Financial services organizations see a lot of promise in artificial intelligence (AI) and machine learning (ML) applications. From chatbots to fraud prevention, to executing faster transactions, the innovation opportunities truly are limitless. The challenge is building them. Every innovation or R&D lab needs reliable refined data that is statistically relevant and usable under privacy and regulatory standards.



Raw data is unsafe—too many personally identifiable information (PII) traits—and unreliable—too exposed to the possibility of duplicates, incomplete fields, and non-standard integration. Even cleaned-raw data is too risky from a privacy standpoint. Only anonymized or synthetic data should be used for AI/ML development or the next big thing that will be the industry game-changer such as:

Enhance payment fraud detection
Predictive lending
Predicting high-transaction customers
Identifying customers likely to attrite

The hype and buzz around AI is far greater than the actual, realized business benefits. Why is that so?  It’s because the data needed for AI to be successful is locked away in different silos or contains sensitive information that shouldn’t be used for innovation.  Through synthetic data, you can now unlock the full power and value behind all of your data to drive your AI/ML engine. Whether that’s building new products or analytics for your existing customers (consumer or business), leveraging data for machine learning or AI to build the “next big thing” (i.e., creating innovation labs or fintech incubators that test new ideas and develop proof-of concepts) or delivering new personalized banking experiences.

Raw Data Universe


10% of transaction data available for AI/ML

  • Only able to access minimal percentage of transactions for ML / AI Innovation
  • Other percentage of data includes PII and too risky

Facteus' Mimic™ Synthetic Data Solution


Synthetic Data Universe


100% of transaction data available for AI/ML

  • Access to all transaction data for ML / AI Innovation
  • Data is safe and compliant with data privacy regulations
  • Maintains statistical relevance for ML algorithms

Let’s discuss how to get your data ready for AI/ML.
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