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Cutting Through ‘Noisy’ Credit Data – and Helping Banks in the Process
Accurate forecasts about loan performance are becoming a top priority, especially when banks merge or acquire and when they must combine their loan performance data sets.
But “noisy” data corruption, particularly that of missing or mislabeled records, presents a challenge to develop default models with any hope of accuracy.
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