• 08:30


  • 09:15



  • 09:30

    Key Factors Evolving the Fourth Industrial Revolution in Insurance

  • 09:50
     Edosa Odaro

    How to Establish Effective Foundations for Data Science and for AI

    Edosa Odaro - Head of Data Services - AXA

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    How to Establish Effective Foundations for Data Science and for AI

    With industry surveys suggesting a staggering 80% of AI and Data Science initiatives fail to deliver value, we explore approaches - developed over the past 20 years - for increasing the chances of success. We will start with the key risk factors, including: Did it start with "why" - and what is the approach to understanding value? Is it safe to fail - and conducive for learning? Have we dared to think - and step - outside the box? The conversation will then shift focus towards strategies for overcoming these challenges (centred around 3 core initiatives) and then - controversially - considers if we could spin the disadvantages of data silos into actively encouraged strategies.

    Edosa is Head of Data Services at AXA – the world's second-largest financial services company by revenue – where his accountability cuts across data vision, strategy, architecture, engineering, science, AI, governance, digital and data operations. Prior to AXA, he has played senior data leadership roles within a variety of multinational organisations – including Barclays Group, the European Commission, Allianz Cornhill Insurance and British Sky Broadcasting Corporation.

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  • 10:10
    Mohamed Ikbal Nacer

    AI, Blockchain and IOT are the Blueprints for a New Insurance Infrastructure

    Mohamed Ikbal Nacer - R&D Engineer - Smart-Cover Insurance

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    AI, Blockchain and IOT are the Blueprints for a New Insurance Infrastructure

    The usage of primitive technique to provide a service to the client in insurance is one of the drawbacks that can hold companies from rapid growth. Whereas in technological companies, senior managers find a way to reuse previous work to keep selling, the insurer depends on hiring and training people to keep new customers satisfied. The urge of automation to ensure better customer relation management, companies finance administration, item tracking, analysis and forecasting is in high need. Artificial intelligence as technology has shown an impressive result on many subjects in recent years. Based on the UK Parliament white paper (parliament , 2019) AI and Blockchain is one of the concepts to start the fourth industrial revolution in the UK. Consequently, insurers must adapt quickly to the work environment changes. Blockchain can be used as a source of trust to build a system resilient to cyber-attacks and automate authentication procedures for a better-automated communication between the client, the finance department, ensured service and the underwriter . Moreover, the AI techniques can be incorporated in steps such as the risk assessment using speaker identification algorithms, finer details extraction from a speech to provide a score for each call, premium forecasting and automated policy generation. Thirdly, the Internet of things is the tracking device for each insured product to generate data that will be a subject of analysis and prediction. The combination of the above three technologies is the key to the new infrastructure of the insurance industry.

    Mohamed Ikbal Nacer is a PhD student at the university of Bournemouth. He had enjoyed being within some technological companies such as Ooredoo Group, Etas which is a subsidiary of the Bosch Group and he is very interested to bring technological solution to revolutionise the way companies handle day to day business. His research is focused on the automation of decision making and how to apply all those theories to make insurance companies enjoy the rapid growth that can be exhibited by their Technological counterpart.

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  • 10:30



  • 11:10
    Dapeng Wang

    Hidden Difficulties in Building Deep Learning Models and How to Resolve Them

    Dapeng Wang - Data Scientist - LV=

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    Hidden Difficulties in Building Deep Learning Models and How to Resolve Them

    As deep learning gains momentum in its application to business, an end-to-end approach where the model handles everything from the input data to the output predictions, seems very attractive. There are plenty of available resources on deep learning projects that focus on the model building aspect of deep learning. But in order to build an effective deep learning model, numerous crucial small obstacles often get overlooked. At LV=, we are currently investigating the power of deep learning to understand from images whether a car is repairable or if it should be over-written, to improve our decision making. This talk will take a deep dive on the problems that we have encountered when building convolutional neural networks, and our approach to solve them.

