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A brief survey : AI in Risk Management and Insurance

Contents

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Misc

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What's the future looks like for DL & Finance?

  • Igor Halperin: RL on management quora
  • Igor Halperin: deep learning is useful for mortgage risk, as was shown by Kay Giesecke and his group at Stanford, as well as for credit scoring/credit cards, P2P lending quora
  • Igor Halperin: RL on credit scoring: Quora

Finance data property w.r.t. Deep Learning

  • Too noisy to use ML model not stationary
  • Data are small-to-medium, where deep learning needs large data
  • Igor Halperin: (Deep) feedforward neural nets, autoencoders and LSTM seem to be the state of the art of ML in finance, based on what is published and presented at different conferences. Quora
  • Igor Halperin: ML currently un-explainable in finance Quora
  • Financial time-series is a partial information game (POMDP), so needs RL
  • High frequency trading and algorithmic trading are the main drivers of price at short intervals (< 1 day).
  • Opening and closing prices have their own patterns - both in stocks and futures - the two asset classes I have worked with.
  • News and rumors are the driving forces when it comes to multi-day horizons. Specific company news can happen at any time without any prior notice. However, the timeline for some events is known beforehand. Company result schedule, as well as the economic data calendar, are known beforehand.
  • Value investing and economic cycles matter the most when it comes to price changes at a multi-year range. Panel Discussion: Will Artificial Intelligence Create a ‘Useless Class’ of Financial Professionals?
  • Matthew Dixon

Finance Industry

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Finance Technology

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  • How America’s Top 4 Insurance Companies are Using Machine Learning article

AI in Risk Management

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Financial Risk Management Categories

  1. Market Risk
  2. Credit Risk
  3. Liquidity Risk
  4. Operational Risk

Casualty Actuarial Society (CAS) define Enterprise risk management (ERM)

  1. Hazard risk: Liability torts, Property damage, Natural catastrophe
  2. Financial risk: Pricing risk, Asset risk, Currency risk, Liquidity risk
  3. Operational risk: Customer satisfaction, Product failure, Integrity, Reputational risk; Internal Poaching; Knowledge drain
  4. Strategic risks: Competition, Social trend, Capital availability

[GLM] Statistical Method can be classified by this

  • Tree-based methods (e.g. Random Forest, AdaBoost, Gradient Boost Machine);
  • Kernel-based methods (e.g. Support Vector Machine, Kernel Learning);
  • Neuron-based methods (e.g. Neural Network, "Deep Learning" or Restricted Boltzmann Machine);
  • Graph/network-based methods (e.g. Naïve Bayes, Hidden Markov Model, Deep Belief Network).

Insurance moves from reactive to predictive

  • not just using data but also create data
  1. Underwriting: rating(Frequency and Severity)
  • CNN: Identify Roof Type
  • Flight delay insurance
  • Lemonade Insurance
  • Car Insurance chips
  • Healthcare insurance: Medical Imaging eg. Google Deepmind AI detect eye disease two mins paper's video
  • Q: legistitive allow ratemaking? on automobile??
  1. Underwriting: Fraud Detection
  • Credit card fraud detection is successful, will that be able to apply to insurance?
  1. Underwriting: Customer segmentation
  1. Marketing: Personalized marketing
  2. Marketing: Recommendation engines
  1. Claims prediction

make a small twist from stat to ML/DL

  • example 想一下有什麼例子概念是一樣的，但是小小的轉變一下就可以變很厲害 搜集資料？ lemonade?

  • 模型方面

  • Deep Learning’s Killer App for Finance? Article

AI in Insurance

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  • [AAA_AMERICAN ACADEMY OF ACTUARIES]: 2018: BIG DATA AND THE ROLE OF THE ACTUARY report 看看看！

  • Insurance 2030—The impact of AI on the future of insurance note

  • Potential AI value in Insurance Supply Chain (by McKinsey 2017) report starting p.30

  • Potential Applications of ML/AI in Insurance

Ref: Institute for SOA Faculty: Practical Application of Machine Learning Within Actuarial Work by Modelling, Analytics and Insights in Data working party
  • ML/DL in terms of different insurance

SOA: 2019Jan: Machine-Learning Methods for Insurance Applications-A Survey

  • Machine-Learning Methods for Insurance Applications
  • Group Long-Term Disability Jupyter File
  • Long-Term Care Jupyter File
  • Group Long-Term Disability Data Set Comparison
  • Long-Term Care Data Set Comparison

Machine Learning for Actuaries ppt 不錯

[Optimization] Can it improve?

