Moody's
Product Design for QUIQspread, an AI-enabled automated financial spreading tool
Role
Design Lead & Team Lead
Team
Product Manager, Tech Lead, 3 Developers, Account Manager
Moody’s Team
Product Manager, Product Owner, 5 Developers, C-Suite Project Sponsor
Project Duration
10+ weeks; multiple engagements
Moody's QUIQspread is an automated financial spreading application that uses advanced machine learning technology to automate the process of extracting financial data from complex financial statements for credit and lending assessments.
I led product design for QUIQspread and was a key contributor to product definition. I designed an award-winning, intuitive interface that surfaces the financial spreading process by displaying automated data extraction, enabling easy validation, and presenting an audit trail to ensure transparency and quality control.
Ultimately, I took over as team lead, advising and mentoring the cross-functional delivery team. I also trained the client PM on product ownership and healthy agile process.
Impact
QUIQspread was released in 2019 and quickly garnered multiple awards within both the fintech and AI communities, becoming one of Moody’s flagship products. Since launching, QUIQspread has served over 100 lenders globally, ranging from the largest international commercial banks to regional banks, captive finance companies, insurance providers, asset managers, government agencies and financial technology companies.
The intuitive interface I designed, and the advanced machine learning technology underneath, accelerate the spreading process by generating completed spreads 70% faster than manual spreading, with an average accuracy of over 95%.
The result is accelerated time to decision on even the most complex financial statements — without a sacrifice in accuracy.
PROJECT OVERVIEW
Product Definition
Product Roadmapping
Systems + Flows
UI + Visual Design
Agile Design
User Testing + Validation
Design Dev Collaboration
Best Practices + Training
At the time of the project, machine learning (ML) was still in an early stage of development, but increasingly becoming crucial for financial services organizations to remain competitive.
Moody’s invested in machine learning and the QUIQspread product with the goal of creating a more efficient risk assessment tool while improving accuracy of large scale financial data.
The goal of QUIQspread was to automate tasks previously done manually around data extraction and in the process bring efficiency, accuracy, and auditability to the process.
We were asked to:
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Bring best practices for digital product design and development to the Moody’s development team
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Take a successful POC and prepare for production deployment
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Leverage great UI/UX to improve efficiency of tool and accuracy
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Scale the product for commercialization (CreditLens)
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Train the Moody’s team to take over
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Deliver full stack development with a focus on front-end
The Challenge
Our team partnered closely with the Moody’s Emerging Business team to create a production release of a POC machine learning product.
Integrating the design and deployment process allowed for customer testing against the initial NLP model so that continual adjustments could refine the model for better accuracy against real world data.
Our Approach
This approach enabled us to:
Develop a robust MVP
We prioritized core functionalities that were crucial for the MVP, such as a content management system, responsive design, and a secure payment gateway.
Embrace collaborative feedback
Our team maintained a dynamic dialogue with all stakeholders, ensuring that our development efforts were aligned with MasterClass's strategic goals.
Ensure scalability
Anticipating future growth, we made scalability a cornerstone of ur development process, laying a foundation that would support MasterClass's expansion.
The first version, launched under the stealth brand Accomplice, included core features—video, CMS, payments, forums—alongside interim branding and a mobile-first responsive design. We also integrated a lightweight A/B testing framework with Optimizely to experiment with pricing and engagement.
The second release refined the product, informed by early feedback and analytics, while providing the polish needed to secure marquee instructors. This rapid, iterative approach gave MasterClass a strong launchpad, enabling the internal team to scale the product into the globally recognized platform it is today.
Final Thoughts
The rise of MasterClass demonstrates how the MVP strategy we helped shape set the stage for future success.
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Explore 'The Comprehensive Approach' - a project that demonstrates my expertise in handling ambiguous early stages, discovery, collaboration, systems-level thinking, hands-on design, and final deliverables.