Zidisha 正在利用 DataRobot 改变生活
公司规模
1-10
地区
- Africa
- Asia
国家
- Ghana
- Kenya
产品
- DataRobot Enterprise AI platform
技术栈
- Machine Learning
- SQL
- Spreadsheets
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Productivity Improvements
技术
- 分析与建模 - 预测分析
适用行业
- 教育
- 金融与保险
适用功能
- 采购
用例
- 欺诈识别
- 质量预测分析
服务
- 数据科学服务
关于客户
Zidisha 是一个非营利性的在线小额贷款社区，将借款人与放款人直接联系起来。它提倡通过众筹的方式为世界上一些最贫穷国家的人们提供小额贷款，帮助他们创业和发展企业或资助教育。Zidisha 不收取贷款利息；相反，借款人需要支付 5% 的服务费来支付转移和管理贷款的费用。通过 Zidisha，企业家可以获得条件灵活、成本合理的商业贷款，使他们能够保留大部分利润并将其重新投资到企业中或用于养家糊口。Zidisha 绕过了传统的确定、量化和定价违约风险的方法，不再需要贷款人员或银行专家。Zidisha 无需贷款人员进行尽职调查，而是建立借款人和放款人之间的直接关系。
挑战
Zidisha 是一个非营利性在线小额贷款社区，旨在通过提供小额贷款来创业、上学或改善生活条件，从而改变一些最贫穷国家人民的生活。然而，每笔贷款都存在违约风险。传统贷款机构已经找到了识别、量化和定价违约风险的方法，风险越高的贷款利率就越高。风险评估工作通常由贷款人员负责，成本则转嫁给借款人。在发达经济体中，数千或数十万美元的贷款很常见，这些成本可以轻松吸收，而不会影响贷款的合理性，但在发展中国家情况并非如此。雇用贷款人员来评估小额贷款的违约风险会导致利率高达 40%，从而破坏经济发展的促进。Zidisha 面临的挑战是通过识别最有可能成为高风险借款人的申请人来提高还款水平。
解决方案
Zidisha 与 DataRobot 合作开发和部署机器学习模型，从根本上改善贷款申请和筛选流程。DataRobot 的一位面向客户的数据科学家 (CFDS) 建议 Zidisha 受益于两个预测模型：一个用于检测欺诈性申请，另一个用于识别贷款违约倾向高的申请人。保护贷款人的资金是 Zidisha 长期成功的基础，因为它可以让更多贷款提供给真正需要的值得信赖的借款人，并提高资金回收到其他值得借款人的速度。Julia 和她的同事与 DataRobot 团队合作，将该平台与他们的系统集成，并简单地阅读 DataRobot 用户文档，开始为 Zidisha 开发自己的预测模型。Julia 和她的同事在不到两周的时间内创建的两个模型通过将贷款违约率降低了 5%，大大提高了 Zidisha 偿还贷款人的贷款百分比。
运营影响
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