CreditPrint: Credit Investigation via Geographic Footprints by Deep Learning

Xiao Han, Ruiqing Ding, Leye Wang, Hailiang Huang

AbstractCredit investigation is critical for financial services. Whereas,traditional methods are often restricted as the employed datahardly provide sufficient, timely and reliable information.With the prevalence of smart mobile devices, peoples’ geo-graphic footprints can be automatically and constantly col-lected nowadays, which provides an unprecedented opportu-nity for credit investigations. Inspired by the observation thatlocations are somehow related to peoples’ credit level, thisresearch aims to enhance credit investigation with users’ ge-ographic footprints. To this end, a two-stage credit investiga-tion framework is designed, namely CreditPrint. In the firststage, CreditPrint explores regions’ credit characteristics andlearns a credit-aware embedding for each region by consider-ing both each region’s individual characteristics and cross-region relationships with graph convolutional networks. Inthe second stage, a hierarchical attention-based credit assess-ment network is proposed to aggregate the credit indicationsfrom a user’s multiple trajectories covering diverse regions.The results on real-life user mobility datasets show that Cred-itPrint can increase the credit investigation accuracy by up to10% compared to baseline methods.

IntroductionUser credit investigation is one of the most fundamental ser-vices required by a variety of financial businesses such ascredit card application, loan approval, and insurance pric-ing. In conventional credit industry, the credibility of creditcard holders are often assessed by statistics of their creditcard transaction records (e.g., FICO score1); credit investi-gations for people without any credit card mostly relies onusers’ self-reported information (e.g., income, occupation,and education) and public record information like tax liensand civil judgements (Emekter et al. 2015). Though existingcredit investigations can provide credit scores and preventsevere financial frauds to some extent, the persistently highdebt delinquency and default rates reveal the pitfalls. Ac-cording to the latest loan debt statistics of U.S. for 20192, ap-proximately 5.04% of all credit card loans are 90-plus dayspast due, and the default rate (90 days or more delinquent)of student loan debt is reaching to 9.54%.

Copyright c© 2020, Association for the Advancement of ArtificialIntelligence (www.aaai.org). All rights reserved.

1https://www.fico.com/en/products/fico-score2https://www.newyorkfed.org/newsevents/news/research/2019/20190514

Analytically, conventional credit investigations are pri-marily restricted to the deficiency of the employed data fromseveral aspects:

(i) Richness. The information used for current credit in-vestigations is still very limited and only reflects users’ cred-its from partial aspects. Moreover, the information sam-plings hardly reach a full coverage of people. For instance,FICO score is not available for students without credit cards.

(ii) Timeliness. It is common sense that a user’s credit sta-tus varies along time. For instance, the change of occupationsuch as losing job or getting promoted may lead to changesof credit status. However, it is quite hard for current creditinvestigations to timely update some important informationif the user does not report the change.

(iii) Reliability. For better credit investigation results,people may manipulate their reported information. For in-stance, one may request friends temporarily transfer anamount of money to her/him. Hence, the reliability of suchself-reported information is also doubtful.

With the prevalence of ubiquitous computing equipments,such as smartphones, tablets, and smartwatches, more andmore information can be continuously collected from ourdaily life. This offers a great opportunity to develop novelcredit investigation techniques that may overcome the afore-mentioned limitations. For instance, Sesame Credit3 lever-ages machine learning techniques to analyze users’ credibil-ity through their purchase and socializing behaviors in theTaobao website and app. While pioneering credit investiga-tion studies with ubiquitous computing devices mainly fo-cus on users’ behavioral data in virtual space, such as onlineborrowing, consuming, or entertaining (Ceyhan, Shi, andLeskovec 2011), this paper explores people’s geographicfootprints data from their physical space for credit inves-tigation. Specifically, it investigates whether the geographicfootprints of people (e.g., obtained from smartphones) canreveal characteristics about their credit, and how to predictusers’ credit levels with the footprint data.

