• DAY 1 - AI IN INSURANCE SUMMIT

  • 08:30

    REGISTRATION & LIGHT BREAKFAST

  • 09:15

    WELCOME

  • CURRENT LANDSCAPE

  • 09:25
    Manan Sagar

    Trust Me, I’m a Robot – The Future of AI and Automation in Insurance

    Manan Sagar - CTO - Fujitsu

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    Trust Me, I’m a Robot – The Future of AI and Automation in Insurance

    Technology is changing the way we live and consume service. Insurance has perhaps been the slowest to react to these changes but it is apparent that data is starting to drive precision in insurance. “Real-time-risk-management” enabled by the “subscription model” is, in the very near future, going to become main stream in personal insurance. Advancements in data sciences coupled with a fourfold increase in the number of sensors is going to lead to a seismic shift in insurance models from the traditional insurance model of “Repair and Replace” to “Predict and Prevent”.

    Manan is a highly experienced insurance professional and a pragmatic business leader. He has previously lead Lockton’s Singapore business where he delivered organisation-wide changes and then went on to manage one of the largest acquisitions in the insurance industry. A Chartered Accountant by profession and now a technologist by trait, Manan is well regarded for his thought leadership. His career has spanned across the Americas, EMEA and Australasia.

    As Fujitsu’s Insurance CTO, Manan is responsible for defining the innovation strategy for the insurance sector. In his role he is a strategic advisor to the insurance sector on connected technology solutions that have already been applied in Manufacturing, Defence and Transportation and co-create future-focused insurance solutions that would enable a shift in the insurance model from “repair and replace” to “predict and prevent”.

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  • AI INFRASTRUCTURES

  • 09:45
     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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  • 10:05
    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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  • 10:25

    COFFEE

  • 11:00

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

  • 11:20

    The Human Aspect to Successful AI Integration

  • CUTTING EDGE AI TOOLS & TECHNIQUES

  • 11:40
    Dapeng Wang

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

    Dapeng Wang - Data Scientist - LV=

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    Understanding Categorical Embedding in Deep Learning

    In insurance, structured data provides valuable information to the problems that we want to solve, and often this information is captured as categorical variables. Therefore manipulating categorical variables into the right format for Machine Learning (ML) models becomes an essential part of the ML pipeline. Common approaches such as one-hot encoding and mean response encoding might work for particular types of models. However, the process of transforming categorical data can be challenging, very time consuming and result in loss of information and sparse input to Neural Network (NN) models. At LV, we are leveraging the success of word embedding, where sematic and syntactic relationships are captured between words. We are exploring the use of categorical embeddings in order to obtain intrinsic properties of categorical data to improve model performance. Furthermore, in this presentation, we will explore different ways of understanding the embedding space.

    Dapeng Wang is a Senior 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.

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  • 12:00

    The Benefits of Combining Imagery and AI for the Insurance Industry

  • 12:20

    AI Powering Insurance

  • 12:40

    LUNCH

  • PROCESS IMPROVEMENTS

  • 13:30

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

  • 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

    ShAIrlock Holmes : AI Inspector for Claim Fraud Detection

  • CONSIDERING FRAUD

  • 14:30
    Julie Wall

    Detecting Fraud in Insurance Claims Calls

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

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    Detecting Fraud in Insurance Claims Calls

    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 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 and NLP 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.

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  • 14:50
    Mike Yates

    Supervised vs Unsupervised Fraud Detection

    Mike Yates - Data Science Manager - Co-Op Insurance

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    Supervised vs Unsupervised Fraud Detection

    This talk will cover a brief history of Co-Op Insurance success with Supervised approach to Fraud detection, and our belief that in certain use cases (fraud being one of them) that supervised models can only take you so far... Enter Unsupervised Fraud detection, including brief overview of techniques used (Isolation Forest & Auto-Encoder).

