How Digital twin technology can be leveraged in insurance industry
Digital Twin technology has been around for decades. The concept is believed to have its origin at NASA when simulations were carried out to bring back the Apollo 13 astronauts. Gartner defines digital twin as a digital representation of a real-world entity
or system. Simulations of what-if scenarios can be performed on this digital/virtual copy of the asset in deriving at the next-best action. Machine Learning models can perform predictive and prescriptive analytics on this digital copy which can then be applied
back to the actual asset. Manufacturing industry has been harnessing the benefits of Digital Twin for decades. Insurance sector can also benefit enormously by leveraging Digital Twin Technology. Recent catastrophic losses in US had an overall negative impact
on P&C insurance. Combined Ratio has not been good for P&C insurers in 2022. The impact is due to the increased repair cost and the supply chain disruptions created during the pandemic. When most of us stopped commuting to work, beginning of the pandemic insurers
had also refunded to its policy holders.
Insurance industry has always been a data rich sector, enormous amount of data is collected during the rating, underwriting and claim process at different stages of policy’s life cycle. With the Digital Twin technology, insurers can apply artificial intelligence
and machine learning to simulate “what-if” scenarios on data in insurers landscape. This aids in making data driven decisions. The favorable next best action can then be applied on the actual process for providing recommendations or improvements.
Here are some ways how P&C insurers can address current challenges and benefit from Digital Twin Technology-
Fraud detection: Fraud detection helps in reducing insurers loss ratio. Fraud occurs in about 10% of property-casualty insurance losses in US (
1 source: insurancefraud.org). Using the Machine Learning algorithms on historic data, insurers can check the inflated claim amount by policyholders. For example, when there is a claim for replacement of
an automobile part, ML model can let us know if the part can be repaired versus replaced. The images of prior similar losses can be accessed by the Machine learning model to derive at this conclusion. In the event of recent catastrophic claims, AI/ML models
can also flag the fraudulent claims filed by a policy holder who is not in the disaster zone.
Claims: Digital Twin can aid in reducing the overall claims settlement cycle time. ML models can identify the delay in the process and simulations can help in fixing the inefficiencies. Effective utilization of the claims data available within the
insurers data ecosystem can also help to process the low impact and high frequency claims quicker. It can also speed up claims processing through simulation of accident scenario, assessing impact to the property which reduces the time to investigate. All these
can aid in improving the overall operational efficiency of the insurer.
Customer retention: Insurance product has become a commodity. Existing policyholders can shop around and switch to competitors in few minutes based on just the pricing. In US, average retention rate in insurance industry is 84%. It costs seven to
nine times more for insurers to attract a new customer than to retain the existing customers. (2 source:
Independent Insurance Agents of Dallas). Historic retention data can help the digital twin to identify customers who are more likely to churn using ML models. A customer who had a pleasant experience during his claims process has more propensity to renew
his policy when compared to a dissatisfied customer. Factors like increase in premium during the policy holder’s tenure, Customer lifetime value can be utilized by the model to predict if the customer will renew their policy. Digital Twin can derive a propensity
score of the existing policyholders and provide recommendations as next best actions for retention. This will increase the overall renewal rate for an insurer.
Customer satisfaction: All roads lead to Rome. Fraud detection, customer retention, improvement in claims processing aims to improve overall customer experience. Digital twin technology will help in predicting customer behavior and enabling high
gain customer interactions. Machine learning algorithm can enable the customer centric product design with the effective usage of historic data. A product can be priced rightly or further analyzed to understand why it did not perform well in the market. This
will enable successful launching of the new product with the insights of the buyer habits and risk profile.
Marketing and Sales: Existing data in the insurer landscape can be leveraged to find the cross-sell upsell opportunity. This will increase revenue for the insurers through cross sell and upsell. ML models can tap into the existing customer datasets,
their buying patterns and other touch points to identify a cross-sell upsell opportunity. Main challenge for an insurer is that majority of the quote does not get converted to a policy. Based on the historic data, twins can identify the gaps and provide insights
to increase the quote to policy conversion ratio.
Today, there are many solutions based on digital twin technology that are readily available in the market which insurers can procure to realize the benefits. Tapping into digital twin technology will provide insurers competitive edge along with improved
KPIs. Digital twin technology can be leveraged for achieving a hyper personalized customer service, simulation of customer personas, profitability and topline growth.