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Data science landscape in the insurance industry
The aim of this master thesis is to map the landscape of data science in the insurance industry today.
Keywords: data science, predictive analytics, survey, insurance, fintech
According to a recent study by BCG, digitalization and data analytics are amongst the core challenges of the insurance industry today. Insurance companies collect and store large amounts of data that have a huge potential of being transformed into actionable insights about risk and, ultimately, society.
From a data science perspective, the insurance industry can be regarded as a greenfield: be it automating repetitive manual data handling tasks, finding better ways to quantify and price risk, detecting fraudulent claims more efficiently, or improving customer services through the use of technologies such as machine vision and speech recognition, the opportunities to apply data science in the insurance industry are vast.
Recognizing these opportunities and the need to remain competitive, many insurance companies have built their own data science divisions in the past few years. Teams ranging from a handful to several hundred data scientists are now working in the context of the insurance business. Data scientists, often coming from an academic background, bring with them state-of-the-art technology and novel approaches, which are slowly changing a business that is hundreds of years old.
Unfortunately, in spite of the large investment by the insurance industry in data science, little is known about what problems are being tackled and which approaches have proved successful so far. There is, at present, no comprehensive overview of data science in the insurance industry.
You should meet following requirements:
• Enrolled in a master program at ETH or HSG
• Knowledge in statistics, empirical research studies and survey methods
• Strong ability in analyzing, structuring and illustrating results
• Interest in digitalization, artificial intelligence and big data
• Familiarity with, or willingness to learn, the needs of an insurance business
• Proficient in English. German of advantage.
According to a recent study by BCG, digitalization and data analytics are amongst the core challenges of the insurance industry today. Insurance companies collect and store large amounts of data that have a huge potential of being transformed into actionable insights about risk and, ultimately, society. From a data science perspective, the insurance industry can be regarded as a greenfield: be it automating repetitive manual data handling tasks, finding better ways to quantify and price risk, detecting fraudulent claims more efficiently, or improving customer services through the use of technologies such as machine vision and speech recognition, the opportunities to apply data science in the insurance industry are vast. Recognizing these opportunities and the need to remain competitive, many insurance companies have built their own data science divisions in the past few years. Teams ranging from a handful to several hundred data scientists are now working in the context of the insurance business. Data scientists, often coming from an academic background, bring with them state-of-the-art technology and novel approaches, which are slowly changing a business that is hundreds of years old. Unfortunately, in spite of the large investment by the insurance industry in data science, little is known about what problems are being tackled and which approaches have proved successful so far. There is, at present, no comprehensive overview of data science in the insurance industry.
You should meet following requirements: • Enrolled in a master program at ETH or HSG • Knowledge in statistics, empirical research studies and survey methods • Strong ability in analyzing, structuring and illustrating results • Interest in digitalization, artificial intelligence and big data • Familiarity with, or willingness to learn, the needs of an insurance business • Proficient in English. German of advantage.
The aim of this master thesis is to map the landscape of data science in the insurance industry today. The scope is worldwide, but with a preferential focus on Europe. The research methodology is expected to comprise both:
• a survey addressed to data scientists in insurance companies; and
• web mining of insurance companies’ websites
The survey component of the research is intended to answer questions such as:
• What do data scientists spend most of their time working on in their insurance company?
• What kind of data science problems are being tackled recurrently across insurance companies?
• What kind of internal and external data are data scientists using in their work?
• What are the typical sizes of data science teams and complexity of data science projects?
• Who are the major beneficiaries of the work produced by a data scientist?, e.g. an internal division, a class of business partners, the customers.
• Which insurance products have been released that make use of data science? Which of those products would not have been possible without data science?
The web mining component of the research is intended to answer the same questions in an indirect way, through inference from the statistics gathered by automatically analyzing the textual content of insurance companies’ websites.
The aim of this master thesis is to map the landscape of data science in the insurance industry today. The scope is worldwide, but with a preferential focus on Europe. The research methodology is expected to comprise both:
• a survey addressed to data scientists in insurance companies; and • web mining of insurance companies’ websites
The survey component of the research is intended to answer questions such as: • What do data scientists spend most of their time working on in their insurance company? • What kind of data science problems are being tackled recurrently across insurance companies? • What kind of internal and external data are data scientists using in their work? • What are the typical sizes of data science teams and complexity of data science projects? • Who are the major beneficiaries of the work produced by a data scientist?, e.g. an internal division, a class of business partners, the customers. • Which insurance products have been released that make use of data science? Which of those products would not have been possible without data science?
The web mining component of the research is intended to answer the same questions in an indirect way, through inference from the statistics gathered by automatically analyzing the textual content of insurance companies’ websites.
Please send your application with [Master Thesis – data science in insurance] in the subject line to:
Cristina Kadar, PhD Candidate
Chair of Information Management (Professor Elgar Fleisch)
D-MTEC, ETH Zurich
Homepage: http://www.im.ethz.ch/people/ckadar.html
Email: ckadar {at} ethz {dot} ch
Please send your application with [Master Thesis – data science in insurance] in the subject line to: Cristina Kadar, PhD Candidate Chair of Information Management (Professor Elgar Fleisch) D-MTEC, ETH Zurich Homepage: http://www.im.ethz.ch/people/ckadar.html Email: ckadar {at} ethz {dot} ch