    Co-Op Insurance has a rich history serving its members & customers for over 150 years. Mike Yates has 15 years experience in Analytics / Data Science across different industries including utilities, retail and insurance. He currently uses Agile practices to manage a team of Data Scientists, adding value across a variety of use cases including fraud, pricing & customer experience.

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  • 15:10

    COFFEE

  • CUSTOMER EXPERIENCE

  • 15:40

    Delivering Personalised Product Recommendations to Aviva Customers

  • 16:00
    Saqib Khanbhai

    Offering Competitive Pricing to Travel Insurance Customers

    Saqib Khanbhai - Data Scientist - Staysure

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    Offering Competitive Pricing to Travel Insurance Customers

    Staysure is one of the UK’s leading Travel Insurance Provider, providing cover for over six million policyholders since 2004. The company is continually searching for ways to offer pricing which is both competitive within the market yet delivers value for stakeholders. A keystone of achieving both objectives revolves around estimating and utilising Price Elasticity, determined through our Machine Learning based conversion models. This talk is intended to give an overview of the importance of having reliable conversion models underpinning the process, the challenges faced while using various Machine Learning techniques to gain estimates of Price Elasticity and the value these models have added to our business.

    Saqib Khanbhai is a Data Scientist at the insurance company Staysure. He has a master's degree in International Economics from The University of Birmingham with a particular interest in Behavioural Economics, applying these learnings to form his thesis on Tax Audit Optimisation. Saqib's role consists of innovating business processes through automation alongside developing data-driven insights to drive the company’s pricing strategy. The machine learning models Saqib is involved in developing combine customer behaviour and business knowledge to achieve market share growth alongside business revenue targets. Saqib looks forward to combining his expertise in Behavioural Economics and machine learning to help develop innovative applications of cutting edge techniques.

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  • 16:20

    The Ethics of Using AI in Personal Insurance

  • RISK MANAGEMENT

  • 16:40
    Marc Fiani

    AI for Actuarial Work: Opportunities and Risks

    Marc Fiani - Director - MetLife

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    AI for Actuarial Work: Opportunities and Risks

    Artificial Intelligence is a natural extension of actuaries traditional quantitative methods. As such, actuaries should be the first users of advanced AI models. Indeed, these models provides ways to find new risk pools, improve computation time and manage more data. However A.I. comes with its own challenges and risks. These risks have kept A.I. out of insurance core practices: risk management and policies pricing whereas departments such as distribution, marketing, claim management and fraud have greatly benefited from it. The three main challenges are individualisation, explainability and ethics which we will cover during the presentation.

    Marc Fiani is a Director in MetLife Actuarial organisation. He has a Master degree in Mathematics from Columbia University in New York. He worked for the past 5 years with actuaries within the company to improve business processes using Machine Learning and Big Data.

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  • 17:00

    CONVERSATION & DRINKS

  • DAY 2 - WOMEN IN FINTECH SUMMIT

  • 08:30

    REGISTRATION & LIGHT BREAKFAST

  • 09:15

    Welcome

  • CURRENT LANDSCAPES

  • 09:25
    Claire Calmejane

    The Changing FinTech Landscape

    Claire Calmejane - Chief Innovation Officer - Société Générale

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    Claire Calmejane began her career in 2006 in the Technology Transformation department of Capgemini Consulting, where she supported companies and especially financial institutions in their technological and digital transformation. Contributing to a study on the digital transformation of large companies led by the Massachusetts Institute of Technology in 2011, she joined the London office of Capgemini to lead the digital centre of the Financial Services sector. Recruited in 2012 by Lloyds Banking Group as Head of Digital Delivery in the Online Services Department, she was appointed Innovation Director and set up the Innovation Labs and the Digital Academy before being appointed Risk Transformation Director at Lloyds Banking Group. In September 2018, Claire Calmejane joined Societe Generale as Group Chief Innovation Officer.

    Claire Calmejane studied IT Engineering with a degree from the École pour l'informatique et les techniques avancées (EPITA) and a Masters degree from the HEC French school of management.