[ratemaking]

-2018: [ML]: Ratemaking application of Bayesian LASSO with conjugate hyperprior paper / slide

  • 作者有Bengio!!: Statistical Learning Algorithms Applied to Automobile Insurance Ratemaking paper note
  • 2016: Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models paper
  • Machine Learning – Applications to Insurance Pricing article
  • AXA: Using machine learning for insurance pricing optimization news

[parameter estimation]

  • [ML-unsupervised?] A MACHINE-LEARNING APPROACH TO PARAMETER ESTIMATION article

[Claims]

[S]

  • Contribution of Data Science to the Solvency 2 regulatory framework: SFCR automated analysis Abstract / slide

[Fraud Detection] Casualty Insurance face lots of fraud.

  • From Logistic Regression in SciKit-Learn to Deep Learning with TensorFlow – A fraud detection case study Blog Part 1 / Blog Part 2 / Github
  • Top 10 Data Science Use Cases in Insurance Article
  • 2018.07 AI IN P&C INSURANCE. Pragmatic Approaches for Today, Promise for Tomorrow. reviews pdf

[loss]

  • [GRU] 2018 Actuarial Applications of Deep Learning_Loss Reserving and Beyond slide / arxiv / DeepTriangle_github_R

  • KPMG: Learning to trust your digital actuary: New technologies can automate loss reserve analysis, providing insurers with more timely data and deeper insights report

[BlockChain]

  • Understanding Blockchain Technology and Its Insurance Implications report
  • The Complete Guide to Blockchain for Insurance Companies article

[Actuarial Education]

[Fairness]

  • Machine learning and fairness in commercial insurance Abstract / slide 圖還不錯

[Company]

  • Allianz's

bot Allie, an online assistant available 24/7 to answer personal lines customers’ questions. It is also using machine learning to carry out risk assessments and support automated underwriting in the small- to medium-enterprise (SME) space.

[Decentralize Insurance] \

Decentralized Insurance Developer Conference 2017 Playlist (Bold is something I'm more interested in.)

  • Keynote: Decentralized Decision Making Video
  • Workshop: How to Develop a Blockchain-based Insurance Solution in a Few Hours Video
  • Bringing the True P2P Mutual Back to Insurance Video
  • Insurance and Blockchain, Show Me the Code! Video
  • Workshop: Smart Contract Car Insurance in 30 min Video
  • Workshop - How to Build Decentralized Insurance Apps Video
  • Fireside Chat - View on Decentralized Insurance From an Insurance VC Video
  • Feeding Authentic Data into Blockchain: Insurance Dapps on Steroids Video
  • A Simple Probability Model For Decentralized Insurance Video
  • Standardized Protocols For Decentralized Insurance Video
  • The Value Behind Hyperledger Video
  • Introduction To Machine Learning For Insurance Use Cases Video
  • Fireside Chat: Prediction Markets For Insurance Video
  • Blockchain & Insurance | Regulation & Compliance Video
  • Risk Pool Tokens Video

Decentralized Insurance Developer Conference 2018 Playlist

Literature review

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BirdView for Risk Management:

  • AI and Machine Learning for Risk Management paper
  • Artificial intelligence, financial risk management and systemic risk paper
  • Overview and Practical Application of Machine Learning in Pricing report
  • Accenture(consulting): Validating Machine Learning and AI Models in Financial report
  • [RL] Reinforcement learning in financial markets - a survey paper
  • Statistical Machine Learning and Data Analytic Methods for Risk and Insurance [paper]notopen

Longitivity v.s. Aging

  • [GAN&RL] 2019: Artificial intelligence for aging and longevity research: Recent advances and perspectives paper
  • 2017: Facing Up to Longevity with Old Actuarial Methods: A Comparison of Pooled Funds and Income Tontines paper

