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Lisa Balboa works as an Actuarial Consultant for Bupa and is passionate about using her actuarial skills to better understand how advances in science, technology and data analytics can be used to better understand and manage risk. Her latest research for the Institute and Faculty of Actuaries (IFoA) is focused on how predictive analytics could be used to manage morbidity risks. Lisa leads the Ethics and Regulation work stream for the IFoA’s Data Science MIG (Member Interest Group). The cross-practice work stream considers regulatory and ethical requirements for actuaries using data science techniques in traditional and emerging fields across insurance, risk management and finance.

Prior to her work in the actuarial profession, Lisa studied for an MSc in International Health Policy at the London School of Economics. Her Master’s thesis focused on the implications of wearable technology for insurers. More recently, she has also published a paper on this topic for the IFoA, presenting her findings at actuarial events both in the UK and internationally. She was also a member of the IFoA’s Wearables and Internet of Things Working Party where she contributed to a research paper on the Life Insurance and Health Insurance implications of wearable and IoT technologies.

During her undergraduate degree at the University of Cambridge, Lisa researched into sustainable funding models for health systems. She co-authored a case study on lessons from the operational franchise at Hinchingbrooke Hospital which she presented to NHS doctors attending the Cambridge Judge Business School’s Chief Residents’ Leadership and Management Programme.  

Whilst pursuing her academic studies, Lisa was also a company Director of the intergenerational think-tank Global Biotech Revolution (GBR). As President of GBR’s GapSummit 2016 conference she brought together current experts and aspiring leaders in bio-sciences from over 40 nations to discuss the challenges facing the
global bio-economy including climate risks, funding pressures and antimicrobial resistance.

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