O slideshow foi denunciado.
Utilizamos seu perfil e dados de atividades no LinkedIn para personalizar e exibir anúncios mais relevantes. Altere suas preferências de anúncios quando desejar.

The four trends driving the insurance industry

342 visualizações

Publicada em

Using new advances in machine learning, we tackled 4 pain points of the insurance industry: Accelerated Underwriting process, Personalized Pricing, Fraud Detection & Improved Customer Retention and Experience

Publicada em: Dados e análise
  • Sex in your area is here: www.bit.ly/sexinarea
       Responder 
    Tem certeza que deseja  Sim  Não
    Insira sua mensagem aqui
  • Dating for everyone is here: www.bit.ly/2AJerkH
       Responder 
    Tem certeza que deseja  Sim  Não
    Insira sua mensagem aqui
  • Sex in your area for one night is there tinyurl.com/hotsexinarea Copy and paste link in your browser to visit a site)
       Responder 
    Tem certeza que deseja  Sim  Não
    Insira sua mensagem aqui
  • Girls for sex are waiting for you https://bit.ly/2TQ8UAY
       Responder 
    Tem certeza que deseja  Sim  Não
    Insira sua mensagem aqui
  • Meetings for sex in your area are there: https://bit.ly/2TQ8UAY
       Responder 
    Tem certeza que deseja  Sim  Não
    Insira sua mensagem aqui

The four trends driving the insurance industry

  1. 1. KOI OS P R E S E N T A T I O N Adding Value to the Insurance Industry Intelligence
  2. 2. 1 NEW DEVELOPMENTS IN THE INSURANCE INDUSTRY 2 PROCESS AUTOMATION 3 PERSONALIZED PRICING 4 RISK MANAGEMENT 5 TEAM TABLE OF CONTENTS
  3. 3. 1PART NEW DEVELOPMENTS IN THE INSURANCE INDUSTRY PROCESS AUTOMATION PERSONALIZED PRICING RISK MANAGEMENT CUSTOMER EXPERIENCE OUR TEAM
  4. 4. Through the unlocking of potential in these 4 areas! PROCESS AUTOMATION PERSONALIZED PRICING ADAPTED RISK MANAGEMENT CUSTOMER EXPERIENCE 01!Koïos Intelligence Inc.! NEW ADVANCES IN BIG DATA AND MACHINE LEARNING ARE DISRUPTING THE INSURANCE INDUSTRY
  5. 5. 02!Koïos Intelligence Inc.! UNLOCKING VALUE THROUGH PROCESS AUTOMATION ! OUTDATED MODEL NEW DEVELOPMENTS EVOLVED MODEL 02 !01! •  Insurance claims and property valuations taking a long time to be processed and involving too many different staff members, resulting in high labour costs and poor customer satisfaction! •  Optical character recognition technology to allow fast transformation of paper documents into computer readable format! •  Improved data analysis using new machine learning models! ! ! •  Improved productivity and reduced costs for complex processes such as property valuations •  Claims processed faster through automated image classification
  6. 6. 03!Koïos Intelligence Inc.! UNLOCKING VALUE THROUGH PERSONALIZED PRICING ! OUTDATED MODEL NEW DEVELOPMENTS EVOLVED MODEL 02!01! •  General price segmentation resulting in undeserved customers who do not fit in general categories •  Product recommendations based on advisor’s experience •  Big amounts of personal data collected though smartphones and other devices (e.g. health, lifestyle, driving behaviour, etc.) •  New predictive analysis tools use new variables (e.g. security, traveling, multiple coverage, etc.) allowing more accurate pricing than before •  Recommendation of more personalized products resulting from in-depth analysis of customer data •  Novel personalized pricing using individual risk profile
  7. 7. 04!Koïos Intelligence Inc.! UNLOCKING VALUE THROUGH RISK MANAGEMENT ! OUTDATED MODEL NEW DEVELOPMENTS EVOLVED MODEL 02!01! •  Risk groups classified using static data from very few metrics(e.g. age, sex, and medical history) •  Many flaws and false alarms in insurance fraud detection •  The advent of artificial intelligence is enabling the development of extremely high- capacity models that can analyze previously unimaginable amounts of data, that are extremely powerful in a fraud detection context •  Insurance fraud detected more accurately using holistic customer analysis •  Significant reduction of false positives •  Risk group classified using a wide spectrum of data such as social networks, clickstreams and web analytics
  8. 8. 05!Koïos Intelligence Inc.! UNLOCKING VALUE THROUGH CUSTOMER EXPERIENCE ! OUTDATED MODEL NEW DEVELOPMENTS EVOLVED MODEL 02!01! •  Inconsistent customer service quality, dependant on agent experience and skill •  Customer experience analysis after losing the client •  Churns (decommissioning rate) customer predicted and intercepted in real time •  Proactive customer service by identifying new needs during important life events of customers. Following up on quotes and contract terminations •  Robust automated learning models that ensure constant reliability in spite of modifications in the data •  These models allow the precise interpretation of client responses in real time
  9. 9. 2PART NEW DEVELOPMENTS IN THE INSURANCE INDUSTRY PROCESS AUTOMATION PERSONALIZED PRICING RISK MANAGEMENT CUSTOMER EXPERIENCE OUR TEAM
  10. 10.  Analyze customer’s profile and assess customer’s need!  CHATBOT!  Make a Proposal (offer/quote)! If quote is approved!  Resiliate the contract!  Retract (cancel)!  Modify the personal situation!  Request documents from client!  Before Sale!  Sale!  After Sale! Proposal! Smart contract! Adjust quote! Modifications / Endorsements! PERSONALIZED PRICING CLIENT EXPERIENCE & COMPENSATION RISKMANAGEMENT PROCESS AUTOMATION AUTOMATION OF THE SUBSCRIPTION PROCESS 06!Koïos Intelligence Inc.!   Subscribe a contract!  Modify the contract!
