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Anomaly, rare event and fraud detection with synthetic data

Increase AI model accuracy using better than real synthetic datasets!
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Challenges
  • Rare events are underrepresented in datasets. As a result, AI models struggle to pick up on fraud and other anomalies, often underperforming in production.
  • Datasets are inherently biased. The overrepresentation of specific patterns could lead to faulty model decisions.
  • Off-the-shelf AI models are trained on data not specific to the bank or insurance provider. Consequently, they fail to reflect their specific realities.
  • Selecting, on-boarding and customizing ML models is a long and costly process.
  • Risk nuances, such as high-risk cross-border transactions and unusual typologies of customer behavior, are often missed.
  • Typically used data masking techniques endanger privacy and destroy patterns.
  • Fraud activities evolve quickly and fraud detection algorithms need to be retrained periodically.
  • Non-compliant data management risks compliance fines and data breaches.
Solution

Your machine learning models are only as good as the data they were trained on. Test, train, and calibrate your ML models with realistic, balanced synthetic data, that is representative of the anomalies and fraudulent activities specific to your institution. Synthetic data significantly improves your fraud detection, and AML models’ accuracy. According to Gartner, by 2024, 60% of the data used for the de­vel­op­ment of AI and an­a­lyt­ics projects will be syn­thet­i­c. Better signals - calibrated to fit the bank’s unique data profile - result in fewer false positives and more true positives. Synthesized datasets are flexible, easy to augment, and safe, so you can recalibrate your ML models as often as you like. What’s more, synthetic data empowers you to draw on alternative data sources locked by privacy regulations or policies.

Result

Synthetic data generated by MOSTLY AI’s synthetic data platform has shown to have a consistent improvement of fraud detecting machine learning models’ performance between 2% and 15% compared to using raw imbalanced data. An improvement of only 2% could yield a 2% decrease in false positives, saving millions of dollars on investigation processes alone.

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Why MOSTLY AI?

MOSTLY AI’s unparalleled data accuracy allows machine learning algorithms to pick up on new, rare patterns hidden in the raw, imbalanced data. Anonymization is automatic and data privacy is a built-in feature: the synthetic copies bear no 1:1 relationship to the original datapoints. This is a mathematical guarantee, that no individual of the original dataset can be reidentified in the synthetic version. As a result, the data no longer classifies as personal data so you can share your auto-anonymized synthetic datasets with third-party AI vendors and internal fraud departments in a privacy-compliant and agile way. MOSTLY AI’s synthetic data platform is easy to use and allows data scientists to quickly and efficiently augment datasets. Each synthetic generation run comes with an automatically compiled privacy and accuracy report to make sure that your machine learning algorithm is fed with the best possible version of your training data. MOSTLY AI has extensive industry knowledge in banking and insurance, providing unique synthetic data advisory in these fields. 

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