Organization
Leading International Dutch Bank
Location
The Netherlands
Industry
Finance
Size
60,000+ employees
Use case
Analytics
Target data
Financial transaction data
Our customer is a multinational banking and financial services corporation. Its primary focus areas are retail banking, commercial banking, investment banking, wholesale banking, private banking, asset management, and insurance services. It has more than 50 million clients in more than 30 countries. The bank is listed in the top 100 in the list of the World’s 1000 Largest Banks.
Navigating the data landscape within the banking sector causes challenges. The bank faced difficulties in accessing and utilizing data due to fragmented storage across disparate databases and compliance regulations. Furthermore, the anonymization of data, intended to protect privacy, often resulted in machine learning models underperforming due to loss of valuable contextual information.
The bank’s commitment to stringent data privacy measures further complicates seamless data sharing and collaboration. These obstacles interfere with leveraging data-driven insights for decision-making, innovation, efficient fraud detection strategies, and the realization of its ambition toward open banking.
Deploying Syntho’s AI-synthetic data generation platform within the bank offers a transformative approach to address complex data challenges. By generating privacy-compliant realistic datasets, synthetic data empowers accurate machine learning model training, elevating fraud detection and risk assessment capabilities. This approach not only speeds up development cycles and enhances model performance, but also allows secure data collaboration among institutions.
Synthetic data offers a powerful strategy to strategically upsampling minority groups within datasets, thereby fostering more balanced and representative input for machine learning models. This approach is used for example in the context of fraud detection and ant money laundry, where often the availability of data could be scarce and limited.
By using synthetic data, banks can adhere to strict data privacy regulations while still achieving accurate results and innovative advancements. By ensuring that sensitive customer information remains protected, this bank is now able to realize data-driven innovation in a privacy-preservative manner. Innovative models are used for example in the field of predicting defaults, marketing optimization, and KYC.
Synthetic data sharing emerges as a strategic advantage in the fight against financial crime within the banking sector, enabling collaborative efforts without compromising sensitive real-world data. Secure data sharing facilitation and analysis among financial institutions, regulatory bodies, and law enforcement. Also upsampling the often scarce amount of financial crime data (e.g. limited fraud data) allowed the bank to experiment with AI-based upsample technologies in comparison to traditional techniques such as interpolation and SMOTE.
Legacy anonymization destroys the data and requires domain knowledge. Synthetic data not only mimics the real data, but also keeps the original format, even for complex data structures like time-series data (in transactions) and location data.
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