  11. 11. 1! Digitization is the process of transforming forms and handwritten documents into a computer-readable format using machine learning algorithms! Digitization ! Fraud Detection! Manual Work! Online Marketing! Call Centres! Automatization of Manual Work! Reduce the number of errors made by manual labor! Claims Processing! Process claims and automate changes and simple queries (virtual agents)! Fraud detection! Digitization is the first step in collecting textual data that can improve fraud detection models! Acquiring customers online! Digitization enables an end-to-end online distribution channel through targeted generation of leads on social media! 07!Koïos Intelligence Inc.! PROCESS AUTOMATION DIGITAZION APPLICATIONS
  12. 12. 08!Koïos Intelligence Inc.! Classification! FAQ! 1 (Future work) Conversion of speech to text 2 Classification of the user’s message in predetermined categories (e.g. date, amount $, etc.) 3 Searching the DB allows to determine if the category found is adequate knowing the previous question 4 If needed, the answer is stocked in a DB 5 If the answer is adequate then the chatbot can send the next question 6 Otherwise the message is sent to a second algorithm which will send an answer based on predefined questions PROCESS AUTOMATION CHATBOT
  13. 13. From the profile of a client and his claim history, the pricing process begins with a model that identifies factors with a high predictive potential to then build a description of severity as a function of the characteristics of the clients! 09!Koïos Intelligence Inc.! Prediction! Coverage! Vehicle! Profile! !Vehicle! • Brand of the car! • Weight! • Type of fuel! !Profile! • Age! • Civil status! • Number of drivers! !Coverage! • Frequency of payments! • Term! PROCESS AUTOMATION AUTOMATED CLAIM SEVERITY ASSESSMENT
  14. 14. Using the data of 180,000 claims, we designed and tested a model to predict the claim severity from 116 categorical factors (e.g. car brand) and 14 continuous factors (e.g. volume of the car’s engine)! Optimal predictions are reached by combining the performance of all models. Value of claims predicted with a mean absolute error of 1110.01362! Fig. 3 Architecture of a model of claim severity prediction! The mean absolute error is a simple and commonly used measure that indicates the spread between predicted and observed values. It is the arithmetic average of the absolute value of the spreads! 10!Koïos Intelligence Inc.! PROCESS AUTOMATION AUTOMATED CLAIM SEVERITY ASSESSMENT
  15. 15. 3PART NEW DEVELOPMENTS IN THE INSURANCE INDUSTRY PROCESS AUTOMATION PERSONALIZED PRICING RISK MANAGEMENT CUSTOMER EXPERIENCE OUR TEAM
  16. 16. Customized services are becoming increasingly popular and have the potential to create huge, high-quality data streams, increase the frequency of client-insurer interactions, and significantly reduce the number of claims by improving health, safety, and the security of the insured! INSURANCE INDUSTRY! Car! House! Health! Life! Safe Driving & Support! •  Alerts and rewards for safe driving! •  Alerts for theft or damage! •  Troubleshooting service! •  Assistance to find a car! Healthy Lifestyle! •  Rewards for a healthy lifestyle! •  Health checks and access to behavioral data! •  Schedule a meeting with a doctor! •  Diagnosis and remote consultation! Home security! •  Remote monitoring and alerts! •  Automatic shutdowns during water leakage & faster fire extinguisher settings ! Energy! •  Aiming towards a smart home! •  Tips for saving energy! Financial Planning! •  Pension and real estate planning! •  Tips on personal finances! 11!Koïos Intelligence Inc.! PERSONALIZED PRICING INSURANCE PERSONALIZATION
  17. 17. The Internet offers insurers a holistic view of their policyholders, improving the quality of their models and enabling them to offer personalized products! Car! House! Health! Life! Constant flow of consumer data! Personalized insurance products! Score Adjustment! ✓  New markets ! ✓  Improved models ! ✓  Better client retention ! Client Score! Sharing data with the ecosystem! 12!Koïos Intelligence Inc.! PERSONALIZED PRICING INSURANCE PERSONALIZATION
