• AI IN INSURANCE VIRTUAL SUMMIT

  • 09:00

    WELCOME & OPENING REMARKS (ALL TIMES EDT)

  • AI IN INSURANCE VIRTUAL SUMMIT

  • ETHICAL CONSIDERATIONS

  • 09:10
    Benedict Dellot

    The Ethics of Using AI in Personal Insurance

    Benedict Dellot - Head of AI Monitoring - Centre for Data Ethics and Innovation

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    The Ethics of Using AI in Personal Insurance

    AI and data-driven technologies are set to alter several dimensions of the personal insurance landscape, from customer onboarding to damage assessments to fraud detection and prevention. While these changes will enhance business efficiency and the overall customer experience, they also have the potential to cause harm if not managed with care. Some fear the adoption of AI for assessing risks could lead to a spike in prices and create a new class of ‘uninsurables’ in society. Others worry that expanding the use of data-driven algorithms in the industry will impinge on people’s privacy, particularly where that data is collected without consent. In this talk, Benedict Dellot will outline the findings of the CDEI’s investigation into the use of AI in the personal insurance industry, and will flag the different measures that could help keep insurers on the right side of the ethical divide as they use this powerful technology.

    Key Takeaways: • An understanding of how AI is being used in the personal insurance industry • An understanding of the top three ethical fault lines when AI is used in personal insurance • What insurers can do today to mitigate the ethical risks posed by AI

    Benedict Dellot is Head of AI Monitoring at the Centre for Data Ethics and Innovation (CDEI). His team is charged with tracking developments in AI and data-driven technology, mapping the risks and opportunities they present to society, and prioritising which issues deserve the greatest attention. Prior to joining the CDEI, Benedict was Head of the RSA's Future Work Centre, where he led research examining the impact of new digital technologies on the UK's labour market.

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  • INSURANCE CLAIMS

  • 09:30
    Roman Swoszowski

    How to Accelerate Claims Adjustment with AI and Machine Learning

    Roman Swoszowski - VP, AI and Cloud R&D - Grape Up

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    How to Accelerate Claims Adjustment with AI & Machine Learning

    What if you could eliminate time-consuming, repetitive, ineffective, and costly elements of the claims adjustment process by automating and enhancing your applications with AI? Roman Swoszowski, will share his insight into applying Machine Learning and Data Science to insurance software, which empowers insurers to virtualize inspection, automate damage evaluation and accelerate claims processing. Roman will showcase how particular, AI-enabled solutions lead to improvements in customer experience and growth in revenues. His presentation is based on expertise gathered through delivering AI solutions for leading insurance companies that embrace disruptive technologies to build a sustainable competitive advantage

    Roman is responsible for developing the overall technology vision of the company with focus on artificial intelligence, deep learning and cloud native technologies. With almost 15 years of hands-on experience in the IT industry, he drives the company’s technology strategy and works closely with engineering teams to ensure continuous delivery of innovative software solutions.

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  • 09:50
    Franziska Kirschner

    Better, Faster, and Cheaper: How Deep Learning Can Bring Real Value to the Claims Process

    Franziska Kirschner - Senior Deep Learning Researcher - Tractable

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    Better, Faster, and Cheaper: How Deep Learning Can Bring Real Value to the Claims Process

    Investment in AI is at an all-time high, yet many projects struggle to scale and generate value, both for businesses and end-users. How do you build an AI product that not only delivers, but thrives, commercially? At Tractable we develop artificial intelligence solutions for accident and disaster recovery, in particular for the auto insurance industry. In this talk I will explore how and why Tractable’s deep learning tech accelerates the claim workflow and delivers value where it matters — to the end-user.

    Key Takeaways: •Taking research from the lab and turning it into something that delivers value is hard! • There are many ways to put the customer first when designing a deep learning system • Tractable has managed to deploy and commercialise deep learning research very successfully, and has helped many policyholders recover their vehicles from accidents faster.

    Franziska is a Senior Deep Learning Researcher at Tractable. She develops Tractable’s deep learning algorithms, and focuses on diversifying and scaling the core AI across domains. Franziska started life as a physicist, and completed her PhD in condensed matter physics at the University of Oxford.

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  • AI APPLICATIONS IN INSURANCE

  • 10:10
    Julia Romero

    Machine Learning & The Future of Insurance Product Development

    Julia Romero - Lead for Actuarial Engineering & Advanced Modeling - Haven Life

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    Machine Learning & The Future of Insurance Product Development

    The insurance industry faces a number of challenges in developing new products: long-term liabilities, a rapidly changing distribution environment, and complex customer behaviors that can materially impact product value. At Haven Life and MassMutual, we believe that the actuarial paradigm must evolve in order to support innovation and continue to delight customers for the next 100+ years. We are building a new product development platform that uses machine learning models and modern econometric techniques to drive rapid and sound product development and pricing for the challenges of the modern insurance market.

    Key Takeaways: • What matters to our customers is what matters to us 2. Actuaries need to change, but so do data scientists • At the end of the day its the technology that really enables us to drive forward

    Julia Romero is the lead for Actuarial Engineering and Advanced Modeling at Haven Life, an online life insurance agency that’s backed and wholly owned by MassMutual. At Haven Life, Julia is focused on integrating and applying data science and other analytics models to drive innovation in actuarial technology. Prior to joining Haven Life, Julia worked as an actuary at AXA US, where she focused on the development of agent based models of annuity policyholder behavior.

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  • 10:30

    COFFEE

  • 11:15

    PANEL: Assessing the Future of AI & Regulation in Finance & Insurance

  • Natalia Bailey

    Moderator:

    Natalia Bailey - Policy Advisor, Digital Finance - Institute of International Finance

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    Natalia Bailey is an Policy Advisor, Digital Finance at the IIF, where she focuses on the digital transformation of the financial system, particularly the application of new technologies such as Machine Learning to the domain of risk management, and financial sector supervision.