Many clues drive us to conduct credit investigationthrough users’ geographic footprints. Firstly, geographicfootprints can be automatically and continuously sensed byup-to-date ubiquitous computing equipments with localiza-tion capacities (e.g., GPS). In other words, geographic foot-

3https://www.creditsesame.com/

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Figure 1: User credit geographic distribution (left, Hangzhou,China) and crime geographic distribution (right, New York, US).

prints can be timely collected with ensured reliability. More-over, an arising research line of work shows that users’ dailytrajectories or their frequently visited locations bear cer-tain social and economic indications that can be used formany predictive applications, such as crime occurrence pre-diction (Huang et al. 2018) and real estate ranking (Fu etal. 2016). More importantly, just as the correlation (e.g., thecorrelation between locations and crime occurrences) find-ings in the prior studies, our preliminary investigation re-veals that locations and visitors’ credit level are somehowrelated (Figure 1). For instance, some locations are full offootprints from high-credit people while some other loca-tions likely assemble low-credit visitors. These observationsdemonstrate the feasibility of credit investigation via geo-graphic footprints and inspire us to uncover the credit char-acteristics embedded in locations which may increase datarichness for credit investigation.

Nevertheless, leveraging users’ daily geographic foot-prints for credit investigation is also non-trivial, facing thefollowing challenges:

(i) While each location can hold various features (e.g.,points-of-interests), few are directly related to the credits ofthe visited users, thus how to characterize the location fromthe credit investigation perspective is the first challenge.

(ii) A person may have multiple footprint trajectories thatcover different sets of locations with diverse (or even con-flicted) credit indications, thus how to merge the credit in-dications from different locations to generate an integratedcredit investigation result is the second challenge.

To address these challenges, this paper makes the follow-ing contributions:

(i) To the best of our knowledge, this paper is one of thepioneering research works studying how users’ geographicfootprints can reveal their credit trustworthiness. The explo-ration of geographic footprints facilitates credit investigationwith better richness, timeliness, and reliability.

(ii) This paper proposes a novel two-stage footprint-basedcredit investigation framework, namely CreditPrint. In thefirst stage, a credit-aware region embedding network is de-signed to learn credit-related representation for each geo-graphic region considering various relationships between re-gions. In the second stage, a hierarchical trajectory-basedcredit assessment network is proposed to fuse a user’s mul-tiple footprint trajectories covering diverse regions into anintegrated representation for final user credit prediction.

(iii) With a real-life user mobility dataset with creditscores including more than 5,000 users, we verify that ourproposed CreditPrint framework can significantly improve

the credit prediction accuracy by more than 10% comparedto baseline methods.

Related WorkIn literature, traditional credit investigation works usuallyleverage users’ reported information, including income, oc-cupation, education, etc., to predict users’ credit scores(Zhao et al. 2017). Various learning techniques have beenused for this purpose, including logistic regression (Emek-ter et al. 2015), random forest (Malekipirbazari and Ak-sakalli 2015), and neural network (Byanjankar, Heikkila,and Mezei 2015). However, as illustrated in the introduc-tion, these types of information may suffer from pitfallsin richness, timeliness, and reliability. Recently, researchersstarted to consider more users’ data collected from perva-sive computing devices. One work similar to ours is usingusers’ mobile phone data to help credit assessment (Ma etal. 2018). Ma et al. (2018) manually extract some statisticsabout users’ mobility patterns (e.g., mean and variance ofthe visited location number) as users’ mobility features forcredit investigation; the limitation is that the specific charac-teristics of each visited location is ignored. In comparison,we propose a region embedding network to extract credit-related representation for each location; also, our trajectory-based credit assessment network can learn hidden featuresuseful for credit investigation beyond manually crafted ones.

With the popularity of ubiquitous and mobile devices,much research has been devoted to mining users’ geographicfootprints for various applications (Zheng 2015), such aslocation-based services (Yang et al. 2014), transportationmanagement (Wang et al. 2019; Chen et al. 2018a), andemergency response (Chen et al. 2018b). Particularly, ourwork can be seen as a novel footprint data classification task.Most classification tasks are relevant to explicit moving pat-terns that can be easily observed by ordinary people, e.g.,whether a car turns round or not (Chen et al. 2018b); incomparison, our research aims to extract footprints’ implicitpatterns related to user credits, which is rather challenging.

The key techniques in CreditPrint are graph convolutionalnetwork (Defferrard, Bresson, and Vandergheynst 2016) andattention (Vaswani et al. 2017) mechanisms. Literature hasshown that graph convolutional network is powerful to en-code spatial relationships (Geng et al. 2019; Chai, Wang,and Yang 2018), and attention mechanisms can effectivelyextract temporal characteristics (Liang et al. 2018). Inspiredby these works, we aim to combine graph convolutional net-work and attention mechanisms to extract implicit credit-related spatiotemporal factors from users’ footprint data.

