Big data versus good data: Using ML to plug data gaps in insurance

Significant founding of enterprise machine intelligence for the past decade has not always resulted in a measurable return of investment in the insurance landscape, as the profitability of large and dynamic datasets is often hampered by insufficient comprehensiveness, lack of explainability and increased information management complexity.  We look at these gaps, and we explain how the latest advancement in ML can help insurers close them implementing ML-driven data augmentation, building intelligible ontologies and allowing accurate risk modelling based on accessible, curated datasets. 

Insurers are using technology to access relevant, granular data to steer risk assessments and shape better products and services. Key approaches include:

• FIXING DATA: Out-of-sample predictions of missing values with machine learning algorithms can help close data gaps in a wide range of insurance specific dataset
• MAKING DATA ACCESSIBLE: ML-driven data curation algorithms can transform non-intelligible data-streams into accessible and explainable ontologies that underwriters can leverage to take informed decisions 
• MAKING DATA USEFUL: ML-curated data allows insurers to move from classic Generalised Linear Models to more accurate, data driven, risk assessment, thus implementing automated, data-led underwriting.

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Alicia Montoya – Head Research Commercialization | Swiss Re

Alicia Montoya heads Swiss Re Institute’s Research Commercialization unit, bringing high potential initiatives from ideation to commercialization. Her work focuses on developing technology, core IP, partnerships, and end-to-end resilience offerings. Alicia joined Swiss Re in 2012, driving innovation, product development, and commercialization of solutions that address some of the world’s biggest risks, from climate change and natural catastrophes to sustainable energy, food security, infrastructure and transportation. She leads Swiss Re’s Quantum Cities™ initiative, using tech to foster sustainable economies and societies. Alicia started her career in London as a financial journalist at Bloomberg, then worked at the European Commission before joining Swiss high-tech start-up, u-blox, in 2005. She then worked for Alstom, driving the company’s Clean Power campaign, promoting solutions to help utilities and governments transition to clean energy.  Alicia holds a B.A. in Economics, MSc in Social Anthropology and MSc in Multimedia Systems. She is a member of the IUCN’s World Commission on Protected Areas (WCPA). Alicia is also a FinTech mentor, helping start-ups use new data, technology, and modeling to advance sustainability.

Antonio Savona – Data and technology lead | Swiss Re

Antonio Savona leads the Swiss Re institute’s data unit in the Research and Commercialisation team. He joined Swiss Re in 2015, defining and executing data & analytics driven strategies, delivering technology led transformation programmes and implementing solutions in a variety of insurance-specific verticals. Before joining Swiss Re, Antonio built a career in information retrieval and data mining holding several roles in companies that specialize in web search, including Microsoft, where he led the development of the clustering and classification solutions for Bing news. Antonio loves to understand data and communicate what it means (and what it doesn’t mean) to both technical and non-technical audiences.