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Data is the foundation on which any robust AML platform resides. This chapter starts with focusing on important aspects of data quality like data integrity, fill rates, presence of outliers, and missing value treatment. Chapter provides explanation of these along with relevant examples of those from AML context. It also discusses ways to deal with data issues. The chapter then focuses on various dimensions of data that is leveraged to develop a robust AI-enabled AML solution. It captures dimensions from raw data fields and mechanism of converting the raw data into a mart that can be leveraged for analyzing 360-degree customer views. Data challenges related to cross-border data sharing, creation of synthetic data, and pooling for handling less volume of data for generating meaningful insights are discussed. It provides hands-on approach to assess the underlying distributions and then generate synthetic data. In the context of data privacy, the chapter also deals with the GDPR-related restrictions and challenges that EU is facing in that context. Lastly, the chapter provides some of the areas of improvements that any head should focus on and shall they intend to develop a robust AI or digital AML organization.

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Gupta, A., Dwivedi, D.N., Shah, J. (2023). Data Organization for an FCC Unit. In: Artificial Intelligence Applications in Banking and Financial Services. Future of Business and Finance. Springer, Singapore.

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