  18. 18. SLEEP DIET PHYSICAL CONDITION ROUTINE 46% 23% 59% 38% Don’t leave any data on the table Smartphones can provide information allowing us to better price a life insurance premium while improving the customer experience, and encouraging customers to have a better lifestyle. 13!Koïos Intelligence Inc.! LIFE INSURANCE
  19. 19. 2! Our Research & Development team designed and tested a model on data containing the classification of more than 80,000 insured persons to predict their risk level, from 130 categorical factors (e.g. medical conditions) or continuous (e.g. BMI) When the three models are used altogether, we obtain an accuracy value of 0.67921, measured with the quadratic weighted Kappa! Fig. 2 Architecture of the model of risk classification that an insured person represents! The quadratic weighted Kappa is a measure of agreement between two assessors, adjusted to take into account the probability to predict correctly, but randomly i.e. without any good reason to predict correctly; the potential values go from -1 (total disagreement) to 1 (total agreement). A value of zero is expected if all the agreements are due to randomness 14!Koïos Intelligence Inc.! PERSONALIZED PRICING INSURANCE PERSONALIZATION
  20. 20. 4PART NEW DEVELOPMENTS IN THE INSURANCE INDUSTRY PROCESS AUTOMATION PERSONALIZED PRICING RISK MANAGEMENT CUSTOMER EXPERIENCE OUR TEAM
  21. 21. 15!Koïos Intelligence Inc.! LEGAL COMPLIANCE All legislative needs (laws and regulations): Personal Data Protection and Electronic Data Protection Act (PIPEDA) Customer data management according to the type of information (e.g. public, confidential, etc.) ECONOMIC COMPLIANCE Remote payment and following compliance for secure payment (phone, Internet) SEPA rules TECHNOLOGICAL COMPLIANCE The data (servers) must remain in Canada, since the networks of the Internet providers are so interconnected between them, it would be impossible to prove that the data are not found in American territory (so that the American government can have access according to the Patriot Act) Koïos Intelligence chose Amazon. SOCIAL COMPLIANCE Anti-Spam Law (abbreviated C-28) Get the consent of customers before contacting them, "almonds" are very very high and especially cumulative for this law RISK MANAGEMENT FACILITATING CONFORMITY AND SECURITY
  22. 22. PUBLIC AND PRIVATE DATA •  Exponential increase in the volume of reported data •  Increased frequency of quarterly and annual reports •  Quantitative and Qualitative disclosures Required NEW ECOSYSTEM •  Complexity of the group •  New rules and regulations to comply •  Reconciliations required •  Reliable and accurate data ACCELERATION •  Limited resources and tight schedule •  The Pillar III Annual Return will change from 20 weeks to 14 weeks •  Requires internal planning to deal with workload fluctuations INTERSECTION •  Solvency II and IFRS 17 focus on assessing and managing the risks facing the insurer and quantifying future risks •  Assets and liabilities are likely to use a current valuation method, which is expected to increase the volatility of financial statements relative to current standards •  A best estimate basis is used in expected future cash flows •  The discount rate used is the sum of the risk-free rate and the liquidity premium Technology! Technical Provisions! SCR / MCR! Financial steps ! Investment! Reporting Xbrl ! 0%! 100%! Large workload! t! Data required to evaluate:! MCR, SCR, ORSA, QRT! ! Insurance companies are now required to provide more and better information in a shorter period of time on a quarterly and yearly basis. The Solvency II standard has implemented prudent supervision of both own funds and technical provisions in order to limit the probability of insolvency of an insurer. IFRS 17 includes the request for financial information relating to an insurer's own funds and technical provisions in order to assess an overall economic value! SOLVENCY II / TSAV AND IFRS 17 16!Koïos Intelligence Inc.!
