CREDIT RISK PREDICTION METHOD AND DEVICE BASED ON LSTM MODEL
First Claim
1. A computer-implemented method for credit risk prediction based on an Long Short-Term Memory (LSTM) model, the method comprising:
- obtaining behavior data of a target account in a period, wherein the period comprises a plurality of time intervals;
generating, based on the behavior data of the target account, a sequence of behavior vectors, each behavior vector corresponding to one of the time intervals;
inputting the generated sequence of behavior vectors into an LSTM encoder in an LSTM model to obtain hidden state vectors each corresponding to one of the time intervals, wherein the LSTM model comprises the LSTM encoder and an LSTM decoder; and
obtaining a risk score of the target account in a next time interval by inputting the hidden state vectors into the LSTM decoder, wherein the next time interval is next to the last time interval in the plurality of time intervals.
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Abstract
Methods, systems and apparatus for credit risk prediction based on an Long Short-Term Memory (LSTM) model are provided. One of the methods includes obtaining behavior data of a target account in a period that includes a plurality of time intervals, and generating, based on the behavior data of the target account, a sequence of behavior vectors. Each behavior vector corresponds to one of the time intervals. The method further includes inputting the generated sequence of behavior vectors into an LSTM encoder in the LSTM model to obtain hidden state vectors each corresponding to one of the time intervals, and obtaining a risk score of the target account in a next time interval by inputting the hidden state vectors into an LSTM decoder of the LSTM model. The next time interval is next to the last time interval in the plurality of time intervals.
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Citations
20 Claims
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1. A computer-implemented method for credit risk prediction based on an Long Short-Term Memory (LSTM) model, the method comprising:
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obtaining behavior data of a target account in a period, wherein the period comprises a plurality of time intervals; generating, based on the behavior data of the target account, a sequence of behavior vectors, each behavior vector corresponding to one of the time intervals; inputting the generated sequence of behavior vectors into an LSTM encoder in an LSTM model to obtain hidden state vectors each corresponding to one of the time intervals, wherein the LSTM model comprises the LSTM encoder and an LSTM decoder; and obtaining a risk score of the target account in a next time interval by inputting the hidden state vectors into the LSTM decoder, wherein the next time interval is next to the last time interval in the plurality of time intervals. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for credit risk prediction based on an Long Short-Term Memory (LSTM) model, comprising:
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one or more processors; and one or more computer-readable memories coupled to the one or more processors and having instructions stored thereon that are executable by the one or more processors to perform a method comprising; obtaining behavior data of a target account in a period, wherein the period comprises a plurality of time intervals; generating, based on the behavior data of the target account, a sequence of behavior vectors, each behavior vector corresponding to one of the time intervals; inputting the generated sequence of behavior vectors into an LSTM encoder in an LSTM model to obtain hidden state vectors each corresponding to one of the time intervals, wherein the LSTM model comprises the LSTM encoder and an LSTM decoder; and obtaining a risk score of the target account in a next time interval by inputting the hidden state vectors into the LSTM decoder, wherein the next time interval is next to the last time interval in the plurality of time intervals. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
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20. A non-transitory computer-readable storage medium configured with instructions executable by one or more processors to cause the one or more processors to perform operations comprising:
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obtaining behavior data of a target account in a period, wherein the period comprises a plurality of time intervals; generating, based on the behavior data of the target account, a sequence of behavior vectors, each behavior vector corresponding to one of the time intervals; inputting the generated sequence of behavior vectors into an LSTM encoder in an LSTM model to obtain hidden state vectors each corresponding to one of the time intervals, wherein the LSTM model comprises the LSTM encoder and an LSTM decoder; and obtaining a risk score of the target account in a next time interval by inputting the hidden state vectors into the LSTM decoder, wherein the next time interval is next to the last time interval in the plurality of time intervals.
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Specification