Weather Risk

  • [DL] 2019: NAAJ: Deep Learning at the Interface of Agricultural Insurance Risk and Spatio-Temporal Uncertainty in Weather Extremes paper
  • [DL] 2019: Deep Learning for Improved Agricultural Risk Management paper
  • [MCMC] 2017: Statistical Methods for Weather-related Insurance Claims dissertation
  • [MCMC] 2017: Extreme Value Modelling of Water-related Insurance Claims paper
  • 2017: Satellite Data and Machine Learning for Weather Risk Management and Food Security paper / slide
  • Credit risk management for agri-business under weather uncertainty
  • [RL] Climate change mitigation management using reinforcement learning
  • [ML] Statistical analysis of weather-related property insurance claims abstract / slide

Rare Event

  • [DL] 2017: Google Brain: LEARNING TO REMEMBER RARE EVENTS paper / github / slide / reddit / openreview
  • Lecture: Data Mining for Analysis of Rare Events: A Case Study in Security, Financial and Medical Applications handout

Unsupervised techniques

Deviation detection, outlier analysis, anomaly detection, exception mining Analyze each event to determine how similar (or dissimilar) it is to the majority, and their success depends on the choice of similarity measures, dimension weighting Supervised techniques Mining rare classes Build a model for rare events based on labeled data (the training set), and use it to classify each event Advantage: they produce models that can be easily understood Drawback: The data has to be labeled Other techniques – association rules, clustering

Telematics Devices / UBI

  • 2019: Annals of Actuarial Science: Multivariate credibility modeling for usage-based motor insurance pricing with behavioural data [accepting]
  • [CNN] 2018: Risks: Convolutional Neural Network Classification of Telematics Car Driving Data paper
  • 2018: Claims frequency modelling using telematics car driving data paper / slide / abstract
  • 2018: The Use of Telematics Devices to Improve Automobile Insurance Rates paper
  • 2018: Evolution of Insurance: A Telematics-Based Personal Auto Insurance Study paper
  • 2017: Exposure as Duration and Distance in Telematics Motor Insurance Using Generalized Additive Models paper
  • 2016: Using GPS data to analyse the distance travelled to the first accident at fault in pay-as-you-drive insurance paper
  • 2016: Risk: Telematics and Gender Discrimination: Some Usage-Based Evidence on Whether Men’s Risk of Accidents Differs from Women’s paper
  • 2016: Usage-Based Insurance: A European Case Study using Machine Learning slide

Tail Risk

Sensitivity

  • [St] Reverse Sensitivity Testing: What does it take to break the model? paper / slide

Annuity

  • 2019: Risks: Using Neural Networks to Price and Hedge Variable Annuity Guarantees paper

Reinsurance

  • PnC Reinsurance Modeling Using NumPy and TensorFlow slide

Survival Analysis:(relatively mature)

Claim Frequency

  • [NN] 2018: Risks: An Individual Claims History Simulation Machine paper
  • [ML+R code] Data Science in Non-Life Insurance Pricing: Predicting Claims Frequencies using Tree-Based Models thesis / note / slide
  • [ML] 2018: Predictive Analytics of Insurance Claims Using Multivariate Decision Trees paper

Car Insurance

  • [CNN] SOA_2018: Applying Image Recognition to Insurance_driver behavior assessment paper
  • [CNN] IBM_2017: DarNet: A Deep Learning Solution for Distracted Driving Detection paper
  • [CNN] Using Convolutional Neural Networks to Perform Classification on State Farm Insurance Driver Images paper

Market Risk Manegement:

  • [LSTM+stacked autoencoder] 2017: A deep learning framework for financial time series using stacked autoencoders and long-short term memory paper
  • [GANs] Using Bidirectional Generative Adversarial Networks to estimate Value-at-Risk for Market Risk Management Medium Github
  • [GAN] 2005/Two-Step Disentanglement for Financial Data note paper
  • [GAN/LSTM/CNN] Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets paper
  • [RL] RISK-AVERSE DISTRIBUTIONAL REINFORCEMENT LEARNING: a cvar optimization approach master thesis

