Moov Financial Innovation, Technology & Agility

How does your team use AI in the engineering design process, and what benefits have you observed?

Our team leverages AI to enhance various stages of the engineering design process. For example, we use AI-driven tools embedded into our integrated development environments to automate repetitive tasks, such as generating boilerplate code or optimizing configurations, freeing up engineers to focus on higher-value activities.

Additionally, during the design phase, AI serves as a powerful resource for education and research. Team members utilize AI to dive into industry-specific or technical information, gaining a deeper understanding of the challenges they’re solving. Whether it’s used for exploring new programming frameworks, understanding complex regulatory requirements or reviewing detailed documentation for similar systems, AI accelerates knowledge acquisition and ensures the team is well-prepared before implementation begins.

Moov uses AI to automate and enhance learning, which improves efficiency and promotes creativity. This empowers the team to tackle design challenges with clarity and confidence.

What challenges have you encountered when implementing AI technologies in engineering design, and how have you addressed them?

In the payments space, one of the biggest challenges is the limited availability of publicly accessible data sets. This makes it difficult to train AI models on relevant data or validate their outputs effectively. To address this, we encourage best practices for validating AI outputs such as cross-referencing multiple sources and relying on team expertise to scrutinize AI-generated insights. While this requires extra effort, it helps ensure that AI contributes reliable value to our engineering design process.

Another challenge is maintaining a balance between AI automation and human oversight. While AI excels at speeding up tasks like code generation or research, it occasionally makes suggestions that may not align with our design principles or goals. We mitigate this by embedding AI in a collaborative workflow where it acts as a co-pilot rather than an autonomous decision-maker.

Adopting AI tools has required cultural adjustments. Some team members were initially skeptical of relying on AI or adapting to new workflows. We’ve addressed this by promoting transparency around AI tools, offering training and encouraging experimentation, which has turned AI into a trusted ally.

What excites you most about your team’s future when it comes to leveraging AI in innovative ways?

What excites me most is the transformative potential of AI to redefine how we approach engineering problems. With the rapid evolution of AI capabilities, we see opportunities to build systems that adapt to change and anticipate it. Imagine an AI-powered platform that dynamically optimizes payment routing or predicts and mitigates fraud in real time — these are possibilities we’re actively contemplating.

The ability to scale innovation quickly is a thrilling prospect. AI can turn what used to take months into something achievable within days, enabling us to test and deploy new features faster than ever. Moreover, as we integrate generative AI into our development processes, we anticipate uncovering entirely new use cases that push the boundaries of what’s possible in fintech.

Ultimately, the relationship between human creativity and AI’s computational power drives our excitement, keeping us at the forefront of innovation in the payments space.