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Datathons & hackathons Innovation powered by safe, synthetic data

Cost efficiency

Streamline data preparation for innovation events, enhancing cost-efficiency while adhering to privacy standards

Validation

Validate innovation concepts against high-utility synthetic datasets

Customer focus

Deliver customer-centric innovation with synthetic data insights

Challenges

High-energy events like datathons or hackathons are fertile ground for sowing the seeds of innovation. However, the impact of bringing together experts to solve challenges and make breakthroughs is often constrained because there is no access to authentic, representative datasets. Production data remains inaccessible due to security concerns. In contrast, hackathons that only use sample data find that it has often been anonymized to such an extent that its utility falls far below the requirements for true innovation and genuine insight.

Case study

A large financial services provider regularly organized hackathons with university research departments to help set a vision and build prototypes using cutting-edge, disruptive technologies and the latest academic research. The teams in these hackathons were from diverse backgrounds, including internal product developers, business leaders, university researchers, and students. Together, they developed solutions for “big ideas” in digital banking, sustainable finance, or other high-value topics over one to two days. Because of data constraints in sharing real-world production data with the participants, hackathon teams were forced to rely only on publically available sample data from sources such as Kaggle. These restrictions often led to hackathon solutions that had limited impact once exposed to authentic production data later in the innovation cycle.

Solution

The hackathon organizers turned to MOSTLY AI’s synthetic data generation platform to create synthetic data that mirrored the complex distributions and relationships of the source data while maintaining the privacy of the original financial services data subjects. Providing the synthetic data to participants through a dedicated sandbox environment — hosted on Google Cloud Platform and equipped with all the necessary developer tools to explore and prototype solutions — created a seamless innovation experience.

Proof

Synthetic datasets became a game-changer, offering unprecedented detail and utility for the hackathon teams as they developed their innovation solutions over the event. As teams explored the synthetic data resources, they uncovered behavioral patterns that would not have surfaced in publicly available data. One team used the data to design a financial assistant that helps customers make smarter spending decisions. Others created solutions tailored to retirement savings or financial planning for underserved customer niches. With high-fidelity synthetic data used across these solutions, the prototypes could explore real-world scenarios with representative data. As a result, these innovation events helped sharpen the company’s strategic focus around the personalization of financial services through digital transformation.

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