Problem FormulationWe begin with describing some preliminaries and formulat-ing the user footprint-based credit investigation problem.

DEFINITION 1. Region. A spatial area A (e.g., a city) ispartitioned into m grids of equal-size (e.g., 1km× 1km). Agrid is a region, denoted by r. The set of regions belongingto a spatial area A is denoted byRA = {r1, r2, ..., rb}.

DEFINITION 2: Trajectory. A trajectory t is a user u’stemporal sequence of footprints in terms of regions for a

Figure 2: Framework of CreditPrint.

predefined time period (e.g., one day). Then a user u’strajectory at time period k can be denoted as t

(k)u =

{r(k)1 , r(k)2 , ...r

(k)m }, where r(k)i indicates the visit region at

timestamp of i during time period k.Footprint-based credit investigation problem. Suppose

there are a set of users U and their credit levels Y = {yu|u ∈U}, where yu = 1 indicates that user u is of low creditand yu = 0 presents that u is of high credit. Each user uholds a set of trajectory records Tu = {t(1)u , t

(2)u , ..., t

(n)u }

for n time periods. Given a new user u′ and his trajectoryrecords Tu′ , our main task is to predict the credit level yu′of u′. More specifically, we aim to learn a prediction func-tion yu′ = F(u′|T, Y,Θ), where yu′ denotes the predictedprobability that user u′ is of low credit and will default in thefuture, T = {Tu|u ∈ U} collects all users’ historical trajec-tories, and Θ is a set of model parameters of function F .

CreditPrintIn this section, we propose a CreditPrint framework to ad-dress the footprint-based credit investigation problem. Wefirst overview the proposed approach and then illustrate eachcomponent of the framework.

Framework OverviewIntuitively, if a user frequently visits regions where manypeople of high credit travel, this user may be also of highcredit, and vice-verse. On this basis, firstly, CreditPrint tendsto generate an embedding for each region to characterize thecredit worthiness of people who visit the region; and thenCreditPrint represents a user’s trajectories by sequences ofcredit-aware region embeddings and learns a user’s creditscore through his trajectories. In particular, CreditPrint en-compasses two major components:

(i) Credit-Aware Region Embedding. As observed inFigure 1, some regions are highly attractive to people withhigh credit score to visit, while some others often assemblefootprints of people with low credit score. In other words,regions are of features that can indicate the visitors’ creditscore. Therefore, taking a collection of users’ informationincluding their historical trajectories and credit levels as in-put, the goal is to generate region embeddings for character-izing the regions in terms of credit worthiness. Straightfor-wardly, a region with more footprints of high-credit peopleand less footprints of low-credit people is more likely a high-credit region. In addition, regions may relate to each otherin terms of credit worthiness by various ways. For instance,

people of a certain credit level may regularly traverse two re-gions, which indicates that the two regions may share somecredit characteristics. In this vein, we propose graph convo-lutional neural networks (GCN) to learn region embeddingsby encoding both regions’ individual characteristics and re-lationships in terms of credit worthiness.

(ii) Trajectory-based Credit Assessment. Once regionembeddings are obtained, a trajectory can be easily repre-sented by a sequence of region embeddings with credit char-acteristics. Gated Recurrent Units (GRUs) are naturally de-signed to model sequences and maintain sequential depen-dencies for a long-term. Then, we basically employ GRUsto learn trajectory embeddings by taking the sequences ofcredit-aware region embeddings as input. Ideally, if a tra-jectory of regions that traverses frequently by people withhigh/low credit score, then the trajectory embedding ac-cordingly encodes the characteristics that indicate high/lowcredit state. In reality, a user often exhibits a number oftrajectories. The collective trajectory embeddings of a userare expected to reveal whether the user is of high or lowcredit score. In particular, a user’s trajectory may be af-fected by some context features (e.g., weather and traffic).Thus we attentively merge a user’s collection of trajectoryembeddings by taken into account the context features. Fi-nally, a user embedding in terms of his trajectories and somemanual-crafted trajectory features are concatenated to learnthe user’s credit state.

