Welcome to the first “Book of the Month” for 2025. This time, we’ll be going over “Data Models for Banking, Finance, and Insurance” by Claire L. Frankel.
This book arms the reader with a set of best practices and data models to help implement solutions in the banking, finance, and insurance industries. Right at the beginning of the book, Frankel highlights that, while this book is a significant shortcut to the work that goes into data modeling and reporting, ultimately one must work with the business to ensure that local considerations are reflected in the work. The book goes through various models that are critical to the industries it focuses on and provides detailed insight into how the models ultimately function. Additionally, as a bonus, the book educates readers on financial fundamentals. This makes the book an ideal companion for a data team just starting with implementing solutions in the financial vertical.
Frankel spends time early in the book covering a data model for credit card information. In covering this topic, as she does for every topic, Frankel spends time providing key definitions for the concepts covered in the data model. Credit card information faces some significant regulatory requirements for its capture and use. Frankel helps significantly demystify and ultimately, derisk the management of customer credit card data. The book also gets into transactions of credit cards used by a customer of the bank and credit cards as a product being offered by a bank. This highlights the robustness of the book. Furthermore, it details various types of credit cards, such as corporate cards, family cards, and more.
In the book, a reader can also find detailed modeling for insurance products – also a heavily regulated area and something many data teams can struggle with. Because of the structure of the book, Frankel ultimately teaches a significant amount about the nature of insurance products, be they life, property, or casualty insurance. Frankel leads the reader through why the modeling is unique for various different types of insurance products. This provides a solid foundation for a topic area that can be a significant challenge to newcomers.
While Frankel covers many other aspects in the finance vertical, the feature that stands out is the overall structure of the book. Frankel goes through great detail on every conceptual and logical model presented in the book and really teaches the reader about the application of each. It’s here where Frankel injects a bit of levity into the book, at one point suggesting something along the lines of, “Don’t invite your DBAs to review this; they may die of boredom.” The honesty of that statement is refreshing, but it also highlights how the book reinforces best practices. While the book is a significant benefit to reducing the amount of work going into implementing solutions, it continually reminds the reader of the collaborative nature of implementing the solutions presented within.
In conclusion, “Data Models for Banking, Finance, and Insurance” by Claire L. Frankel is an invaluable resource for professionals in the financial data management sector. The book provides a comprehensive set of best practices and detailed data models that are crucial for implementing effective solutions in banking, finance, and insurance. Frankel’s clear explanations and practical insights make complex topics accessible, while her attention to regulatory requirements and real-world applications ensures that readers can confidently apply the knowledge gained. If you find yourself working in the financial sector as part of a data team, this book is an excellent reference and tool you can use to understand the breadth of what awaits you. It’s a must-have for anyone working in financial data management.
More About the Author
Claire L. Frankel earned a Bachelor’s Degree in Physics and Mathematics from the State University of New York at Albany and attended graduate school in Computer Engineering at Boston University.
She has worked as a data modeler, senior data modeler, and executive director of data modeling for 40 years at leading banking and Wall Street financial firms. She has advised start-up companies on database technology and advised several vendors on data models necessary for Wall Street operations.
She is the author of white papers on SEC regulations, notably SEC 144a and b, global requirements for international firms based on the GDPR, and contributed the chapter on ANSI standards to “The Handbook for Wall Street Operations.”
She lives in New York and writes poetry for fun.