Case Studies Smart Test Data for Online Banking Products

Edit This Case Study Record

	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	Digital Twin Predictive Quality Analytics
Services	Data Science Services System Integration
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! Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

Digital Twin

Predictive Quality Analytics

Services

Data Science Services

System Integration

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 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.

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.

Impact #1	Smart, data-driven product features, such as account balance prediction and responsive UX decision-making.
Impact #2	Shortened development sprints by several days as a result of near-instant availability of synthetic customers transaction data.
Impact #3	Demand a highly realistic product to internal stakeholders, earning valuable support throughout the organization.

Impact #1

Smart, data-driven product features, such as account balance prediction and responsive UX decision-making.

Impact #2

Shortened development sprints by several days as a result of near-instant availability of synthetic customers transaction data.

Impact #3

Demand a highly realistic product to internal stakeholders, earning valuable support throughout the organization.

Benefit #1	Generating smart test data took significantly less time than anonymizing the initially used and discarded dummy data.

Benefit #1

Generating smart test data took significantly less time than anonymizing the initially used and discarded dummy data.

Download PDF Version

Overview

Smart Test Data for Online Banking Products

Operational Impact

Quantitative Benefit