Preparing Data for Artificial Intelligence and Machine Learning
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.
How Do You Prepare Data for AI/ML?
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
Predicting high-transaction customers
Identifying customers likely to attrite
Unlock All Your Data
The hype and buzz around AI is far greater than the actual, realized business benefits. Why is that so?
Let’s discuss how to get your data ready for AI/ML.
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