Mortgage Risk

  • [DL] 2015: Deep Learning for Mortgage Risk paper

Credit Risk

  • [DL] 2018: Risks: Credit Risk Analysis Using Machine and Deep Learning Models paper
  • [LSTM] 2018: Deep Credit Risk Ranking with LSTM talk+slide
  • [DL] Credit Card Default Prediction Using TensorFlow (Part-1 Deep Neural Networks) medium
  • [ML] 2018: Ensemble Learning or Deep Learning? Application to Default risk analysis. student's paper in TW data
  • [ML] 2017: ANALYSIS OF FINANCIAL CREDIT RISK USING MACHINE LEARNING thesis
  • [ML] 2017: Predictably Unequal? The Effects of Machine Learning on Credit Markets paper

Risk Attitude

  • [2018] Eye-Tracking and Economic Theories of Choice Under Risk paper

Option Pricing:

  • [NN] 2019: Risks: Pricing Options and Computing Implied Volatilities using Neural Networks paper
  • [LSTM] 2018: Option hedging with Long-Short-Term-Memory Recurrent Neural Networks Blog / Deep Hedging Ref Paper / Github
  • [RL] 2018 Igor Halperin: Model-Free Option Pricing with Reinforcement Learning slide / coursera
  • [RL] 2017 Igor Halperin: QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds paper
  • [RL] 2017 Igor Halperin: Inverse Reinforcement Learning for Marketing paper
  • [RL] 2018 Igor Halperin: The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolios paper
  • [RL] 2018 Igor Halperin: Market Self-Learning of Signals, Impact and Optimal Trading: Invisible Hand Inference with Free Energy (or, How We Learned to Stop Worrying and Love Bounded Rationality) paper
  • [RL] 2018: Pricing Options with an Artificial Neural Network: A Reinforcement Learning Approach thesis w/ simplified python / note
  • [NN] 2018 World Quant Blog: BEYOND BLACK-SCHOLES: A NEW OPTION FOR OPTIONS PRICING blog
  • [DNN] 2018: The Day my Computer Won the Nobel Prize (Neural Network Option Pricing) medium w/ python
  • [DNN] 2018: Deeply Learning Derivatives paper / note
  • [GNN] 2017: Gated Neural Networks for Option Pricing: Rationality by Design paper / note / github? / blog
  • [FeedFroward] 2017: Machine Learning in Finance: The Case of Deep Learning for Option Pricing paper / pythob code / note
  • [NN] 2012 An Option Pricing Model That Combines Neural Network Approach and Black Scholes Formula
  • [ANN] 2013: Option Pricing Using Artificial Neural Networks: An Australian Perspective dissertation / note

Pricing:

  • [DNN] 2018: The Day I Taught My Computer What Duration Is (Neural Network Bond Pricing) blog w/ code
  • [ML] Machine Learning Methods to Perform Pricing Optimization. A Comparison with Standard GLMs. paper
  • [ML] Non life pricing: empirical comparison of classical GLM with tree based Gradient Boosted Models slide Github
  • [ML] Tree-based machine learning for insurance pricing. slide / talk
  • [ML] Machine Learning application in non-life pricing. Frequency modelling: an educational case study. report
  • [ML] Data Analytics for Non-Life Insurance Pricing paper
  • [ML] Data Science in Non-Life Insurance Pricing. Predicting Claims Frequencies using Tree-Based Models. Master Thesis
  • [ML] Overview and practical application of Machine learning in Pricing report conclusion: domain knowledge win
  • Tree-Based Machine Learning for Insurance Pricing slide talk

Portfolio Management

  • [GAN+RL] 2018: Adversarial Deep Reinforcement Learning in Portfolio Management paper
  • [RL Model Free] 2017: A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (Crypto) paper
  • [RL] 2015: A Comprehensive Survey on Safe Reinforcement Learning paper note
  • [RL] 2005/J of AI: Risk-Sensitive Reinforcement Learning Applied to Control under Constraints paper note
  • [RL-deepQ] Portfolio Management using Reinforcement Learning paper note
  • [RL] 1996/ Use Of Neural Network Ensembles for Portfolio Selection and Risk Management