Credit-Aware Region EmbeddingThis section proposes a credit-aware Region EmbeddingNetwork (REN) to generate region embeddings by consid-ering both the regions’ individual characteristics and the re-gional relationships in terms of credit worthiness. Specifi-cally, we assign each region a credit score with respect to thesynthetical credit levels of visitors in the region to describeits individual credit characteristics. In addition, concerningthat GCN is a popular solution that can generate node em-beddings by aggregating information from the nodes’ neigh-bors, we leverage GCN to encode the complex relationshipsof credit worthiness between regions. As there are perhapsvarious relationships among various regions, we constructmultiple graphs according to the various relationships andpropose a credit-aware region embedding network based onGCN with attentions. Figure 3 illustrates the framework ofcredit-aware region embedding.

Region credit score. We assign a credit score for each re-gion to interpret the tendency of region assembling people oflow credit. Therefore, the more users of low credit visited aregion, the higher score it gains. Specifically, let respectivelydenote the number of visitors with low credit in a region rby |V−r |, and the overall number of visitors in the region rby |Vr|. We compute the region credit score sr by:

sr = |V−r |/|Vr| (1)

Graph Generation. Graph generation is the primary taskfor graph convolutional operation. In our context, regions arethe nodes in the graphs. Edges between any two regions aresupposed to represent their relationships in terms of credit

Figure 3: REN: Credit-Aware Region Embedding by Graph Convolutional Neural Networks with Attention.

worthiness. And the edges are usually assigned with largeweights if the connected two regions are of strong relation-ships. In particular, we propose three types of graphs to cap-ture different types of relationships between regions.

• Region Distance Graph. According to Tobler’s first lawof geography: everything is related to everything else, butnear things are more related than distant things 4. It isalso intuitive that people in a region are more likely tovisit the nearby regions than the distant regions. In otherwords, a region maintains more similar footprints of peo-ple and present more similar credit characteristics to thesurrounding regions than the distant ones. In this vein, weconstruct a distance graph by connecting adjacent regions.

• Region Interaction Graph. In addition to visiting sur-rounding regions, people may also frequently traverse be-tween certain distant regions due to various reasons. Forinstance, a user of high credit may travel from his homeregion to work region every work-day. Naturally, two re-gions are more interacted if users are more likely to tra-verse between them. Following this idea, we connect re-gions if they simultaneously appear to sufficient numberof trajectories and build up an region interaction graph.

• Region Correlation Graph. Despite some regions arenot sequentially traveled by users, they are also perhapsstrongly correlated to each other because of some similarregional attributes. For instance, many high credit usersregularly go to some decent workplaces located in differ-ent regions in the morning of work day; and museumsin different regions are also often visited by high creditusers during weekends. Therefore, we try to capture theregions’ correlations by their similarity of footprint pat-tern. In particular, we construct a dynamic credit scorevector to record the average ratio of low credit visitors tooverall visitors by time (e.g., hour). Two regions are con-nected to build a region correlation graph if their dynamiccredit score vector are correlated.

Multiple Graph Convolution with Attentions. In orderto synthetically capture various types of relationships be-tween regions, we learn to merge the graphs with atten-tions. Assume that we generate M types of graphs G ={G1, G2, ..., GM

} and Ag represents the adjacent matrix of

4https://en.wikipedia.org/wiki/Tobler%27s first law of geography

Gg . We add regions’ self-connection on graph (i.e., Ag ←Ag + I) to ensure that the previous region embeddings canbe involved in the update operations. In addition, we nor-malize the adjacent matrices to eliminate graph differencesand hold stability of parameter learning. Then we have,

Ag = D− 1

2g (Ag + I)D

− 12

g (2)

where Dg is a diagonal degree matrix with entries Diig =∑

j Aijg . Assume the weight of graph gq is represented by

Wg . We try to normalize the graph weight and merge variousgraphs with attentions as:

ag = softmax(Wg) =exp(Wg)∑Mg=1 exp(Wg)

(3)

AG =

M∑g=1

ag � Ag (4)

where � stands for element-wise product. After attentivelymerge the graphs, we conduct convolutional operations toupdate the hidden state by:

H(l+1)G = σ(AGH

(l)G W l

G) (5)

Loss Function of REN It is worth noting that there isa significant difference between our model and conven-tional graph convolution neural networks (GCN). In gen-eral, GCNs require a supervised training for obtaining theparameters by minimizing the errors between the actual andpredicted labels with the following loss function:

L =∑r∈R

yr log σ(XTr θ) + (1− yr) log(1− σ(XT

r θ)) (6)

where yr indicate the region credit label in our context. Weset yr = 1 if sr is larger than the median value of regioncredit scores, otherwise, yr = 0.

