy models to process claims is outdated as its manual-intensive and lacks quality too. Using ML-driven analytics will allow insurers to cut down on the time and money spent on routine claims settlement processes.
Automation of application processing involves extracting valuable information from a large volume of documents and capturing the relevant information to expedite claims processing. This reduces human errors and is a cost-effective way to process claims. According to a McKinsey study on digital disruption in insurance, automation can reduce the cost of a claims journey by as much as 30 percent.
Telematics insurance comprises innovative car insurance products that are fitted into vehicles to monitor speed, location, time, accidents, and other driving data. These devices have GPS, sensors, and a sim card that transmits data to the insurance companies.
To stay relevant and competitive, insurers of the future will have to shift their focus from just customizing products to providing solutions
Minimize Claims, Minimize Losses
Insurance companies have always been interested in predicting the future to help save losses by minimizing their claims. Today’s tech innovations can even help them predict natural disasters to alert their insured customers in danger and, thereby, minimize their claims. Even IoT-enabled smart home tech can send real-time alerts in case of damage.
Deep learning is a powerful tool made of machine learning algorithms that use artificial neural networks to understand and leverage patterns in data to create predictive models that can accurately forecast future outcomes. AI and Predictive Analytics have the potential to drive optimal outcomes by identifying the most complex and costly claims and even ranking them accordingly.
When insurers get an overview of the risk and severity of each claim, it is easier for them to determine where to focus and what steps to take to mitigate the severity.
To stay relevant and competitive, insurers of the future will have to shift their focus from just customizing products to providing solutions. Technological advancements have paved the way for umpteen opportunities for insurance companies to offer unique products and services in the most cost-effective and exceptional way.
The advancement of technology comes with its own challenges. Cyber security is a huge threat to insurers and by gaining deep insights into emerging frauds and credit risks, these threats can be tackled with an effective strategy.
Analytics, AI/ML, IoT, Blockchain, API, wearables, and Telematics are emerging technology trends that should be leveraged to stay competitive. Insurance companies need to strategically invest in these tools, and even in talent that will play a significant role in the execution and effective use of analytics strategies.