A major implication of Big Data, is that analytics must rely less on persistent (historical) data and instead adjust dynamically in the stream. This is particularly true for fraud analytics, given the ever-changing nature of fraud.
Development of the traditional neural network fraud model requires access to high-quality historical data. This presents a challenge in emerging markets, which tend to have volatile market dynamics, data availability restrictions, fraud tag reporting issues and constantly changing customer behaviors.
To counter this, FICO recently improved a patented self-calibrating fraud analytics technology into what FICO calls Multi-Layered Self-Calibrating (MLSC) analytics. This innovation was specifically designed to further improve detection accuracy in the stream. MLSC also has the benefit of being a robust adaptive modeling technology for changing data environments.
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Source: Bankinganalyticsblog.fico.com