    In her prior role she focused on banking prudential regulation where she reviewed the modeling practices in banks’ internal RWA models, and helped develop a multi-pronged approach to enhance internal model based capital approaches.

    Natalia holds a MPP from George Mason University, and a BA in Economics from Hollins University, where she attended on an IIE-Fulbright Scholarship.

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  •  Edosa Odaro

    Panelist:

    Edosa Odaro - Chief Business Officer - Theory & Practice

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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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  • Julie Wall

    Panelist:

    Julie Wall - Reader in Computer Science - University of East London

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    Detecting Deception and Tackling Insurance Fraud Using Conversational AI

    We are developing an explainable pipeline that will identify and justify the behavioural elements of a fraudulent claim during a telephone report of an insured loss. To detect the behavioural features of speech for deception detection, we have curated a robust set of acoustic and linguistic markers that potentially indicates deception in a conversation. Using statistical measures and machine learning approaches, the detection of these linguistic markers in the right context is being investigated. The explainable pipeline means that the output of the decision-making element of the system will provide transparent decision explainability, overcoming the “black-box” challenge of traditional AI systems.

    Dr Julie Wall is a Reader in Computer Science, Director of Impact and Innovation for the School of Architecture, Computing and Engineering and leads the Intelligent Systems Research Group at the University of East London. Her current research focuses on developing machine learning and deep learning approaches for speech enhancement, natural language processing and natural language understanding and she maintains collaborative R&D links with industry. This has led to the successful acceptance of two Innovate UK grants with a combined total value of £2,273,177. Since starting her PhD in 2006, Julie has been exploring the overarching research area of designing intelligent systems for processing and modelling temporal data. This primarily involves investigating the architectures and learning algorithms of neural networks for a variety of data sources.

    https://www.uel.ac.uk/research/intelligent-systems

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  • Irina Velkova

    Panelist:

    Irina Velkova - Senior Manager - Grant Thornton

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    Irina is a member of the Fintech Steering Committee at Grant Thornton and leads its Regtech proposition. Grant Thornton’s Fintech practice brings together firms developing and embracing emerging technology to help technology adopters and well established financial services institutions adapt in this rapidly changing environment. Irina specialises in governance and regulatory matters and has worked on numerous projects in the financial services space and fintech. Her role at Grant Thornton includes managing large financial services regulatory projects across banking, insurance and investment management, including designing and implementing improved clients’ structures, business processes and models; advising senior management and C-suite stakeholders on governance and regulatory matters; developing new market propositions and services that leverage current technology trends and new tech-enabled financial services solutions. Irina has graduated in Law and has a Master Degree in Corporate Law from University College London (UCL). She also holds a Diploma in English and European Law from the University of Cambridge

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  • 11:50
    Kinga Kita-Wojciechowska

    Using AI to Extract Alternative Data: How a Google Street View Image Can Predict a Car Accident

    Kinga Kita-Wojciechowska - Researcher - University of Warsaw

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    Using AI to Extract Alternative Data: How a Google Street View Image Can Predict a Car Accident

    Artificial intelligence and data collection at scale open up limitless opportunities to tap into streams of data previously ignored by practitioners. In this presentation, delegates will have the chance to explore a new study from researchers at Stanford University and the University of Warsaw, which shows that a Google Street View image of a house can predict car accident risk of its resident, independently from classically used variables such as age and zip code. Find out how modern computer vision techniques, such as deep learning, applied to publicly available data from Google Satellite and Street View may dramatically improve risk models and take current insurance pricing methods to the next level.

    Key Takeaways: • Publicly available Google Maps and Street View images are a great source of data • Modern machine learning techniques applied to these images allow insurers to make use of this alternative data source on scale • It is worth to refine insurance pricing and rely on the address of the client, instead of a postcode only.

    Kinga is a researcher at the University of Warsaw looking for innovation in insurance pricing through application of AI. Prior to that she was working 10 years in motor insurance pricing, most of the time at AXA Group, where she held various managerial and expert roles in Poland, France, Spain, Korea, Japan and China. Holds a double master degree in mathematics and economics from the University of Warsaw.

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  • 12:10
    Julie Wall

    Detecting Fraud in Insurance Claims Calls

    Julie Wall - Reader in Computer Science - University of East London

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    Detecting Deception and Tackling Insurance Fraud Using Conversational AI

    We are developing an explainable pipeline that will identify and justify the behavioural elements of a fraudulent claim during a telephone report of an insured loss. To detect the behavioural features of speech for deception detection, we have curated a robust set of acoustic and linguistic markers that potentially indicates deception in a conversation. Using statistical measures and machine learning approaches, the detection of these linguistic markers in the right context is being investigated. The explainable pipeline means that the output of the decision-making element of the system will provide transparent decision explainability, overcoming the “black-box” challenge of traditional AI systems.

    Dr Julie Wall is a Reader in Computer Science, Director of Impact and Innovation for the School of Architecture, Computing and Engineering and leads the Intelligent Systems Research Group at the University of East London. Her current research focuses on developing machine learning and deep learning approaches for speech enhancement, natural language processing and natural language understanding and she maintains collaborative R&D links with industry. This has led to the successful acceptance of two Innovate UK grants with a combined total value of £2,273,177. Since starting her PhD in 2006, Julie has been exploring the overarching research area of designing intelligent systems for processing and modelling temporal data. This primarily involves investigating the architectures and learning algorithms of neural networks for a variety of data sources.

    https://www.uel.ac.uk/research/intelligent-systems

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  • 12:30

    VIDEO ROUNDTABLE NETWORKING MIXER

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