    Dapeng Wang is a Data Scientist at the insurance company LV=. He graduated in maths from the University of Cambridge and has an MSc from the University of Sussex. At LV=, Dapeng is leading in the adoption of Deep Learning across the company. He is currently developing the end to end pipeline to build and integrate Deep Learning within current LV= processes. Dapeng is also a frequent Kaggle competitor and Kaggle competition expert. Dapeng looks forward to using his experience to help the deep learning community find suitable and better implementation solutions for deep learning.

  • 11:30
    Valeria Verzi

    Text Mining Algorithms for Data Management

    Valeria Verzi - Senior Data Scientist - Generali

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    Generali is the largest Italian insurance company and the third in Europe. Generali’s action plan for 2021 is based on 3 fundamental pillars, one of these is Innovation and Digital Transformation. The goal is to become a digital company at 360°; impacting customers, the operative model and the distribution.

    In this context, in Generali Italia the Advanced Analytics team works in close cooperation with the antifraud team in order to study and find always new and innovative ways to fight the fraudulent situations, taking advantage of the big amount and variety of data that are available.

    One of the latest challenge of this collaboration is the management and processing of claim documents. This talk will be about one of the ways we decided to implement to exploit document values through artificial intelligence.

    Valeria Verzi is currently Senior Data Scientist at Generali Italia. Valeria’s role consists of exploiting the deep technical knowledge and the business understanding to develop cutting edge artificial intelligence models to drive internal digital transformation.

    She got a bachelor in Mathematical Engineering, for which she developed a thesis on multi-objective optimisation at ABB. Valeria then took the master’s degree with honors in sound and music Engineering (Computer Engineering) at Politecnico of Milan. She wrote her master thesis at Queen Mary University of London on piano inharmonicity mathematical and computer modelling. Valeria continued her career as data scientist moving to industry in 2014.

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  • 11:50
    Jonathan Davies

    Data Science at Zurich: The Whole is More Than the Sum of its Parts

    Jonathan Davies - Lead Data Scientist - Zurich Insurance

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    Data Science at Zurich: The Whole is More Than the Sum of its Parts

    At Zurich we have invested heavily in building an industry-leading data science and advanced analytics capability. I will cover our pioneering use of external and open data, and how valuable this data is to data science work when used together. I discuss how we have fostered a working environment inspired by academia, and the collaborations we have been establishing with local Universities to ensure our team reach their full potential. Finally, I will cover the innovative new ideas and ways of thinking we have brought to the business.

    Jonathan holds the current position of Lead Data Scientist at Zurich Insurance UK, where he leads a team working on a variety of projects ranging from Internet of Things through to Natural Language Processing. He has a doctorate in Theoretical Astro-particle Physics from Durham University, and previously worked as a researcher at the Sorbonne in Paris and King's College London.

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  • 12:10



  • 13:10

    Building On Text Mining & Anomaly Detection in Fraud Management

  • 13:30

    End-To-End Fraud Prevention with AI: Analysing Images, Speech & Text


  • 13:50
    Clara Castellanos Lopez

    NLP for Claims Management

    Clara Castellanos Lopez - Senior Data Scientist - QBE Europe

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    NLP for Claims Management

    QBE Insurance Group is one of the world’s leading insurers and reinsurers, with operations in 31 countries worldwide. Covering multiple lines such as marine, motor, casualty, amongst others, the diversity of claims is wide. Exploiting the richness of claims data can help to process claims faster and gain deeper insight. In this talk, I will discuss how some nlp techniques could be used to this end.

    Clara Castellanos Lopez is a Senior Data Scientist at QBE Insurance Group. She works with the claims teams building machine learning algorithms to provide data driven insights and automated solutions. Clara has been working in the industry since 2014 with experience in oil and gas, retail and insurance. She has a master degree in Applied Mathematics and a Ph.D in Geophysics from Universite de Nice Cote d’Azur.

