Synthetic Data: Key Use Cases
Discover the Potential of Synthetic Data in Supporting the Deployment of Specialised AI Models Across Various Industries and Business Departments
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Overcoming Data Challenges with Synthetic Solutions
Companies are increasingly leveraging data for decision-making and AI model customisation, but acquiring comprehensive and representative datasets can be challenging and costly. Synthetic data provides a scalable solution, addressing privacy, labelling, and cost issues by flexibly generating data that mirrors the real world. It enables the creation of tailored datasets, enhancing model training, experimentation, and innovation. Generated through generative AI models, synthetic data spans various types, including tabular data, texts, images, videos, spatial data, time series, graphs, and 3D simulations.
Key Use Cases by Industry
Reply supports the adoption of synthetic data across diverse industrial sectors, enhancing both data management and operational efficiency. For example, by combining 3D simulation with AI, Reply effectively replicates real-world scenarios to train AI systems in autonomous vehicle development, using a "sim-to-real" approach. The advantages of synthetic data are evident in simulations, where traditional data collection methods are inadequate or challenging in the real world. Thanks to promising results, interest in synthetic data is expanding across the financial, pharmaceutical, healthcare, and manufacturing industries, as its potential to drive innovation and improve operations becomes increasingly recognised.
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Financial Institutions
Synthetic data helps in modelling extreme events and enhancing fraud detection and prevention. In the insurance sector, it aids in managing claims and identifying fraudulent requests. For credit assessment, synthetic data enhances risk evaluation and credit scoring, enabling bias detection, improving the reliability of financial models, and ensuring compliance with international regulations.
Healthcare & Pharmaceuticals
Synthetic data simulates patient responses, accelerating drug development and aiding the study of rare diseases by modelling progression and personalising treatments. In medical imaging, it generates images of underrepresented conditions, enhancing AI precision and scalability. Synthetic electronic health records maintain privacy while accurately reflecting clinical information.
Manufacturing
Synthetic data enhances predictive maintenance by simulating rare events, improving the accuracy of AI, and reducing costly downtimes as well as the complexities of real-world data collection. It also supports the adoption of smart factory models, robotics, and industrial automation, thereby improving operational efficiency and safety while accelerating AI integration into production processes.
Using Synthetic Data in Different Business Departments
The benefits of synthetic data extend across various business functions and industrial sectors. It plays a transformative role in data management by addressing complexities in collection, labelling, and compliance, thereby accelerating data generation, reducing costs, and improving scalability while maintaining quality and security. Synthetic data also helps tackle AI biases, particularly in HR, by balancing datasets to ensure fairer predictions through the creation of synthetic CVs to address demographic imbalances in historical data, thereby improving recruitment processes. The most promising departments include cybersecurity, marketing and sales, and supply chain and logistics.
Reply’s Pragmatic Approach to Synthetic Data
Reply is actively engaged in the development of synthetic data, fostering extensive internal research and development activities, supported by dedicated business lines. Reply experts develop advanced solutions to tackle critical challenges across multiple industries and design effective implementations, assisting clients in uncovering the business value and opportunities offered by synthetic data.