  23. 23. ONLINE SERVICES AUTOMATION BIG DATA WHY IT IS TIME TO ACT? Democratization of internet and overwhelming simplicity to obtain false identity online triggers an increase in organized frauds Several repetitive processes such as reading the claim of a client will largely be simplified and even completely automated, in some cases We are currently living in the midst of a technological revolution where it is now possible to analyse any kind of data which were once left to an expert’s judgement, for instance the images of a car crash RISK MANAGEMENT FRAUD DETECTION IN INSURANCE 17!Koïos Intelligence Inc.!
  24. 24. CLAIMANT DATA COVERAGE DATA DAMAGE DATA Modelling of variables First step is to structure the data of the claimant, coverage and damage using different machine learning algorithms (see example of structured variables below) Resource requirements Estimation of resources needed to launch an investigation for each of the fraudulent claims Gain expectation Estimation of resources and probability of fraud allow us to calculate the potential gain to launch an investigation for a given claim Ranking We can then rank the claims by order of priority to maximize the work of the investigation team Variables Analysis & processing of data Coverage -  New coverage -  Profile of the device used to buy the coverage -  Value of claim Damage -  Geolocation of the damage -  Type of damage -  Missing police report Main Model Once the data is in a computationally processable form, it can be introduced in the main model (the main models are presented in the next page. Not that it is possible, even recommended, to use a set of models) Claimant -  Important claim history -  Declared revenues does not correspond objects’ value Fraud probability The main model calculates the probability that the claim is fraudulent by taking into account all introduced data RESULTS 18!Koïos Intelligence Inc.! RISK MANAGEMENT FRAUD DETECTION IN INSURANCE CLAIMS
  25. 25. ✓  Create an end-to-end system capable of calculating the dollar amount of the claim using images of a car crash and a minimum of the information about the car involved in the accident OBJECTIVES OF THE PROJECT! •  When a claim is received, the images of the accident and the car data are automatically processed by a deep learning model that estimates the amount of the claim •  Another model then predicts the probability that this claim is fraudulent by analyzing the spread between the amount estimated by the model and the required amount entered by the client (in practice, it is also useful to transform that probability in a score) •  It is then possible to incorporate that probability/score in the main model which will decide, given all the other information (claimant, coverage), if it is necessary to pursue the investigation OURAPPROACH ILLUSTRATION! CLIENT BENEFITS! ✓  Allow to incorporate data, which up to now could only be analyzed at the beginning of an investigation ✓  Enriches the client’s existing models by using another explanatory variable IMAGE RECOGNITION 19!Koïos Intelligence Inc.! RISK MANAGEMENT FRAUD DETECTION IN INSURANCE CLAIMS Information processing Main model to predict cost of damages Comparison of predicted vs observed values to test model accuracy
  26. 26. Our Research & Development team analyzed the data of 300,000 credit card transactions, 500 of which were fraudulent SVM Neural Network Auto Encoder True positive 68 84 73 False positive 35 2501 953 True negative 49965 47499 49047 False negative 25 9 20 Most of the models besides the SVM resulted largely in false alarms (false positives)! Fig. 1 Results on the testing set - 50,093 transactions/103 frauds! ➢  The optimal precision is reached by using an ensemble composed of the models above, but assigning a greater weight to the SVM to account for the fact that it produces less false alarms than other models 20!Koïos Intelligence Inc.! RISK MANAGEMENT FRAUD DETECTION IN CREDIT CARDS
  27. 27. 5PART NOUVEAUTÉS DANS L’UNIVERS DE L’ASSURANCE ROBOTISATION DES PROCESSUS TARIFICATION PERSONNALISÉE GESTION DES RISQUES EXPÉRIENCE CLIENT NOTRE ÉQUIPE NEW DEVELOPMENTS IN THE INSURANCE INDUSTRY PROCESS AUTOMATION PERSONALIZED PRICING RISK MANAGEMENT CUSTOMER EXPERIENCE
  28. 28. CLIENT EXPERIENCE 1! 2! 3! 4! 5! 6! 7! 1! 2! 3! 4! 5! 6! 7! Registration in a new! ecosystem! Buying a new vehicle – the offer an insurance product by the insurer ! The offer of a personalized life insurance product that rewards clients with a good lifestyle ! Receiving an alert during an abnormal event! LIFETIME Alerts sent in case of damage & vehicle search by customer assistance ! Assistance in case of illness and making appointments online with a specialist! Buying a new house - the offer of a home insurance by the insurer! 6! CUSTOMER EXPERIENCE BETTER SUPPORT DURING KEY LIFE EVENTS OF CUSTOMERS 21!Koïos Intelligence Inc.!