Stock Price Prediction

Fraud Detection

Sentimental Analysis

Optimication/Decision Making

  • [RL] Adobe: Risk-averse Decision-making & Control slide ===
  • FinBrain: When Finance Meets AI 2.0. arxiv
  • Deep Learning in Finance. arxiv - 2018
  • Forecasting Economics and Financial Time Series: ARIMA vs. LSTM. arxiv - 2018
  • TSViz: Demystification of Deep Learning Models for Time-Series Analysis. arxiv - 2018
  • Geometric Learning and Filtering in Finance. arxiv - 2017
  • On Feature Reduction using Deep Learning for Trend Prediction in Finance. arxiv - 2017
  • Forecasting Volatility in Indian Stock Market using Artificial Neural Network with Multiple Inputs and Outputs. arxiv - 2016
  • Financial Market Modeling with Quantum Neural Networks. arxiv - 2015
  • Identifying Metaphoric Antonyms in a Corpus Analysis of Finance Articles. arxiv - 2013
  • Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts. arxiv - 2013
  • Identifying Metaphor Hierarchies in a Corpus Analysis of Finance Articles. arxiv - 2012

Credit scoring:

PF:

MCMC

  • [NICE-MC] A-NICE-MC: Adversarial Training for MCMC paper
  • [MH-GAN] Metropolis-Hastings Generative Adversarial Networks paper

Literature ML Finance focused

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  1. Neurocomputing - Machine learning in finance link
  • Stock portfolio selection using learning-to-rank algorithms with news sentiment
  • Twitter data models for bank risk contagion
  • Bank distress in the news: Describing events through deep learning
  1. Advances in Financial Machine Learning book

Actuarial Science Program Worldwide

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  1. ETH Zurich / Department of Math / RiskLab Switzerland Course 強
  2. RiskLab Finland website
  3. The School of Risk & Actuarial Studies at UNSW Sydney Course 還好
  4. UIUC University of Illinois at Urbana-Champaign, Department of Mathematics, Actuarial Program Facultyx3
  5. GSU CEAR Georgia State University / Center for the Economics Analysis of Risk Lots of Conference Department of Risk Management and Insurance
  6. UNSW University of New South Wales, Australia The School of Risk & Actuarial Studies Focus on AIPAR (Australian Institute for Population Ageing and Research) and the Centre of Excellence in Population Ageing and Research (CEPAR).
  7. Wisconsin University of Wisconsin–Madison
  8. QRSL: Quantitative Risk Solutions Lab web / Heriot-Watt University, School of Mathematical & Computer Sciences, Actuarial Mathematics & Statistics
  1. University of Barcelona, the Research Group on Risk in Insurance and Finance web
  1. GitHub: Short Course: Applied Machine Learning for Risk Management GitHub HW: Python / Income Prediction / Fraud Detection / Credit Scoring
  2. GitHub: Practical Machine Learning Github Exercise: Survival Detection / Feature Engineering / Classification / Fraud Detection / NLP Cheating Detection

Online Course by NYU Igor Halperin Machine Learning and Reinforcement Learning in Finance Specialization Course 1: Guided Tour of Machine Learning in Finance 4week 21hrs Course 2: Fundamentals of Machine Learning in Finance 4weeks 17hrs W4開始有RL Course 3: Reinforcement Learning in Finance 4weeks 22hrs Course 4: Overview of Advanced Methods of Reinforcement Learning in Finance 4weeks 14hrs

The University of Melbourne Laval University, Canada The University of Hong Kong University of Waterloo, Canada University of Iowa

