The increased availability of data is opening up a new world of analytics to insurance companies. There are both new sources of data and also increased volumes of information, and together, these are being used to generate insights and influence decision making. This post, the second in a series on analytics and customer-centricity in the insurance industry, examines how analytics is being used to improve understanding of customers, and therefore offer new and better insurance products and services.
Instant insurance – and more
At first glance, the most obvious place for the use of instant insurance is in underwriting under the umbrella of “instant services.” There are, however, many other areas of insurance business that can also use analytics and real-time services to improve the customer experience and differentiate in quality terms. These include:
Marketing and digital
New techniques of behavioural analysis can be used to examine both online and mobile customer behaviour, and also perform tailored instantaneous offers for customer engagement.
Sales network and distribution
Analytics supports the use of an omnichannel approach, ensuring a seamless experience for customers across all channels. The sales force and agents can be given access to analytical tools that help them propose the most suitable product and offers for each individual customer.
Underwriting
Analytics enables the generation of real-time quotes, including the ability to customise the discount and optimise the proposed price. It can also be used to help to identify a potential customer as a possible fraudster and prevent fraud from the first step of the customer application process.
IoT division and telematic services
Analytics can be used in motor insurance, for example, to identify driving profiles based on customer behaviour. It can be used even before a claim has been made to make an immediate assessment of the damage at the time of an accident and enable pre-calculation of possible costs for damages incurred. It can also be used to provide proactive advice to customers, based on their behaviour, to try to prevent accidents.
Claims management
Analytics is already in use to analyse the severity of incidents from images provided by the customer and make a real-time estimation of expenditure. It is helpful in speeding up the claims process by channelling particular types of claims to the most appropriate management route and applying predictive models to identify accident characteristics and possible critical issues. This reduces claims handling costs, as well as time frames, improving the customer experience and lowering the risk of fraud.
There are multiple applications and areas of use for analytics and instant services. These require the adoption of new technologies and changes in organisational processes.
Fraud detection
Analytical tools and models have been used to help identify cases of potential fraud and also to manage these suspicious cases, prioritising alerts based on the analytical score and analysing relationship networks to identify potential organised crime.
Compliance
Analytics has also been used to improve the quality of “onboarding” of customers, for example, to screen for anti-money laundering purposes, reduce false positives and improve the efficiency of the management system for this data, ensuring the appropriate protection of sensitive information.
Customer care
Analytics has a wide range of use in customer care, including to automatically manage customer calls and complaints, find correlations between complaints and requests for information, and analyse dynamic customer satisfaction surveys, such as Net Promoter Score, that allow instant analysis of customer feedback to improve customer satisfaction. Models can be used to compare the quality of proposed products and services and assess the efficiency of various internal processes.
How to get started?
There are, therefore, multiple applications and areas of use for analytics and instant services. These require the adoption of new technologies and changes in organisational processes. These changes must be introduced in a way that is not disruptive to customers and may require shorter or longer adoption times. But is there a road map to introduce analytics into insurance companies that will reduce the adoption time and promote a cultural organisational change with the least violent impacts possible? Our experience at SAS suggests that the answer is yes, and my next post explains more about this.
