Application of data analytics in insurance – from hype to reality

Application of data analytics in insurance – from hype to reality

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Insurance professionals collect a huge amount of data from their customers.

However, the insurance sector is currently lagging behind in data analytics applications and needs to up its game to compete with big tech companies and disruptors such as insurtech companies. On a strategic level, most insurers aspire of becoming a costumer-centric, digital and data-driven organisation.

On the other hand they are facing a number of obstacles that make it difficult to achieve their aspiration in practice:

  • Reluctance to change;
  • Working in silos;
  • Legacy systems, quality and availability of data;
  • Data protection issues;
  • Lack of talent and expertise;

These obstacles have to be addressed and overcome in order to achieve the strategic goals. The presentation will cover applications already implemented by insurance companies with PwC´s help, including:

  • Non-life pricing - machine learning models used to automate and improve accuracy of pricing
  • Claims analytics
  • fraud detection and insights into drivers of claims
  • Telematics
  • smart motor insurance products using telematics
  • Costumer churn retention - machine learning model used to develop a scorecard predicting customer propensity to lapse
  • Costumer lifetime value
  • reliable indicator produced for CLV across the business

In addition to the specific applications mentioned above, we will also explain how an open-source and modular big data analytics platform can help insurers to shorten the path to measurable insights and to make build apps easy. Such a platform is capable of tilising data from multiple systems efficiently while maintaining data privacy. The advantages and practical considerations of setting up digital innovation hubs or centres of excellence will be covered as well. Such an entity can be a key enabler for insurers in their journey to transform to a costumer-centric and data-driven organisation. Finally, we will explain how actuaries can contribute to data analytics projects.

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