EBOOK

Australia / RiskLab

PhD Research Projects link

  1. PhD Project: Simulation-based stochastic control for portfolio management
  2. PhD Project: Long-term decision making under uncertainty for sustainable life-cycle retirement management
  3. PhD Project: Cyber security (analysis and evaluation)
  4. PhD Project: Cyber security (model and product design)
  • Develop a quantitative pricing model for cyber-insurance.
  • Develop a model for determining resource allocations to combat cyber-crime.
  1. PhD Project: Non-Linear Least Square Monte-Carlo Algorithms
  2. PhD Project: Local Volatility Inference through Optimal Transport
  3. PhD Project: Statistical analysis and visualisation of spatially distributed big time series electricity usage data
  • Cognostics methods for electricity usage time series data with spatial and spatio-temporal structure.
  • Develop visualisation methods for spatial and spatio-temporal cognostics for spatially distributed big time series.
  • Develop inferential methods for spatially distributed large time series electricity usage data.
  1. PhD Project: Statistical analysis of big time series electricity usage data
  • Develop scalable method to analyse a large number of time series.
  • Extend the scalable method developed to analyse a large number of time series to work in real-time or near real-time.
  1. PhD Project: Optimal retirement age for a sustainable superannuation system
  2. PhD Project: Managing and modeling longevity risk in the 21st century
  3. PhD Project: Alternative stochastic models in Finance
  4. PhD Project: Health of an ageing population
  5. PhD Project: Designing a method to hedge European (social) welfare risk
  • The identification and modelling of causes of increased welfare costs (retirement costs, unemployment costs, disability costs etc).
  • The design of a bond like product that will smooth those costs by transferring some of the risk from individual countries to the issuer of the European bond (e.g. the European central bank).
  1. PhD Project: Solving Portfolio Selection Problems under Gaussian Process
  2. PhD Project: Contribution of real option to sustainable agriculture
  3. PhD Project: Conditional Phase – Type Model in Pension and Health Insurance Premium Calculations
  4. PhD Project: Real time car insurance for the New Age Allianz
  5. PhD Project: Assessing the Real Option Approach for Mining projects
  6. PhD Project: Pricing in a competitive stochastic insurance market
  7. PhD Project: The impact of climate change in pricing reserve in a competitive insurance market

ML DL Finance Conference

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--

Sponsored by SOA already 50+ years

2018 Actuarial Research Conference program

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Sponsered by UNSW

[2018] 22nd International Congress on Insurance: Mathematics and Economics Web program

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[2019/June/Switzerland] Insurance Data Science Conference Web [2018/London] Insurance Data Science Conference Web / github [2017/Paris] R in Insurance Web [2016/London] R in Insurance Web [2015/AMSTERDAM] R in Insurance Web [2014/London] R in Insurance Web [2013/London] R in Insurance Web

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Sponsered by ETH Zurich

[2019/ETH Zurich] Risk Day 2019 Web [2018/ETH Zurich] Risk Day 2018 Web

  • AI in risk management and risk management in AI slide
  • Hedging derivatives under market frictions using deep learning techniques slide [2017/ETH Zurich] Risk Day 2017 Web
  • Machine learning in mortality modeling slide [2016/ETH Zurich] Risk Day 2016 Web [2015/ETH Zurich] Risk Day 2015 Web
  • Insurance-Linked Securities slide
  • Risk models in practice: The view of a non-mathematicianslide [2014/ETH Zurich] Risk Day 2014 Web
  • 20 years of RiskLab slide [2013/ETH Zurich] Risk Day 2013 Web

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Sponsered by QRSL Quantitative Risk Solutions Lab web / Heriot-Watt University, School of Mathematical & Computer Sciences, Actuarial Mathematics & Statistics

  • SFRA Colloquium 2019 and HW-UoE-ISM Workshop web: Scottish Financial Risk Academy methodological and computational aspects of Data Science, Machine Learning and their applications and developments within Financial Risk, Financial Mathematics and Insurance

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Sponsered by University of Barcelona

2016 Data Science & Big Data for Actuaries live

Sponsered by GSU CEAR

GSU CEAR Archieved Conference 好像還好？

  • 2019 CEAR/Huebner Summer Risk Institute
  • Welfare, Preferences, and Risk: Theory, Behavioural Evidence, and Policy
  • Georgia State FinTech Conference 2019
  • The Chicago School and Research Related to Organizational and Market Risk, a 50-Year Perspective
  • Behavioral and Experimental Public Choice Workshop 2019
  • 6th Workshop in Behavioral and Experimental Health Economics
  • CEAR/MRIC Behavioral Insurance Workshop 2018

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Sponsered by Columbia

[2018] Machine Learning in Finance Workshop 2018 by The Data Science Institute (DSI) at Columbia University and Bloomberg Web

