{"id":29387,"date":"2018-11-13T19:36:51","date_gmt":"2018-11-14T00:36:51","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/predicting-casualties-how-machine-learning-is-revolutionizing-insurance-pricing-at-axa\/"},"modified":"2018-11-15T17:36:32","modified_gmt":"2018-11-15T22:36:32","slug":"predicting-your-casualties-how-machine-learning-is-revolutionizing-insurance-pricing-at-axa","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/predicting-your-casualties-how-machine-learning-is-revolutionizing-insurance-pricing-at-axa\/","title":{"rendered":"Predicting your casualties \u2013 how machine learning is revolutionizing insurance pricing at AXA"},"content":{"rendered":"

The auto insurance pricing model disruption<\/strong><\/span><\/p>\n

Historically, auto insurers have relied on linear regression of a limited number of risk factors, partly reported by the policyholder on a basis of trust, to determine an individual\u2019s insurance premium [1]. Fierce competition among insurers and low customer switching costs have since emerged as the main drivers that force insurers to determine a competitive insurance price which covers their incurred costs [2].<\/p>\n

An adequate prediction of future insurance cost on a case-by-case basis is thus becoming a business imperative [1]. Against that backdrop, machine learning algorithms present an incredible opportunity as they are capable of calculating risk premium estimates based on real-time analysis of massive amounts of policyholder data. Data collection is facilitated through \u2018telematics\u2019, devices that are installed in policyholders\u2019 cars and collect data that make driving behavior measurable (e.g., speeding, harsh breaking) [3]. In combination, this allows for more flexible and accurate auto insurance pricing models such as \u2018safe driving discounts\u2019 or \u2018pay-by-mile\u2019, that take individual behavior into account [4].<\/p>\n

 <\/p>\n

How AXA is using machine learning <\/strong><\/span><\/p>\n

\u00a0<\/strong>AXA has started to position itself at the forefront of embedding machine learning (ML) in their auto insurance pricing approach.<\/p>\n

In the short term, they are forming strategic partnerships with fast-moving tech startups and building on open source ML algorithms to introduce their new pricing solutions with particular focus on their strategic test markets \u2013 Western Europe and Malaysia. In the UK AXA\u2019s partner \u2018By Miles\u2019 is providing the device, software, and app to collect and visualize real-time driver data [5]. In Malaysia AXA has developed and introduced its own app \u2018AXA Flex Drive\u2019 which is compatible with the telematics data collection device from its local partner \u2018CSE Connex\u2019 [6]. AXA is working on complementing the traditional list of relevant risk factors (e.g., driver age) through new items such as real-time vehicle diagnostics and maintenance results. The collected data is processes through the open-source deep-learning framework \u2018tensor flow\u2019 [7].<\/p>\n

In the mid-term, AXA is pursuing its broader vision to drive the transition from being a \u2018bill payer\u2019 to becoming a true partner for its policyholders across all their auto insurance products [8]. Execution on this strategy is planned to take place locally with individual solutions and partnership introduced per country. On a group level, AXA will work on refining its deep learning model (see Figure 1), extending data sources, and building additional partnerships with telematics\/ML providers to increase the accuracy of its risk premium predictions [9].<\/p>\n

\"\"<\/a>
Figure 1: AXA\u2019s deep learning model demo user interface; Source: [7]<\/figcaption><\/figure>\n

Defining the future of auto insurance with machine learning<\/strong><\/span><\/p>\n

To build a long-term sustainable competitive advantage through machine learning, I believe AXA must take three additional steps to complement its short- and mid-term efforts: 1) Take policyholder data collection in house; 2) Synchronize rollout of ML\/telematics solutions to all its geographic locations; 3) Become the key player in accident prevention through real-time insurer-to-driver feedback.<\/p>\n

The reasons for taking these specific actions can be summarized as follows: 1) Owning proprietary data allows AXA to collect and learn from large amounts of data that are not publicly available, which lowers the risk of being disrupted by new market entrants that train their ML algorithm through data made available by non-exclusive partners; 2) Being the first-to-market with integrated ML\/telematics solutions like AXA Flexi Drive across regional markets can provide the necessary impulse for price-sensitive policyholders to switch to AXA and stay due to its strength in rapid innovation for accurate pricing; 3) Taking responsibility for goals beyond its own profitability, AXA can demonstrate superior customer focus and contribute to the joint goal company-customer-government goal of radically reducing fatal accidents \u2013 a concrete suggestion is to embed safety push messages in the AXA application (e.g., fatigue warnings) and offering alternatives to unsafe driving (e.g., taxi contractor).<\/p>\n

