Abstract: Various embodiments described herein support or provide for data management operations, such as identifying an uncalibrated fraud score that corresponds to a set of transactions; using a machine learning model to generate a calibrated fraud score based on the uncalibrated fraud score; determining a calibrated fraud score distribution associated with the calibrated fraud score; identifying an uncalibrated fraud score distribution associated with the uncalibrated fraud score; using a score scaling function to generate a mapping between the calibrated fraud score distribution and the uncalibrated fraud score distribution; and generating a scaled calibrated fraud score based on the mapping.

Abstract: A method and apparatus for machine learning model based fraud detection of transactions are described. The method may include determining, by a fraud detection system, a first block rate of a first fraud detection machine learning model (MLM) that scores transactions of a server computer system, wherein the fraud detection system blocks a transaction when a score generated for the transaction by the first fraud detection MLM does not satisfy a first threshold value. The method may also include accessing, by the fraud detection system, a second fraud detection MLM to be used by the fraud detection system for blocking future transactions. Furthermore, the method may include determining, by the fraud detection system, a second threshold value using a set of prior transactions input into the second fraud detection MLM that results in a second block rate of the second fraud detection MLM within a predetermined margin of the first threshold.

Abstract: A method of detecting and quantifying blur in a digital image, making use of a computer and comprising: a step a) of obtaining a digital image comprising pixels; a step b) of obtaining a brightness parameter for each pixel, said step b) comprising a convolution operation with an edge detection matrix; a step c) of calculating a score S1 comprising the maximum, calculated over all the pixels, of the brightness parameter obtained in step b) and a second score S2 comprising a logistic function, and a step d) of evaluating the digital image, said digital image being considered to be blurry if the score S1 obtained in step c) is strictly less than a first predetermined threshold S10, the score S1 providing a first quantity of blur present in the digital image.

Abstract: A method of detecting and quantifying blur in a digital image, making use of a computer and comprising: a step a) of obtaining a digital image comprising pixels; a step b) of obtaining a brightness parameter for each pixel, said step b) comprising a convolution operation with an edge detection matrix; a step c) of calculating a score S1 comprising the maximum, calculated over all the pixels, of the brightness parameter obtained in step b) and a second score S2 comprising a logistic function, and a step d) of evaluating the digital image, said digital image being considered to be blurry if the score S1 obtained in step c) is strictly less than a first predetermined threshold S10, the score S1 providing a first quantity of blur present in the digital image.