Cocoon 'buy now, pay later' platform development

现在购买，稍后付款(BNPL)意味着消费者可以今天买东西，以后再付款。这对消费者和企业都是好事：消费者可以将高价值商品的成本分摊到易于管理的分期付款中，因此，提供BNPL的企业的销售额最高可增加30%。企业与BNPL融资提供商合作，后者为购买提供资金，并在未来从消费者那里收取还款。许多企业向消费者提供免息的BNPL--企业支付利息，这一成本被他们所经历的销售增长所抵消。这对企业和消费者来说是双赢的：企业创造了更多的销售额，消费者可以获得免息信贷。为了确定他们应该向哪些消费者提供BNPL条款，金融公司会对潜在消费者进行信用检查。信用检查是用来检查消费者的信用记录，让贷款人知道消费者过去曾负责任地使用信贷，让他们知道消费者未来偿还的可能性有多大。然而，在英国，有1000-1400万消费者(占成年人口的20%-25%)很难从主流的BNPL提供商(PWC)那里获得信贷。这是因为，例如，这些消费者可能以前没有申请过信贷或有轻微的不良信用记录。这些消费者经常被拒绝享受免息的BNPL，即使他们可能信誉良好，有能力偿还贷款。这对企业和消费者都是不利的：企业失去了额外的销售，消费者失去了获得无息融资的机会，他们经常寻找其他高息融资来源来为购买提供资金。我们项目的目标是研发一个技术平台，该平台将使用替代数据来源，最近才通过开放银行和机器学习提供，使企业能够确定他们是否可以向现有供应商拒绝的消费者提供BNPL。我们的产品通过切断贷款人，向企业提供更低成本的服务来扰乱BNPL。在我们的模型中，商家提供免息信贷，并承担贷款风险，以受益于额外的客户，使用我们的技术以风险管理的方式做出决策。从我们的可行性研究来看，我们估计，使用开放的银行交易数据与机器学习相结合，将使企业能够通过BNPL为高达~20%的消费者提供更多利润。这将促进经济中额外20亿GB的支出。

Buy now, pay later (BNPL) means a consumer can buy something today and pay for it in the future. This is great for both consumers and businesses: consumers can spread the cost of high-value items into easy-to-manage instalments and, because of this, businesses that offer BNPL see up to a 30% increase in sales.Businesses work with a BNPL finance provider who fund the purchase and collect the repayments from the consumer in the future. Many businesses offer BNPL interest-free to consumers -- the business pays the interest and this cost is offset by the increased sales they experience. This is a win-win for businesses and consumers: businesses generate more sales and consumers have access to interest-free credit.To determine which consumers they should provide BNPL terms to, finance companies perform 'credit checks' on potential consumers. A credit check is used to check a consumer's credit history and lets lenders know a consumer has used credit responsibly in the past, giving them an idea of how likely the consumer is to make future repayments.However, there are 10-14m consumers in the UK (20-25% of the adult population) that find it difficult to access credit from mainstream BNPL providers (PWC). This is because these consumers might, for example: not have previously applied for credit or have a mildly adverse credit history.These consumers are often rejected for interest-free BNPL even though they might be creditworthy and able to make the repayments. This is bad for businesses and consumers: businesses lose out on extra sales and consumers lose access to interest-free finance, often seeking alternative sources of high-interest finance to fund their purchases.The aim of our project is to R&D a technology platform that will use alternative sources of data, only recently made available through open banking, and machine learning to enable businesses to determine if they can provide BNPL to consumers rejected by existing providers.Our product disrupts BNPL by cutting-out the lender, providing a lower cost service to businesses. In our model, the merchant provides interest-free credit and takes the lending risk to benefit from the extra custom, using our technology to make decisions in a risk-managed way.From our feasibility study, we estimate that using open banking transaction data coupled with machine learning will allow businesses to serve up to ~20% more consumers profitably with BNPL. This would facilitate an additional £2bn of spending in the economy.