We notice that the regions with similar credit characteris-tics should also have similar embeddings while the embed-dings of regions with diverse credit scores are supposed tobe unlike. Therefore, we propose a region embedding simi-larity regularizer to supervise the learning. Specifically, werespectively use R+ and R− to denote the high credit re-gions and low credit regions. We set a similarity threshold δto identify similar region pairs. In brief, given a region r, its

Figure 4: Trajectory-based Credit Assessment Networks.

similar regions rs ∈ Rs satisfies two conditions: (1) r andrs belong to the same region set, and (2) the credit score dif-ference between r and rs is smaller than δ. We also samplethe same number of dissimilar region pairs for each region,where r’s dissimilar regions rd ∈ Rd also satisfies two con-ditions: (1) r and rs are from different region sets, and (2)the credit score difference between r and rs is larger than δ.Finally, we introduce the region similarity loss as:

Lr =∑r∈R

∑rs∈Rsrd∈Rd

log σ(Xr, Xrs) + (1− log σ(Xr, Xrd))

(7)where σ(x1, x2) = 1

1+exp(〈x1,x2〉) is a sigmoid functionand 〈x1, x2〉 is the inner product of x1 and x2. To train ourmodel, the unified loss function can be written as:

LREN = L+ γRENLr (8)

Trajectory-based Credit AssessmentIn general, a user u has a number of trajectories which con-tains multiple ordered regions. With the region embeddingsthat encode regions’ credit characteristics, we represent u’strajectories by sequences of region embeddings. Then, wepropose a hierarchical Trajectory-based Credit AssessmentNetworks (i.e., T-CAN), which firstly encodes each trajec-tory of u by Trajectory Embedding Networks (i.e., TEN) andthen predicts u’s credit level with all of his trajectory em-beddings by Credit Prediction Networks. Figure 4 shows theframework of T-CAN.

Trajectory Embedding Network Since a trajectory isrepresented as a sequence of region embeddings, TEN em-ploys GRU, which is one of the most popular sequencemodel, to carry out trajectory embedding. A basic GRU the

following update functions:

qri =σ(W qXri + Uqzri−1) (9)

γri =σ(W γXri + Uγzri−1) (10)

hri =tanh(WhXri + Uh(zri−1� γri)) (11)

zri =(1− qri)� hri + qri � zri−1(12)

whereXri is the i-th input region embedding of a trajectory,zri is the hidden state, qri is the update gate, hri is the can-didate activation, σ(·) represents the sigmoid function, andW q , W γ , Wh, Uq , Uγ , Uh are learnable parameters.

It is worth noting that the regions in a trajectory maypresent different importance when interpreting the trajec-tory’s credit characteristics. For instance, the last region (i.e.,destination) of a trajectory is often regarded more importantthan the region passed by in the middle, as the credit embed-ding of destination is more representative of the trajectory’scredit characteristics. Therefore, we introduce temporal at-tention function to differentiate the inputs’ weight as:

ari =exp(XriWt)∑mj=1 exp(XrjWt)

(13)

where ari is the attention score of i-the region of a trajectory,zri is the hidden state and Wt is a trainable parameter. Thetraining process is supervised by users’ credit level. In thislight, the region embeddings that are more representative toa user’s credit level are supposed to be assigned with a higherscore. The attention score is used to control the update ofhidden state, which can be rewritten as follows:

zri = (1− ari)zri−1+ arihri (14)

Taking the last hidden state zrm of a trajectory t as input, adense network is used to output t’s embedding Xt.

Credit Prediction Network Ideally, each of a user u’strajectory embeddings is encoded of u’s credit characteris-tics and can be used to predict u’s credit level. Althoughu’s credit characteristics are relatively stable, u’s trajecto-ries are often highly affected by surrounding context suchas weather, wordday/weekend, or traffic, which may lead agreat variance to u’s trajectory embeddings. Therefore, wetake several measures to mitigate the variance due to contextfeatures, learn the stable credit characteristics and accuratelypredict u’s credit level.

Firstly, we represent a trajectory’s surrounding context bya one-hot encoding feature vector and incorporate the con-text features to the trajectory’s embedding. This incorpora-tion could not only supervise trajectory embedding learn-ing but also benefit for the credit level prediction. Secondly,to avoid prediction bias of a single trajectory, we aggregateu’s trajectory embeddings to synthetically predict u’s creditlevel. Thirdly, as a user’s essential credit characteristics areoften stable in periods of time, we regularize the similarity auser’s two trajectories to further supervise the trajectory em-bedding process. We will elaborate the trajectory similarityregulation in the next subsection.