 <\/p>\n

Protecting interests of the<\/strong> \u2018see-through\u2019 consumer<\/strong><\/span><\/p>\n

With policyholders granting insurers real-time access to sensitive data, the two most pressing questions are 1) Do we need to create a relevant ethical and regulatory framework to restrict and audit machine learning\/telematics applications in (auto) insurance? and 2) How can the individual be protected from unforeseen uses of the data collected (e.g., use as evidence in lawsuits, speeding tickets)?<\/p>\n

 <\/p>\n

(700 words)<\/p>\n

 <\/p>\n

References<\/strong><\/span><\/p>\n

[1] Verbelen, R., Antonio, K., Claeskens, G., \u201cUnravelling the predictive power of telematics data in car insurance pricing.\u201d Journal of the Royal Statistics Society, <\/em>Applied Statistic 67(5) (2018): 1275\u20131304<\/p>\n

[2] Smith, K., Willis, R. & Brooks, M., \u201cAn analysis of customer retention and insurance claim patterns using data mining: a case study.\u201d Journal of the Operational Research Society <\/em>51(5) (2000): 532<\/p>\n

[3] Deloitte, \u201cAuto insurance telematics – The three-minute guide.\u201d https:\/\/www2.deloitte.com\/us\/en\/pages\/deloitte-analytics\/articles\/auto-insurance-telematics-the-three-minute-guide.html<\/a>, accessed November 2018.<\/p>\n

[4] M. F. Carfora et al., \u201cSoft Computing – A Fusion of Foundations, Methodologies and Applications.\u201d Soft Computing 22<\/em>(236) (2018)<\/p>\n

[5] Insurance times, \u201cAXA partners with insurtech start-up to target less frequent drivers.\u201d April 12, 2018, https:\/\/www.insurancetimes.co.uk\/axa-partners-with-insurtech-start-up-to-target-less-frequent-drivers\/1426847.article<\/a>, accessed November 2018.<\/p>\n

[6] AXA \u201cThe 1st Telematics Motor Insurance That Rewards You For Being A Safe Driver.\u201d https:\/\/www.axa.com.my\/axa-flexi-drive-telematics<\/a>, accessed November 2018.<\/p>\n

[7] Sato, Kaz. \u201cUsing machine learning for insurance pricing optimization.\u201d Google Cloud, March 29, 2017, https:\/\/cloud.google.com\/blog\/products\/gcp\/using-machine-learning-for-insurance-pricing-optimization<\/a>, accessed November 2018<\/p>\n

[8] AXA \u201cHow can insurance make roads safer for all?\u201d https:\/\/group.axa.com\/en\/newsroom\/news\/20170511-insuring-safer-roads<\/a>, accessed November 2018<\/p>\n

[9] Van Egghen, Robert. \u201cAxa using AI to boost manager performance.\u201d 5 April 2017, https:\/\/www.essentia-analytics.com\/essentia-news-and-media\/axa-using-ai-to-improve-investment-performance\/<\/a>, accessed November 2018<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

Insurance giant AXA, frequently classified as \u2018archaic and static\u2019, is now embracing machine learning and real-time driver data collection to determine individual car insurance premiums.<\/p>\n","protected":false},"author":11241,"featured_media":29589,"comment_status":"open","ping_status":"closed","template":"","categories":[1831,2462,730,1653,4426,346,4919,4370,1517,2772],"class_list":["post-29387","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-axa","category-deep-learning","category-insurance","category-insurance-companies","category-insurance-premium","category-machine-learning","category-machinelearning","category-pricing-optimization","category-real-time-tracking","category-telematics","hck-taxonomy-organization-axa","hck-taxonomy-industry-auto","hck-taxonomy-country-france"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nPredicting your casualties \u2013 how machine learning is revolutionizing insurance pricing at AXA - 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