  29. 29. RETENTION •  It is more expensive to acquire a new client than to retain an existing one •  Clients that renews are the most profitable on the long run Key: determine the elasticity of the price in order to know what increase a client is willing to accept 22!Koïos Intelligence Inc.! There is no doubt that any change in the pricing has an impact on customer retention. Intuitively, a decrease in pricing is a guarantee of a high retention rate whereas an important increase will bring more clients to turn to competition.! CUSTOMER EXPERIENCE DETERMINE CUSTOMER PRICE SENSITIVITY
  30. 30. With the use of new models CONTEXT •  The automobile insurance domain works with cycles presenting stages of profitability & non- profitability! •  In non-profitability stages, insurance companies generally have the reflex to increase the premium in order to reduce their losses! •  Nevertheless, very large increases can have as a consequence to massively repulse customers towards the competition. A too high of an attrition rate could have a negative effect on long-term profitability of the company! •  A sound management of rate increases is thus of paramount importance for an insurance company! •  New tools allow the simulation of insurance portfolio owned by an insurer as a function of the change in the rate proposed to each of the insured! INNOVATION The availability of such a tool could result in increased profits and would allow to anticipate attrition RESULTS 23!Koïos Intelligence Inc.! CUSTOMER EXPERIENCE DETERMINE CUSTOMER PRICE SENSITIVITY
  31. 31. SEGMENTATION Client’s data are composed of 20+ variables (individuals, social, demographics, coverage, etc.). A segmentation using unsupervised learning is necessary. COMPETITION METRICS An important factor in retention are the prices of competitors. A proxy indicator for these external factors shall be designed and incorporated in the model The model must predict, for a specific profile and external competition variables, the probability of retention of a client PREDICTION Extract data using statistical learning 24!Koïos Intelligence Inc.! CUSTOMER EXPERIENCE ENSURE CUSTOMER RETENTION
  32. 32. Automated learning models stand out from traditional models by their robustness, i.e. their constant reliability in spite more or less important modifications in the data. The reliability of automated learning models to predict retention has proven effective Avantages! Disavantages! Random Forest! Factor selection and robustness! Poor prediction performance! Logistic Regression! High performance in prediction! Absence of robustness! Gaussian Process! High robustness and performance of prediction! Computationally intense! Less traditional learning models perform better, generally, than logistic regression. This is explained notably by the more important generalization of automated learning models, with the trade off being the needs for enormous computational capabilities Traditional models are generally effective in the case of small changes in the rate of retention, but lack precision in case of more unusual changes. Our team has worked with models capable of good and robust performance in situation of most extreme rate changes Learning model to predict retention 25 Koïos Intelligence Inc.! CUSTOMER EXPERIENCE ENSURE CUSTOMER RETENTION
  33. 33. NOUVEAUTÉS DANS L’UNIVERS DE L’ASSURANCE ROBOTISATION DES PROCESSUS TARIFICATION PERSONNALISÉE GESTION DES RISQUES OUR TEAM NEW DEVELOPMENTS IN THE INSURANCE INDUSTRY PROCESS AUTOMATION PERSONALIZED PRICING RISK MANAGEMENT CUSTOMER EXPERIENCE 6PART
  34. 34. OUR TEAM MOHAMED HANINI CEO & FOUNDER MANUEL MORALES PARTNER & CO-FOUNDER Mohamed serves North American and global companies in the financial services. His areas of expertise include risk management, quantitative trading, business strategy and digital transformations. Mohamed has done over 10 years of research and development in statistics, finance and operations research. He taught for 7 years at the University of Montreal several actuarial and risk management courses such as life insurance, property and casualty insurance, statistics, financial mathematics and risk management. As a Professor in the Department of Mathematics and Statistics of the University of Montreal, he has accumulated over fifteen years of experience in collaborative research projects in partnership with key industry players. He has experience leading collaborative technology transfer projects in algorithmic trading, market micro-structure modeling, energy markets and automation in quantitative finance through machine learning. Our team is composed of experienced scientists and seasoned consultants in the fields of mathematics, statistics, economics, computer science, operations research, quantitative finance and risk management dedicated to innovate through artificial intelligence technology and to develop business analytics solutions and automation for today's businesses. ! !
  35. 35. C O N T A C T U S ADDRESS 5155 Chemin de la rampe Suite J1249, Montréal (Québec), H3T 2B1 E-MAIL mohamed.hanini@koiosintelligence.ca morales@koiosintelligence.ca 514.927.6739 514.730.4975
  36. 36. T H A N K Y O U QUESTIONS?

×