Explore Synthetic Data’s Revolutionary Role in Fuelling AI driven innovation in finance. Learn how synthetic data makes up for data scarcity, overcomes privacy hurdles, and speed constraints, removing the hurdles for AI innovation in finance.
Artificial Intelligence (AI) is fast emerging as a transformative force in the financial services industry, throwing open the promises of unparalleled advancements and innovations. Yet, despite its immense potential, this sector faces a formidable challenge in adopting AI. One that is serious enough to limit the value potential compared to other industries. It involves data.
Enter synthetic data, a game-changing solution to this perennial predicament – imagine being able to create 100% regulatory compliant and statistically representative data. Synthetic data encapsulates a brilliant concept – it offers a pragmatic approach to addressing the problem. Essentially, synthetic data is machine generated data that resembles real data in statistical representation. This data generation is possible at a volume, velocity and veracity and can be meticulously tailored to the required specifications, such as, applying privacy constraints. It’s a paradigm shift, offering a bespoke and scalable solution to the perennial data sourcing challenge in AI development.
The seriousness with which data scientists have sought a solution to this problem is evident from Gartner’s visionary prediction that 60% of data for AI training will be synthetic, up from 1% in 20211.
So, what makes the case for such synthetic data?
Volume of Data:
The scarcity of certain types of historical data or market information creates a bottleneck. This scarcity hampers the ability to provide large volumes of data, hindering the development and training of robust AI models.
Data Privacy:
Anonymization, which involves transforming data to prevent the re-identification of data subjects, is a powerful tool for data processing while being compliant with data protection regulations such as GDPR. However, ensuring that data anonymization is correctly applied at a large scale is a daunting task, especially when trying to minimize risks and ensure proper anonymization. Traditional methods of anonymizing data struggle to keep up with the demands of large-scale data requirements, raising significant concerns about data privacy and compliance.
Data at Speed:
Real-time data acquisition and utilization pose a considerable challenge, preventing financial firms from capitalizing on immediate insights for innovation and decision-making. Data quality issues can arise due to various factors, such as inconsistencies in data sources or errors in data collection.
Diversity of Data:
Certain critical use cases, such as fraud detection, suffer from highly imbalanced datasets, making it difficult to train AI models effectively. Collecting data connected to fraud detection is similar to collecting real-world driving data by autonomous car manufacturers to train the AI engine. Collecting real-time data for every conceivable scenario an autonomous vehicle might encounter on the road is simply not possible. Given the unpredictability of the real world, it would take hundreds of years of real-world driving to collect all the data required to build a truly safe autonomous vehicle2. So is the case for fraud detection if one were to wait for such data to get generated naturally.
These challenges create roadblocks that limit the industry’s ability to harness the full potential of AI.
Synthetic Data Generation is akin to a talented artist sketching an alternative reality—it creates a safe and scalable space, like a secret lab, to nurture AI without compromising due to data-based limitations. Various techniques exist to create synthetic data, each having its nuances and considerations:
Statistical Distribution Methods: These methods heavily rely on the expertise of data scientists. They are adept at mimicking the statistical properties of original datasets.
Agent-Based Modelling: While effective, machine learning methods face the challenge of potential overfitting. This makes it necessary to give due consideration to how the data is put to use.
Deep Learning/Generative Models: Generative Adversarial Networks (GANs), Variable Autoencoders (VAEs), and Language Models emerge as the most reliable options, leveraging complex neural networks to create synthetic data. Data generated via these models mirror the original while preserving privacy and scalability.
CXOs looking to embark on this transformative journey need a deliberate strategy to start with Synthetic Data Generation:
Assessment and Evaluation: Carefully evaluate the available options (commercial products and python-based libraries) and choose a methodology that aligns with the organization’s specific needs and goals.
Expertise and Implementation: Consider leveraging the deep expertise of data scientists to navigate the complexities of Synthetic Data Generation methods effectively. Several best practices that impact the accuracy of synthetic data need to be considered, such as, during preparing clean data, managing anomalies, and measuring the utility of synthetic data to real data through different techniques.
Continuous Innovation: Embrace a culture of ongoing innovation to adapt different techniques and evolve the synthetic data generation process continually. One such approach is using simulation scenarios as a digital twin to create a continuous improvement cycle.
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As the industry hurtles toward a future dominated by AI, embracing Synthetic Data Generation stands as a strategic imperative for CXOs seeking to harness AI’s transformative power, ensuring their organizations remain at the forefront of financial innovation.
Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies
- https://www.gartner.com/en/newsroom/press-releases/2023-08-01-gartner-identifies-top-trends-shaping-future-of-data-science-and-machine-learning
- https://www.forbes.com/sites/robtoews/2022/06/12/synthetic-data-is-about-to-transform-artificial-intelligence/
- https://www.cio.com/article/305902/what-is-synthetic-data-generated-data-to-help-your-ai-strategy.html
