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Case Studies > Smart Test Data for Online Banking Products

Smart Test Data for Online Banking Products

Technology Category
  • Analytics & Modeling - Data-as-a-Service
  • Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
  • Finance & Insurance
Applicable Functions
  • Product Research & Development
  • Quality Assurance
Use Cases
  • Predictive Quality Analytics
  • Digital Twin
Services
  • Data Science Services
  • System Integration
The Challenge
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!
About The Customer
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.
The Solution
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.
Operational Impact
  • Smart, data-driven product features, such as account balance prediction and responsive UX decision-making.
  • Shortened development sprints by several days as a result of near-instant availability of synthetic customers transaction data.
  • Demand a highly realistic product to internal stakeholders, earning valuable support throughout the organization.
Quantitative Benefit
  • Generating smart test data took significantly less time than anonymizing the initially used and discarded dummy data.

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