Trust in Numbers

William Chiu (MSiA '13) works at the intersection of cutting-edge technology and human understanding in finance, where algorithms often make decisions that impact millions of lives and even more dollars.

He is now bringing that experience to Northwestern Engineering's Master of Science in Machine Learning and Data Science (MLDS) program (formerly the MSiA program), where he is teaching a new elective called Interpretable Machine Learning for Finance.

His mission is to go beyond crunching numbers and to delve into the critical realm of explainable AI.

“The goal of this course is to teach students how to develop interpretable machine learning models tailored for the financial services industry,” Chiu said. “Students learn to explain predictions, build trust with stakeholders, and ensure compliance with regulations.”

In an era where AI-driven decisions can make or break financial futures, Chiu’s course stands out for its emphasis on transparency. The word “interpretable” in the course title is intentional.

“The term underscores the importance of creating models that are transparent and understandable by both technical and non-technical audiences,” Chiu said. “In financial services, where decisions have significant consequences, interpretability ensures accountability, regulatory compliance, and stakeholder confidence.”

Chiu's passion for the subject is clear. To the classroom he brings experience working for some of the nation’s leading financial institutions, including Wells Fargo, Bank of America, TransUnion, and First Republic Bank.

Today, he serves as vice president and head of predictive modeling at National Mortgage Insurance Corporation.

“What excites me most about my role is the ability to apply my expertise in predictive modeling to solve impactful business problems like credit risk estimation,” Chiu said. “It’s fulfilling to see how our work directly contributes to the stability of the housing market.”

This industry experience infuses his teaching with practical insights. Students in Chiu’s class don’t just learn theory; they grapple with the same challenges faced by professionals in the field.

Students develop a deeper understanding of technical and practical aspects of machine learning, as well as advanced interpretability techniques that will help differentiate them in the job marketplace.

For Chiu, teaching the course is a way to give back to the program that shaped his own career. As an MSiA graduate, he knows firsthand the value of blending technical expertise with business acumen.

“My time in MSiA helped me build a solid foundation in data science methodologies while emphasizing their practical applications in business contexts,” Chiu said. “The program also taught me how to effectively communicate complex analytical results to diverse audiences, a skill I use daily in my career.”

As the financial world continues to evolve, Chiu's course prepares students to be at the forefront of responsible AI implementation.

It is not just about building models, he said. It’s more about building trust.

“I emphasize the importance of balancing model accuracy with interpretability in industries like finance where trust, transparency, and compliance are critical,” Chiu said. “This balance is key for building models that drive impactful decisions while meeting regulatory standards."