AI risk prediction tool for accurate real-time pricing for insurance

自行车市场（包括电动自行车）在过去一年经历了前所未有的增长，这是由于COVID-19作为公共交通的首选替代品以及政府激励措施，以帮助解决肥胖问题并实现净零碳目标（骑自行车上班，修理自行车券）。由于需求增加（因此，价值增加），预计自行车盗窃和事故将在封锁后飙升。保险对于让人们在事故或盗窃后重新骑上自行车至关重要，但研究表明，拥有适当（如果有的话）保险的骑自行车者数量少得惊人。保险公司根据客户索赔历史以及未来预期损失的风险因素（盗窃/损坏将在何时/何地发生）来定价保费，而不考虑客户的价值观，行为或不断变化的情况。这导致：（1）不准确和不公平的定价。固有的不准确性和缺乏客户细分/理解意味着许多客户支付过高，其他人被定价，造成缺乏平等和包容;（2）缺乏透明度。客户共享数据，但不知道如何使用它;（3）不信任。保险公司有动机拒绝索赔，以最大限度地提高利润，从而损害客户的最佳利益。在发生任何事情之前，保费就已预付，续期价格毫无理由地上涨，尽管情况/行为发生变化，合同仍不灵活，在COVID期间，尽管行为发生变化，保费仍保持不变。Laka旨在通过提供颠覆性的，公平的，以社区为基础的保险模式来克服这些固有的缺陷。客户加入一个风险状况相似的群体，在每个月底，该群体将提交的索赔的实际成本分摊到个人上限。当索赔得到解决时，拉卡得到支付，提供完全的透明度和一致的利益。迄今为止，与其他保险相比，Laka的客户平均节省了25%。虽然已经获得了极大的兴趣和支持，但Laka目前的“风险分组”方法是基于对客户的有限数据/了解，因此最适合特定类型的客户风险概况（“自行车爱好者”）。为了满足需求并“适合目的”，并包容所有类型的乘客，例如大众通勤者，电动汽车-这两个增长最快的细分市场，Laka旨在开发一种新型的人工智能客户“风险”预测工具，以更准确地计算/预测客户的实时风险基于主观数据点，将其适当分组并生成公平的个性化定价，流动性和反映个人行为和情况。

The bike market (including e-bikes) has experienced unprecedented growth in the past year, propelled by COVID-19 as a preferable alternative to public transport and government incentives to help tackle obesity and meet net-zero carbon targets (Cycle to Work, Fix Your Bike vouchers). As a result of increased demand (and as a consequence, value), bike theft and accidents are predicted to soar post-lockdown. Insurance is critical in getting people back on their bikes after accident or theft, however research shows a staggeringly low number of cyclists have appropriate (if any) insurance cover.A fundamental issue with current insurance is that the business model is flawed. Insurers price premiums based on customer claims history combined with a risk-factor of future expected losses (when/where theft/damage will occur), and do not account for customers' values, behaviours or changing circumstances. This results in: (1) Inaccurate and unfair pricing. Inherent inaccuracies and a lack of customer segmentation/understanding means many customers overpay, others are priced out, creating a lack of equality and inclusion; (2) Lack of transparency. Customers share data but do not know how it is used; (3) Distrust. Insurers are incentivised to reject claims in order to maximise profits, acting against customers' best interests. Premiums are paid upfront before anything happens, renewal prices increase with no justification and contracts are in-flexible despite changing circumstances/behaviours, highlighted during COVID where premiums remained the same despite changes in behaviour.Laka aims to overcome these inherent flaws, by offering a disruptive, fair, community-based insurance model. Customers join a group of people with similar risk profiles, and at the end of each month the group splits the actual cost of claims submitted, up to a personal cap. Laka get paid when claims are settled, providing complete transparency and aligned interests. Laka customers have, to-date, saved on average 25% compared to other insurance.Although already gaining significant interest and support, Laka's current approach to 'risk grouping' is based on limited data/understanding of the customer and therefore most suited to a particular type of customer risk profile ('bike enthusiasts'). To meet demand and become 'fit for purpose' and inclusive for all types of riders e.g. mass-commuter, e-mobility -- the two fastest growing segments, Laka aim to develop a novel AI customer 'risk' prediction tool to more accurately calculate/predict customers' real-time risk based on subjective datapoints, grouping them appropriately and generating individual pricing that is fair, fluid and reflects individual behaviours and circumstances.