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How do insurers convert data to value
- 1. How do insurers convert data to value? Pedro Ecija Serrano ¦ www.datadriven.ie ¦ pedro_ecija@yahoo.es
- 2. Introduction What insurers need to convert data to value: • Experience • Talented workforce • Statistical and actuarial knowledge • Technology
- 5. Shopping • Marketing analytics: how effective are my marketing campaigns? • Sentiment analysis: what is the public’s opinion of my brand? • Propensity modelling: to increase cross-selling and better target your customers. • Using open data: such as the Census, to supplement your knowledge of customers.
- 6. Example of Sentiment Analysis Analysis by Luc Gendrot: http://rpubs.com/lgendrot/sentiment
- 7. Example of Propensity Modelling
- 9. Buying • Price Optimisation: combining price elasticity, customer behaviour and expected claims to optimise business goals. • Customer Lifetime Value: to know which customers are more valuable to the company. • Detecting Underwriting Fraud: or accidental non-disclosure. • Compliance Analytics: for early detection of conduct risk, Politically Exposed Persons, etc.
- 10. Example of Price Optimisation
- 11. Example of CLV Custora’s CLV analysis for online retailers in the US by channel and geographic location. Taken from its 2013Q2 E-Commerce Customer Acquisition Snapshot: http://blog.custora.com/custora-content/uploads/downloads/2013/06/Custora_EcommSnapshot_2013_Q2.pdf
- 13. Claiming • Claims Fraud Detection: using classification models or network analysis. • Claims Submission Analysis: to capture important information in claims forms. • Claims Triage: to allocate complex claims to more experienced handlers, also reducing operational risk. • Telematics: to price motor insurance policies according to individual driving patterns.
- 14. Example of Network Analysis Visualisation of Al Qaeda terrorist network by Sentinel Visualizer http://www.trackingthethreat.com/SocialNetworkAnalysis/index.asp
- 16. Renewing • Lapse analysis: using statistical models to identify customers likely to lapse and find patterns in lapses. – Survival models used for long term life insurance. – Cohort analysis used for annually renewable.
- 17. Example of Cohort Analysis 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 1 2 3 4 5 6 7 Cohort Retention 2010 2011 2012 2013 2014 2015 2016 60% 65% 70% 75% 80% 85% 2010 2011 2012 2013 2014 2015 2016 Year 1 Retention by Cohort
- 18. Not enough data or too much data? • Big data vs. Right data: collect data with a goal, or size will only slow you down. • Relevance is more important than quantity. • Data quality saves time: 80% of data analytics time is data cleaning and preparation. • Exercise careful judgement when using public data. Regulation will likely apply and the public may not like you using their data.
- 19. Technology • NoSQL, Hadoop, etc. to deal with massive amounts of data. • Tableau, Power BI, etc. for visualisation. • Cloud Computing (AWS, Google Cloud, Microsoft Azure) • GPU Computing. • Artificial Intelligence (IBM’s Watson, Theano, Google’s Tensorflow)
- 20. Summary • Insurers have much data but most of it is unstructured. However, it can be used and can be valuable. • Abundance of publicly available data. • Technology develops very fast. Converting data to value is becoming easier and cheaper. • So much can be done that insurers must set clear data goals, aligned with their business strategy.
- 21. Next Five years • Insurers successfully engaging with digital customers. • Management understands how data analytics adds value. • Insurance companies ready to change operational models to become data driven. • First Internet of Things insurance?