Data takes a crucial role in the finance industry, driving informed decision-making, risk management, customer insights, and regulatory compliance, while enabling innovation and efficiency through data-driven strategies and solutions. Synthetic data usage offers financial organizations a privacy-preserving solution to enhance risk assessment, fraud detection, algorithm training and software development. By creating realistic yet synthetic datasets, financial institutions can optimize decision-making, improve regulatory compliance, and develop innovative strategies without compromising sensitive customer information.
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Finance organizations and the use of synthetic data
Banks
Improve fraud, anti-money-laundring and anomaly detection models
Accelerate open banking and enterprise data-sharing with stakeholders
Implement data-driven innovation
Assure regulatory compliance with stringent data protection regulation
Insurance
Personalized customer insights based on high-quality synthetic data
Test data for digital banking products
Secure collaboration and data sharing
Ease secondary uses of insurance data
FinTech
Accelerated product development with the use of synthetic data
Reducing the time-to-market
Regulatory compliance with data protection regulations
Secure algorithm training by maximizing the data and minimizing the privacy
of financial institutions afraid of losing competition without leveraging Big Data
1%
investment in Big Data and business analytics in the financial sector by 2023
$1b
Improvement of utilization of resources is estimated due to a data ecosystem
Why do finance organizations consider synthetic data?
Stay ahead of the competition. Solutions that allow financial organizations to utilize data smarter will enhance the competitive position.
Reduce time-to-data. Synthetic data accelerates access to data by minimizing risk assessments, internal processes and bureaucracy related to data access requests.
Ambition to innovate with data. The ambition to innovate with data is significant in the financial sector. Synthetic data will accelerate the realization of this ambition.
Ensures compliance with data privacy regulations by minimizing the use of real personal data, without hindering developers due to synthetic data.
Why Syntho?
Syntho has extensive experience working with financial organizations
Experience working with financial organizations
Extensive project involvement with international banks, insurance companies, and fintech organizations
Time series data
The platform supports time series data (typically relevant for transaction data, market data, investment data, event data etc.)
Upsampling
Syntho supports upsampling, that allows users to generate more data in case of limited data, typically used in the field of fraud detection and anti-money-laundering
Winner of Global SAS Hackathon in the Category Health Care & Life Sciences
We are proud to announced that Syntho won in the healthcare and life sciences category after months of hard working on unlocking privacy-sensitive healthcare data with synthetic data as part of cancer research for a leading hospital.
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