A multinational banking and financial services company, with 3000+ offices in 60+ countries and around ~40 million customers.


Key Challenges

Validating huge amounts of masked data


Lack of end-to-end (E2E) dynamic and heterogeneous systems


Complexity of staging, ETL (extract, transform, and load), and Hadoop transformations

The Impact


effort saved by using text-to-text comparator (RDBMS to Hadoop comparator)


The Solution

End-to-End testing solution for Anti-Money Laundering (AML)

Our testing solution helped to reduce the overall testing effort by 35%.

Looking for a breakthrough solution?


Our testing solution is comprised of an open source automation stack, which handles data conversions, data comparisons and connecting to different layers of the Hadoop ecosystem.

40 million history records and 140,000 daily transactions are handled by our newly developed solution.

Innovating in the Fight Against Money Laundering

Infosys introduced many innovation in this project like Hive Output Formatter (HOF) and Transformation Traceability Matrix (TTM)

Our End-to-End testing solution for Anti-Money Laundering (AML) has the following key primary components:

  • Automated utility: Java-based, masked data validator compares masked and unmasked data with the data dictionary
  • Comprehensive TTM: This helps in creating test scenarios for specific functional changes as well as overall impacted areas
  • Query output formatter: A Hive Output Formatter to format and analyze Hive query outputs
  • Text-to-text comparator: Java utility created to compare two pipe delimited text files, field by field
  • End-to-end regression automation framework (in HDFS / Hive and ETL): Ensures one-time synchronization of all UNIX boxes
Optimal data testing coverage for a data lake implementation


Enhancing quality assurance and testing procedures

This paper outlines seven key areas that the QA and testing functions must focus on to enhance their organizational maturity and apply innovation in their day to day work.

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