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  • 09:45

    PANEL: Ideals Compared to Realities

  • Sharon Kits Kimathi

    Host:

    Sharon Kits Kimathi - Editor - FinTech Futures

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    Sharon Kits Kimathi is Editor of Fintech Futures and Banking Technology since May 2019, having been Deputy Editor at the International Financial Law Review (IFLR) and a capital markets Reporter at mtn-i and Global Capital.

    Having obtained a Bachelor of Laws degree at the University of Sussex, Sharon graduated from her Legal Practitioner’s Course (LPC) as President of the Law Society at City Law School and Winner of the Young Legal Minds Award, 2012.

    This was then followed by permanent positions as a Paralegal for Freshfields Bruckhaus Deringer; Legal Compliance Associate for Goldman Sachs; and Paralegal at Reed Smith LLP. It was conducting legal research for fintech clients at the latter which prompted a shift in career trajectory, away from the legal profession and towards specialist research and journalism from September 2016.

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  • WOMEN IN TECH

  • 10:10

    When Dreaming Doesn’t Spark Joy

  • 10:30

    PANEL: Women in Tech: What We Wish We Knew

  • Neeta Mundra

    Host:

    Neeta Mundra - Banking and Financial Services Executive - Salesforce

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    Neeta is a senior executive with a strong entrepreneurial spirit and a commitment to driving business outcomes with over 19 years of global banking and insurance experience and a deep understanding of technology and financial services. She recently won the “Exceptional Women of Excellence Award” at Women Economic Forum. Besides her full-time role at Salesforce, Neeta is non-executive director and on the board of Young Women’s Trust (UK), on the committee of Women in Banking and Finance (Head Women on Board program), and a mentor with the Cherie Blair Foundation. She is very passionate about technology disruption, transparency in financial services, open banking/finance and appears in various panel discussions. She is an angel investor in fintech start-ups and is particularly interested in women led fintech start-ups.

    Over the years, she has performed various roles ranging from building platforms, delivering large-scale transformation programs to top banks and insurance companies, outsourcing, management consulting to heading regions. She has managed P&L to build successful, profitable and scalable business units and led strategic mergers & acquisitions.

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  • 10:55

    COFFEE

  • COMPANY CULTURE

  • 11:30
    Rukasana Bhaijee

    Evolution of Culture - To Accomplish New Things, We Have to Change Old Ways of Working

    Rukasana Bhaijee - Diversity & Inclusion Senior Manager - Google

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    Evolution of Culture - To Accomplish New Things, We Have to Change Old Ways of Working

    We live in a transformative time, which has changed the role that culture plays in companies. Along with other factors like disruption, shifting priorities and behavioural risk D&I has been identified as an important component within culture.The role of D&I in the workplace continues to change as a result of a globalizing workforce, challenges surrounding recruiting and retaining top talent, organizations focused on improving their bottom line, and enhanced public scrutiny. These and other global trends are requiring organizations to re-focus their attention on D&I and to refresh their approach to these challenges.

    Rukasana is an Inclusion and Diversity leader who provides support to public and private sector organisations to achieve their D&I ambitions through providing consultancy at all levels of D&I maturity. In the last year Rukasana has worked with Executive levels teams to build inclusive leadership capability, led on D&I strategy build, developed a sponsorship programme curriculum to support the progression of women/ethnic minority individuals and has delivered cultural intelligence programmes. Rukasana has also led on the delivery of organisational change for EY D&I internally through leading on change programmes in areas of the business acting as an Inclusion Coach.

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  • 11:50

    PANEL: Company Culture: Closing the Gap

  • 12:15
    Julia Streets

    Panelist:

    Julia Streets - Founder & Host - DiverCity Podcast

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    Julia Streets is a champion of fintech innovation, diversity & inclusion. As Founder & CEO of Streets Consulting her business development, marketing and communications consultancy, she and her team advise UK & international fintech firms. Her previous tenures included global head of communications, NYSE Technologies and head of marketing and sales at Instinet Europe. As a City entrepreneur, advisor, podcaster and investor she is regularly called upon to MC and contribute to conferences round the world, drawing also on her capabilities as a sell out stand up comedian. She is founder & host of 'DiverCity Podcast' talking about the commercial imperative for equality, diversity and inclusion in financial services.

