Predictive analytics transforms insurance decision-making
CAPE TOWN – Driven by data, insurers must be able to gain meaningful insights and act on them to remain competitive. Kelly Preston, data analytics manager at SilverBridge, believes predictive analytics is a valuable resource to drive this data differentiation.
While predictive analytics is not able to tell the future, it does empower decision-makers with the means to extract information from existing data sets. This can be done to determine patterns and forecast what is likely to happen. Furthermore, it includes risk assessment and scenario planning, all valuable elements for an insurer.
“Adding impetus to this dynamic [data] environment, is the increasing popularity of machine-learning, artificial intelligence, and even smart data discovery capabilities previously unavailable to the insurer. All these technologies work to unlock the hidden potential that exists in the vast amount of unstructured data at insurers,” she says.
On the one hand, predictive analytics can be used in appraising and controlling risk in underwriting, better manage product pricing, and improve the claims process. On the other hand, it can also drive efficiencies inside the business for the insurer to enhance the productivity of staff thereby enabling them to fulfil more strategic functions inside the organisation.
“All these result in improved customer satisfaction. Whilst actuarial science has always been used to a great effect in insurance, predictive analytics introduces an additional layer of insight to the process. This involves pro-actively identifying things like the progression of client injuries and illnesses so the necessary steps can be taken instead of having clients manage the process.”
Other industries have been using predictive modelling for marketing purposes and gaining better insights on who their customers are and what solutions they need. The same can be done in insurance. By improving solutions on a more individual level, not only will customer service improve, but the reduction in churn to competitors will also be significant.
From a security perspective, predictive analytics can better detect potential fraudulent or duplicate claims resulting in reduced costs from an insurance perspective. This data analysis can identity trends and anomalies that traditionally might have slipped through the claims process due to human error or a lack of system integration.
In a 2010 report, Deloitte Consulting cited credit scoring as the classic example of predictive modelling in this new era of business analytics. Moreover, it wrote that the use of credit and other scoring models represented a shift in actuarial practice.
“This pointed to the importance of behaviour in these models that represented a change in the traditional rating variables. These more modern, predictive techniques have become vital tools in overcoming traditional actuarial problems such as estimating morality, setting loss reserves, and establishing classification ratemaking schemes according to the Deloitte report.”
The predictive model has become instrumental in highlighting the next wave of insurance innovation.
“Insurers who are using predictive analysis are not only more agile in developing solutions and making decisions based on market conditions, but they are in a strong position to differentiate themselves from their competitors,” she concludes.
BUSINESS REPORT ONLINE