Smart Test Data for Online Banking Products
公司规模
Large Corporate
地区
- Europe
产品
- MOSTLY AI synthetic data platform
- Smart test data dashboard
技术栈
- REST API
- Synthetic data generator
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Customer Satisfaction
- Digital Expertise
技术
- 分析与建模 - 数据即服务
- 应用基础设施与中间件 - 数据交换与集成
适用行业
- 金融与保险
适用功能
- 产品研发
- 质量保证
用例
- 质量预测分析
- 数字孪生
服务
- 数据科学服务
- 系统集成
关于客户
The customer is one of the largest retail banks in Europe, known for its extensive range of financial services and products. The bank has a significant customer base and is committed to providing innovative solutions to enhance customer experience. With a focus on digital transformation, the bank developed a mobile banking app intended to serve as a comprehensive alternative to traditional in-person banking. The bank's IT department is tasked with ensuring the app's functionality and security, particularly in handling sensitive transaction data. The bank's commitment to privacy and data protection necessitates the use of advanced technologies to create realistic test data without compromising customer information.
挑战
One of the largest retail banks in Europe developed a mobile banking app that aims to be a true alternative to in-person banking. To provide a high-quality user experience, extensive testing of the app with clients’ transaction data was crucial. However, the bank’s IT department had to heavily mask transaction data due to privacy policies, and the resulting test data failed to provide realism in transaction amounts, dates, and so on. Dummy data could never match the smart synthetic dataset’s granularity and realism, failing to provide the complexity necessary for testing a product so important to work flawlessly. Imagine you could create realistic customers at the push of a button!
解决方案
MOSTLY AI synthetic data platform, our synthetic data generator was delivered to the bank through a REST API. The algorithm - fed with raw client data - learned its patterns and properties. Once the algorithm was trained, any number of new, realistic synthetic users could be generated. Using a smart test data dashboard, the product development and testing team could generate synthetic customers based on pre-defined parameters, such as the number of accounts, income range, urban or rural address, and others. New, predefined synthetic customers could be created using behavioral data generation to test edge cases.
运营影响
数量效益
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