Use Data Analytics to Power
Your Claims and Underwriting Services
Insurance companies are experiencing rapid digital transformation as new mobile-first and customer-centric InsureTechs flood the market. The industry is responding quickly with new products, processes, and technologies to compete.
Additionally, new technology companies are emerging with packaged-applications to automate banking and insurance business processes.
Splice Machine Use Cases: Insurance
Splice Machine applies the power of scale-out architecture and machine learning in the insurance sector across a variety of use cases.
Dynamic Product Configuration
Once your customer is in a buying motion, how can you get them to complete an application for an insurance policy? Insurance products are complex with a number of available options and choices that can be used to customize the base product.
Using machine learning, you can present the most likely packages that are pre-configured to the client need leading to higher conversion rates.
Optimize Marketing for Customer Acquisition And Retention
Putting the right message, offer, or service in front of the right customer at the right time. Communicate with a personalized and consistent message to customers with different motivations in order to convert them into a buying motion.
Computing Risk and Optimal Pricing
Once you get the customer engaged in the underwriting process, how can you accurately assess the risk of underwriting a policy and how do you price the policy properly to reflect that risk? Machine learning can use data from many sources in addition to your own underwriting history to augment traditional statistical and actuarial methods with experience of how customers turned out.
Detecting Fraud and Money Laundering
One of the most significant challenges every insurance company faces is to fight fraud. Using exogenous data at scale and predictive methods enable you to uncover missing or erroneous underwriting information in the underwriting process to detect fraud.
Detecting fraudulent applications and claims is a constant battle where data scientists must continuously experiment to evolve and improve models.
Another area where ML has been successfully applied is in predicting litigation. Litigation exponentially increases losses for which insurance companies must be financially prepared. With predictive methods companies can determine what is the likelihood claims will go to litigation and then determine how to reserve funds and assemble legal teams.