NC State Design Alumna Honored for Role in Award-Winning IBM AI Innovation

NC State College of Design alumna Dipali Aphale (BID ‘17) has been recognized on the global stage as part of an IBM team that won Best AI Solution for Data Insights & Knowledge Management at the Banking Tech Awards USA 2025. 

The award, announced May 29 in New York City and highlighted by IBM in June, recognized IBM Synthetic Data Sets: a groundbreaking tool that allows banks and insurers to train artificial intelligence models without relying on sensitive customer information. 

The recognition placed IBM alongside some of the biggest names in global finance, but for Aphale, it also marked a personal milestone. “Most of the other winners that night were some of the biggest financial services companies in the world,” she said. “So the fact that our small but mighty team of five was recognized among giants was a really special experience.”

Building Responsible Innovation

Released in early 2025, IBM Synthetic Data Sets uses agent-based modeling from statistical distributions to replicate the complexity of real financial transactions without using any personally identifiable information (PII). Each dataset has specific features that result in better and faster AI model training. For example, every transaction is pre-labeled as “fraud” or “not fraud” so that AI systems can be trained and validated more quickly, and data scientists spend less time with manual, error-prone labeling. 

The innovation has since earned global attention for its potential to accelerate trustworthy AI development across banking, insurance and other data-intensive industries. For Aphale, who works as a Design Lead in AI and Strategic Foresight at IBM, the project was a chance to blend rigorous design methods with cutting-edge technology.

“My work as a Design Lead is split into three key domains: research — how did we get here? Design — where do we go? And strategy — what do we work on first?” she explained. “Bringing each of these domains to the table helped shape the types of data we included in each dataset, how our product was able to shorten the AI model lifecycle and which customer segments we prioritized for adoption.” 

Her contributions helped define not just the product’s structure but its purpose: making financial innovation faster, safer and more equitable with high-quality data. 

Lessons from Design School

Aphale traces much of her professional mindset back to her time at the College of Design. 

“One of my favorite things about design school — something I wish we did more of in the ‘real world’ — was experiencing brutally honest critiques,” she said. “Being able to objectively break down your work from multiple perspectives that challenge your original intention, all while maintaining emotional composure, and then being actionable about iterating on the work is something that only design school can really prepare you for.”

“My classmates and I developed such a thick skin and were able to objectively challenge ideas , even when they were our own. These skills are imperative in developing a healthy environment to iterate on shared work with your teams.“

That iterative process, she added, taught her how to separate ego from output, which is a crucial skill in fast-moving, high-stakes innovation environments. “My classmates and I developed such a thick skin and were able to objectively challenge ideas, even when they were our own. These skills are imperative in developing a healthy environment to iterate on shared work with your teams.” 

Beyond critique, she points to her industrial design education as foundational for understanding human systems within technological contexts.

“So many skills from industrial design are applicable across technology design roles, but specifically for IBM Synthetic Data Sets, user research and market research were cornerstones in getting the product to market,” she said. 

The Human Side of Data

While the technical achievement behind Synthetic Data Sets is impressive, Aphale believes its true impact lies in what it represents: a step toward ethical, privacy-conscious data science. “In the age of surveillance capitalism that we live in, I hope this project speeds up the value that AI can bring to financial services without compromising user data for monetary gains,” she said. 

By replacing sensitive personal data with synthetic information that behaves the same way, the technology enables companies to build and test AI solutions without the ethical or legal risks of handling real user records. The result: faster innovation, greater transparency and stronger consumer trust.

“I’m also excited to see what data scientists can do much more effectively with AI models now that they can access data unencumbered by the months of pre-processing that usually takes place in enterprise markets,” Aphale added. 

As technology continues to evolve at lightning speed, Aphale encourages design students to keep their eyes not just on what’s new, but on what’s enduring. 

“Technology is without a doubt incredibly cool,” she said, “but pay more attention to patterns in society and how technology has changed or proliferated them. Those patterns will outlast any shiny new thing that gets built.”