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  • 12:15

    Journey from Traditional Banking to the New Wave of Financial Services

  • 12:30

    LUNCH

  • 13:20
    Chandini Jain

    AI: Can Machines Truly Make Smarter Investment Decisions Without Any Human Input?

    Chandini Jain - Founder - Auquan

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    Women in FinTech Summit - AI: Can Machines Truly Make Smarter Investment Decisions Without Any Human Input?

    Quantitative and semi-quant funds have consistently outperformed discretionary funds over the last few years. Does this mean the age of human intuition based investing is over? Can machines truly replace humans? We will explore the case for data driven decision making in investment management, the benefits and pitfalls of fully automated investing and why the human touch is still important. AI in finance, when done right, is not a replacement for human trader but a powerful tool that traders can combine with their own skill to massively augment their decision making and boost profitability.

    AI in Finance Summit - AI for the buyside: Deep Learning for Factor Research

    Problems in the financial markets are different from typical deep learning applications since the emphasis is not on replicating tasks that humans already do well. Humans have no innate ability to solve these problems that involve large data sets and complex data interactions that currently are difficult to specify in a purely economic model. Applying deep learning methods to these problems can detect and exploit interactions in the data that are, at least currently, invisible to any existing financial economic theory.

    We will demonstrate an example of how we can combine the latest statistical techniques with deep learning methods to compute any relationship between thousands of raw indicators and asset prices, no matter how complex and non-linear. These insights can be used to create new empirical representations of fundamental factors which lead to long-term over-performance. This also enables a better understanding of portfolio exposure to different risk factors, allowing asset managers to create high performing, well diversified portfolios. We will compare the performance of these methods to the linear factor models of traditional financial economics and the ad hoc methods of statistical arbitrage and other quantitative asset management techniques.

    Chandini Jain is the CEO and founder of Auquan. She has 7+ years of global experience in finance with Deutsche Bank in Mumbai/New York and as a derivatives trader with Optiver in Chicago and Amsterdam. At Optiver, she traded volatility arbitrage strategies and was involved first hand in making the shift from discretionary to automated trading. Since 2017, she has been working on Auquan, an early stage fintech startup employing new and cutting edge ML and Deep Learning techniques to solve financial prediction problems for hedge funds and asset managers.

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  • 13:35
    Emily Bailey

    Large-scale Business Forecasting at Uber: Successes and Failures in Adapting to a Changing Business

    Emily Bailey - Data Science Manager - Strategic Finance - Uber

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    Large-scale Business Forecasting at Uber: Successes and Failures in Adapting to a Changing Business

    Can a machine learning framework keep up with a business changing as rapidly as Uber has over the last 4 years? The core mission of Uber’s finance data science & machine learning engineering team is to provide high-quality forecasts of key business metrics for executives to use in planning upcoming growth and optimizing investments. As Uber has evolved, so too have those algorithms, becoming more complex or simpler as-needed. In some cases, we have failed to keep up with the pace of change. In this talk I discuss where and how we’ve been able to evolve our algorithms to drive enormous impact. I also share my failures — both technical and organizational — so that others can learn from them without the pain of experience.

    Emily Bailey leads data science for Uber’s rides finance organization. Her team focuses on accurate short and long-term forecasting and optimizing Uber’s investment strategy. Prior to joining Uber in 2015, she solved forecasting problems at cleantech startup Opower (acquired by Oracle in 2016). Emily's educational background is in Economics (Duke University, BS) and Computer Science (Columbia University, MS).

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  • 13:50

    PANEL: Female FinTech Founders

  • CHANGING THE FUTURE

  • 14:15

    Creating Leaders in AI in Finance

  • 14:35

    PANEL: Driving The Future of Work

  • 15:00

    CONVERSATION & DRINKS

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