Developing Early Warning System to Identify Relevant Events in Unstructured Data
Swiss Re is a leading player in the global reinsurance sector. Its role is to anticipate, understand and price risk in order to help insurers manage their risks and absorb some of their biggest losses. As one way to stay ahead of the curve and provide thought leadership to its clients, Swiss Re is developing an early warning expert community platform based around big data and natural language processing. The platform is intended to work on the front lines, to detect events that have the potential to change our view on risk drivers and to help us make business decisions in shorter timescales.
Key Takeaways: 1) The traditional methods to identify relevant events become unreliable when information volume rapidly increases; 2) Uncoordinated views pose a challenge in taking proactive and strategic actions to manage risks; 3) Early warning expert community platform leverages new data techniques to identify relevant signals and helps integrating experts in a more joined up process.
Nataliya Le Vine is a data scientist at Advanced Analytics Center of Excellence at Swiss Re, bringing machine learning and AI to drive the technology transformation in insurance. Over the last decade, she worked in academia, tech and insurance industries both in EMEA and Americas with a core expertise in predictive modeling and machine learning.