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  • 14:10

    ANNs vs SVMs for Claims Management


  • 14:30
    Sergey Mastitsky

    Delivering Personalised Product Recommendations to Aviva Customers

    Sergey Mastitsky - Data Science Lead - Aviva

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    Delivering Personalised Product Recommendations to Aviva Customers

    Aviva is the largest insurance company in the UK, with over 15 million registered customers. The company is constantly looking for ways to increase the relevance of its marketing communications. As part of this work, the Aviva UK Customer Science team has built ADA (Algorithmic Decision Agent), an AI-based system that predicts what is the most relevant product to talk to each individual customer about at a given time. This talk will give an overview of the technical details around this system, challenges the team faced while building and embedding it, and the value it added to the business.

    Sergey is a Data Scientist with multiple years of experience in academic and industrial sectors. He currently manages a team of Data Scientists and engineers, who are helping to deliver personalised product offerings and experience to customers of the insurance company Aviva (UK). Sergey has published 4 books on data analysis and visualisation using R, and has been authoring a popular blog about this language since 2011.

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  • 14:50

    One-Policy-For-All Product Bundling with AI

  • 15:10



  • 15:45

    NLP Developments for Accurate Insurance Advice

  • 16:05

    AI Assistants for Health Insurance

  • 16:25

    PANEL: What are the Barriers to Advancing Chatbots in Insurance?

  • 17:00


  • 08:30


  • 09:15



  • 09:25
    James Russell

    AI-for-Good: Unbiased Advice for the Busy Small-Business Owner

    James Russell - CEO & Co-Founder - Brisk

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    AI-for-Good: Unbiased Advice for the Busy Small-Business Owner

    Small businesses are busy. If they had the time to monitor late paying customers, supply disruption, employee absenteeism, cyber crime etc - they would. Unfortunately, they don’t have the resources of larger businesses, and neither do their human brokers and advisers. It’s not right that with the insurance practices of today, over 60% of SMEs are under-insured (or over-insured) and exposed to risk.

    What if the busy SME and their trusted advisers had the support of an ‘AI’ powered platform that could identify risks, recommend action and enable the next generation of insurance product that adapts as the business changes?

    James is the CEO and co-founder of www.getBrisk.com. Having worked at Aviva Insurance for 17 years including four years as the MD of the internet auction business bluecycle.com, James is now launching Brisk. Brisk is a robo-advice platform that provides holistic protection for small to medium sized businesses beyond just an insurance policy. Brisk works with brokers and business advisers to help well-run businesses get rewarded for being better ‘risks’ and enable financial products to be tailored and adapt as the business changes. Prior to Aviva, James was a supply chain consultant at Ernst & Young and Key Account Manager at Lucas Automotive.

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  • 09:45

    Increasing Marketing ROI in Insurance with Machine Learning


  • 10:05

    AI in Property & Causality Insurance

  • 10:25

    Benefits of Time Series Analytics in Property Insurance

  • 10:45



  • 11:20

    Innovation & Digital Transformation in the Insurance Industry


  • 12:25



  • 13:25
    Jason Engelbrecht

    Insurance for Remote Industries

    Jason Engelbrecht - CTO - Munich Re

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    Jason Engelbrecht recently joined Munich Re as Chief Technology Officer. Whilst based in London he has an international remit and works with all of Munich Re’s subsidiaries and businesses. His mandate within Munich Re’s is to work across business units to define and deliver on Munich Re's IT innovation strategy, concentrating on new Agile Product Development and innovative business models. Jason’s background is in Machine Learning and New Product Development, with a focus on Lean and Agile development methodologies. He joined Munich Re from Bloomberg and Thomson Reuters where he ran AI and Research & Development departments, delivering new and innovative AI and analytics products to the Legal, Risk, Finance and Energy sectors. He has a master’s degree in Data Science and an MBA from CASS Business School.

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  • 13:45

    Cross-Sector Applications of Facial Recognition


  • 14:05

    Document Digitalisation

  • 14:25

    PANEL: The Future of InsurTech

  • 15:00


RE•WORK London AI Finance Summit

RE•WORK London AI Finance Summit

31 - 01 April 2020

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