  • A High Frequency Trade Execution Model for Supervised Learning slide
  • Big Data's Dirty Secret slide

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Sponsered by Finland RiskLab

Conference run by Finland RiskLab web

  • 2019 RiskLab/BoF/ESRB Conference on Systemic Risk Analytics
  • 2018 RiskLab/BoF/ESRB Conference on Systemic Risk Analytics
  • 2018 Special session on Machine Learning and Network Analytics in Finance
  • 2017 RiskLab/BoF/ESRB Conference on Systemic Risk Analytics
  • 2017 Special session on Machine Learning and Network Analytics in Finance
  • Special session on Systemic Financial Risk Analytics
  • 2016 RiskLab/BoF/ESRB Conference on Systemic Risk Analytics
  • Special session on Systemic Risk Analytics
  • Special session on Systemic Risk Analytics
  • 2015 RiskLab/BoF/ESRB Conference on Systemic Risk Analytics
  • Special session on Systemic Risk Analytics and Macroprudential Policy Session on Systemic Risk Analytics and Macroprudential Policy

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International Gerber-Shiu Workshop Actuarial Research Conference

-- Talks in Financial and Insurance Mathematics This is the regular weekly research seminar on Insurance Mathematics and Stochastic Finance. http://www.risklab.ch/news-and-events/talks-in-imsf.html

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Blog or Videos

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From machinelearning.ai

  • Insurance Analytics: Application of analytics / machine learning techniques in insurance industry blog / YT -Customer lifecycle in insurance business -Fundamental of insurance business -Application of various analytics techniques along with the customer life cycle
  1. Use of predictive analytics / supervised machine learning algorithms
  2. Use of collaborative filtering
  • D1Conf | Introduction To Machine Learning For Insurance Use Cases YT
  • Artificial Intelligence impact on Insurance Companies in 2017 YT
  • Machine Learning: Predict Car Insurance Fraud with UK Accident Data YT12min
  • Innovation and InsurTech: How to get practical with artificial intelligence in insurance YT40min
  • AI, Machine Learning and Chatbots Improving Insurance Profitability & CX YT52mins / blog
  • Insurance and machine learning YT16mins
  • OpenStack at Progressive Insurance Data Science and Machine Learning (Progressive combines OpenStack, Kubernetes and OpenShift) YT
  • Insurance Insights Artificial Intelligence YT20mins
  • How Hepstar uses data analytics and machine learning to boost travel insurance revenue YT
  • Using Machine Learning to Increase Health Insurance Coverage – Ricky Hennessey – ML4ALL 2018 YT25mins
  • Lowering Insurance Rates Through Machine Learning – Tomer Kashi & Ori Blumenthal, Skywatch.AI-Drone Insurance YT25mins
  • Risk Roundup Webcast: Machine Learning on Insurance Data to Predict Hospitalization YT1hr
  • [claims] Webinar: Are you Ready? Reshaping Insurance with Artificial Intelligence, focus on claims YT55mins
  • Artificial Intelligence and Machine Learning — Implication for P&C Insurance YT1.5hr
  • [claims] Humanizing Insurance Claims with Artificial Intelligence YT1hr
  • [Jupyter] Machine learning with sample Insurance Dataset YT17mins india accent
  • Artificial Intelligence Task Force – Medical, Healthcare, and Insurance 1/18/19 YT3.5hr
  • Webnir: Rise of Robotic Process Automation ( RPA ) and Artificial Intelligence ( AI ) in Insurance YT1hr India accent
  • [Lecture] Machine Learning Lecture 16 “Empirical Risk Minimization” -Cornell CS4780 SP17 YT46mins

DataSet or Kaggle

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  1. Driver Telematics Analysis

Analysis w/ details github

  1. Prudential Life Insurance Assessment article github

Libraries

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architecture

Startups

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Longetivity v.s. Biological Age

  • 2018: GERO: AI to predict biological age based on smartphone and wearables data news / paper

Appendix

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AI-in-FinTech-Market-Map-Image3

Screen-Shot-2018-07-18-at-7.38.18-PM-1024x467

Artificial-Intelligence-Financial-Industry

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