Vehicle financial loan waiting time estimation method, device, and calculation medium

A waiting time and financial technology, applied in finance, computing, neural learning methods, etc., can solve problems such as poor prediction accuracy, impact of waiting time, large difference in processing time, etc., to avoid data errors, improve prediction accuracy, The effect of reducing the burden

Active Publication Date: 2021-12-24
长安汽车金融有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This solution is suitable for scenarios where the number of queues fluctuates less over time, and auto loans are affected by factors such as new car releases, holidays, auto shows, etc., and cannot take advantage of changes in real-time application data on the day, resulting in poor prediction accuracy
[0006] (3) Conventional queuing follows the first-come-first-served basis. However, in the auto finance business scenario, due to special circumstances (such as supplementary documents, customer reasons, etc.), there will be frequent urgent (jumping in the queue) situations, and the relevant waiting time estimation plan Less support for urgent cases
In addition, the processing time required for individual loan review varies greatly, and the waiting time will be affected by other applications of different levels, which makes it difficult to accurately predict the time

Method used

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  • Vehicle financial loan waiting time estimation method, device, and calculation medium
  • Vehicle financial loan waiting time estimation method, device, and calculation medium
  • Vehicle financial loan waiting time estimation method, device, and calculation medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0041] This embodiment proposes a method for estimating the waiting time of an auto finance loan based on a neural network, including a data collection and processing stage, a model training stage, and a prediction stage.

[0042] In the data collection and processing stage, it is first necessary to collect multiple auto finance loan application forms, extract and encode the characteristic information of each auto finance loan application form. In this embodiment, data related to queuing time for about half a year was collected by burying points on the big data platform, including policy data, personnel data, process time node data, etc. Specifically, the data extracted from the big data platform mainly consists of four tables: Application form processing flow table, application form level table, application form level rule table and approver level rule table, enter simple data processing such as table connection, and the final production fields include: application form number...

Embodiment 2

[0048] This embodiment is improved on the basis of Embodiment 1, and a specific method for extracting time features is proposed, but it is obvious that the time feature extraction method of the present invention is not limited to the form of this embodiment.

[0049] The time feature can include the entry queue time, processing start time, and processing completion time of the auto finance loan application form. Taking the extraction of the entry queue time as an example, the main elements of the time feature are the time when the application form entered the queue, the day of the week, and the time period. In this embodiment, considering business commuting and rest time (such as going to work at 8.30, lunch break at 11.30-1.30), 30 minutes are divided into a time period, and the form of the first few minutes in the time period is reflected, so that the expression form of the extracted time feature It is (a, b, c), where a, b and c are all positive integers, and a∈[1, 7] repres...

Embodiment 3

[0052] This embodiment improves on the basis of Embodiments 1 and 2, and proposes a specific method for encoding time features. In this embodiment, the time features are average-coded, and the formula is as follows:

[0053]

[0054] Where x is the time feature to be encoded. For example, when encoding the time feature extracted in Embodiment 2, x involves the three fields a, b, and c, and xi represents the time feature of a certain actual value, such as implementing (3, 21, 13) in Example 2. no i is x=x i When the number of samples, N is the total number of samples, y is the target value, means x=x i The mean value of y corresponding to , is the mean of y over the entire training set.

[0055] λ(n i ) represents the balance coefficient between the special case and the whole, λ(n i )∈[0,1] is responsible for calculating the reliability of two probability values, λ(n i )=0.5 means that the reliability of the two probabilities is equal, with n i As the value increa...

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Abstract

The invention provides a vehicle financial loan waiting time estimation method, a device and a calculation medium. The estimation method comprises a data collection and processing stage, a model training stage and a prediction stage, firstly, an vehicle financial loan application form is collected, feature information is extracted and encoded, and the feature information comprises a time feature, a risk level feature and a quantity related feature; then, a neural network model is constructed, the neural network model is trained and verified through a sample set, and the sample set comprises the coded value of the feature information corresponding to each vehicle financial loan application form obtained in the data collecting and processing stage and the actual waiting time; and finally, obtaining a to-be-predicted vehicle financial loan application form, extracting a corresponding feature information code, inputting the feature information code into the trained neural network model, and outputting a predicted waiting time result by the trained neural network model. Compared with a traditional isochronous pre-estimation scheme, the method has the advantages that more complex data can be processed, more accurate pre-estimation time can be obtained, and more automatic model deployment can be realized.

Description

technical field [0001] The invention belongs to the technical field of queuing prediction, and relates to a method, equipment and computing medium for predicting the waiting time of an auto finance loan based on a neural network. Background technique [0002] When customers are waiting for the approval of auto finance loans, the waiting time of uncertain time is longer than the limited waiting time of known time. The waiting time estimation model can provide accurate estimated time, let customers know the loan progress and time, and facilitate customers to determine the car purchase Arrangement, ease the dissatisfaction caused by time waiting, and improve the customer experience as much as possible. [0003] At present, related technologies have been proposed in the fields of restaurant call time estimation and takeaway waiting time prediction, but there are still the following shortcomings: [0004] (1) Due to the different types of data processed in different fields and d...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q40/02G06N3/04G06N3/08G06F16/25
CPCG06N3/084G06F16/254G06N3/045G06Q40/03
Inventor 陈邦玮张胜庆曹家楷张浩
Owner 长安汽车金融有限公司
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