WEAKLY-SUPERVISED FRAUD DETECTION FOR TRANSPORTATION SYSTEMS VIA MACHINE LEARNING
Example methods and systems disclosed herein train an accurate machine-learned model that detects fraud within an electronic transportation system. A first model is trained on a first (comparatively small) set of trip data items representing trips taken, or requested, in the electronic transportation system. The first set of trip data items have been manually labeled by human analysts to determine whether the trips were or were not fraudulent. The first model is used to generate weak labels for a second (comparatively larger) set of trip data items that lack manual labels. The weak labels are used along with the second set of trip data items to train a second model that is more accurate than the first model for detecting fraud.
1 . A computer-implemented method for detecting fraudulent trips within an electronic transportation system, the computer-implemented method comprising:
training a generative model from a first set of trip data items representing a corresponding first set of trips, the trip data items having manual labels indicating whether the corresponding trips are fraudulent;
obtaining a set of weak labels for a corresponding second set of trip data items representing a corresponding second set of trips by applying the generative model to the second set of trip data items, the second set of trip data items comprising more trip data items than the first set of trip data items and lacking manual labels indicating whether the second set of trips are fraudulent, the set of weak labels indicating probabilities that the second set of trips are fraudulent;
training a discriminative model from the second set of trip data items and the set of weak labels; and
obtaining a fraud score for a trip data item representing a requested trip that has not begun, by applying the discriminative model to the trip data item.
2 . The computer-implemented method of claim 1 , further comprising grouping the first set of trip data items into a positive training set and a negative training set based on the manual labels.
3 . The computer-implemented method of claim 1 , further comprising:
applying fraud rules to the first set of trip data items to obtain corresponding fraud values;
wherein the generative model is trained at least in part based on the fraud values.
4 . The computer-implemented method of claim 3 , further comprising:
obtaining additional trip data items;
obtaining additional fraud rules specified by data analysts;
applying the additional fraud rules to a third set of trip data items to obtain corresponding additional fraud values; and
re-training the generative model and the discriminative model based, at least in part, on the additional fraud values.
5 . The computer-implemented method of claim 1 , wherein the trip data items include at least one of: information about drivers of the trips, information about riders of the trips, times of the trips, locations of the trips, or sensor data from client devices used on the trips.
6 . The computer-implemented method of claim 1 , further comprising providing the trip data item to an analyst review process responsive to the fraud score being greater than a threshold.
7 . The computer-implemented method of claim 6 , further comprising:
obtaining a manual label for the trip data item from a human analyst; and
using the manual label and the trip data item to re-train the generative model.
8 . A non-transitory computer-readable storage medium storing instructions that when executed by a computer processor perform actions comprising:
training a generative model from a first set of trip data items representing a corresponding first set of trips, the trip data items having manual labels indicating whether the corresponding trips are fraudulent;
obtaining a set of weak labels for a corresponding second set of trip data items representing a corresponding second set of trips by applying the generative model to the second set of trip data items, the second set of trip data items comprising more trip data items than the first set of trip data items and lacking manual labels indicating whether the second set of trips are fraudulent, the set of weak labels indicating probabilities that the second set of trips are fraudulent;
training a discriminative model from the second set of trip data items and the set of weak labels; and
obtaining a fraud score for a trip data item representing a requested trip that has not begun, by applying the discriminative model to the trip data item.
9 . The non-transitory computer-readable storage medium of claim 8 , the actions further comprising grouping the first set of trip data items into a positive training set and a negative training set based on the manual labels.
10 . The non-transitory computer-readable storage medium of claim 8 , the actions further comprising:
applying fraud rules to the first set of trip data items to obtain corresponding fraud values;
wherein the generative model is trained at least in part based on the fraud values.
11 . The non-transitory computer-readable storage medium of claim 10 , the actions further comprising:
obtaining additional trip data items;
obtaining additional fraud rules specified by data analysts;
applying the additional fraud rules to a third set of trip data items to obtain corresponding additional fraud values; and
re-training the generative model and the discriminative model based, at least in part, on the additional fraud values.
12 . The non-transitory computer-readable storage medium of claim 8 , wherein the trip data items include at least one of: information about drivers of the trips, information about riders of the trips, times of the trips, locations of the trips, or sensor data from client devices used on the trips.
13 . The non-transitory computer-readable storage medium of claim 8 , the actions further comprising providing the trip data item to an analyst review process responsive to the fraud score being greater than a threshold.
14 . The non-transitory computer-readable storage medium of claim 13 , the actions further comprising:
obtaining a manual label for the trip data item from a human analyst; and
using the manual label and the trip data item to re-train the generative model.
15 . A computer system comprising:
a computer processor; and
a non-transitory computer-readable storage medium storing instructions that when executed by a computer processor perform actions comprising:
training a generative model from a first set of trip data items representing a corresponding first set of trips, the trip data items having manual labels indicating whether the corresponding trips are fraudulent;
obtaining a set of weak labels for a corresponding second set of trip data items representing a corresponding second set of trips by applying the generative model to the second set of trip data items, the second set of trip data items comprising more trip data items than the first set of trip data items and lacking manual labels indicating whether the second set of trips are fraudulent, the set of weak labels indicating probabilities that the second set of trips are fraudulent;
training a discriminative model from the second set of trip data items and the set of weak labels; and
obtaining a fraud score for a trip data item representing a requested trip that has not begun, by applying the discriminative model to the trip data item.
16 . The computer system of claim 15 , the actions further comprising grouping the first set of trip data items into a positive training set and a negative training set based on the manual labels.
17 . The computer system of claim 15 , the actions further comprising:
applying fraud rules to the first set of trip data items to obtain corresponding fraud values;
wherein the generative model is trained at least in part based on the fraud values.
18 . The computer system of claim 17 , the actions further comprising:
obtaining additional trip data items;
obtaining additional fraud rules specified by data analysts;
applying the additional fraud rules to a third set of trip data items to obtain corresponding additional fraud values; and
re-training the generative model and the discriminative model based, at least in part, on the additional fraud values.
19 . The computer system of claim 15 , wherein the trip data items include at least one of: information about drivers of the trips, information about riders of the trips, times of the trips, locations of the trips, or sensor data from client devices used on the trips.
20 . The computer system of claim 15 , the actions further comprising providing the trip data item to an analyst review process responsive to the fraud score being greater than a threshold.