Let denote the context feature of trajectory t(i)u by C(i)tu .

After concatenating X(i)tu and C(i), we aggregate a user’ tra-

jectory embeddings to obtain a user embedding XTuby the

following attention score:

a(i)tu =

exp(Concat(X(i)tu , C

(i)tu )Wu)∑n

j=1 exp(Concat(X(j)tu , C

(j)tu )Wu)

(15)

XTu =

n∑i=1

a(i)tu X

(i)tu (16)

where n is the number of u’s trajectories and Wu is a train-able parameter. Since a few manual-crafted trajectory fea-tures can significantly differentiate the high and low creditusers, we also concatenate u’s manual-crafted trajectory fea-tures to his embedding XTu . A dense network with ReLUactivation is then used to predict u’s credit label.

Loss Function of T-CAN To predict a user’s credit level,we simultaneously train the trajectory embedding networksand the credit prediction networks. The primary goal oftraining is to minimize the user credit prediction error, i.e.,logarithmic loss between the users’ actual and predictedcredit level:

Lc =∑u∈U

yu log σ(XTuθ) + (1− yu) log(1− σ(XTu

θ))

(17)In addition, users’ trajectory embeddings are expected to en-code his credit characteristics. As a user’s credit character-istics are relatively stable, the embeddings of a user’s differ-ent trajectories are preferably similar. Accordingly, we in-troduce a trajectory similarity regularizer to supervise thetrajectory embedding process:

Ltu =∑u∈U

∑i 6=j

t(i)u ,t(j)u ∈Tu

log σ(X(i)tu , X

(j)tu ) (18)

where X(i)tu and X(j)

tu are embeddings of any two of u’s tra-jectories. Ideally, the trajectory similarity regularizer can di-rect the learner to extract a user u’s essential credit charac-teristics reflected by u’s various trajectories. Combining theuser credit prediction error and trajectory similarity regular-ization, the complete loss function of T-CAN is written as:

LT-CAN = Lc + γT-CANLtu (19)

ExperimentIn this section, we leverage real-life user mobility datasets toverify the effectiveness of CreditPrint. We first describe theexperiment setup and then report the evaluation results.

Experimental SetupData. We use a user mobility dataset including more than5000 users’ trajectories and their credit label (whether theypay their mobile phone bill or not) for our experiment. Thedata is collected from Hangzhou, China by one mobile op-erator. The region is set to 1km*1km, and the data spans forone month. Figure 1 (left) gives an overview of our experi-ment area. We use 80% of users as training, 10% as valida-tion, and 10% as test.

Name Description

num daily region number of visited regions per day

std daily region standard deviation of visited regions perday

region entropy entropy calculated by the region visitingfrequency

turning radius mean distance of the visited regions to themost-frequently-visited region

weekday weekend diff difference between the number of visitedregions in weekday and weekend

num record days number of days with at least one visitedregion

Table 1: Manual features used in experiments

Benchmarks. To compare with our method, we imple-ment the following benchmark approaches. Assessing users’credit from footprints is still a very novel direction, and pio-neering related work usually learns it from manually craftedfeatures (Ma et al. 2018). Accordingly, we design the fol-lowing benchmark approaches:

• Manual-LR: Using logistic regression (Emekter et al.2015) with manual features for credit prediction.

• Manual-RF: Using random forest (Malekipirbazari andAksakalli 2015) with manual features for credit predic-tion.

• Manual-NN: Using neural network (Byanjankar,Heikkila, and Mezei 2015) with manual features forcredit prediction.

The manual features used in the experiment are summarizedin Table 1. In addition, we implement several variants ofCreditPrint by removing some parts of the framework to ver-ify that every proposed part can contribute to CreditPrint.

• CreditPrint without REN: This approach does not use theregion embedding network (REN), but directly use thecredit score (probability) of each region as their feature.The other parts are same as CreditPrint.

• CreditPrint without TEN: This approach does not use tra-jectory embedding network (TEN), but directly use theaverage region embedding of a user’s visited regions asthe trajectory representation. The other parts are same asCreditPrint.

Metrics. We adopt the widely-used binary classificationmetric, AUC (Area Under Curve) to verify the effectivenessof user credit investigation.

Network Structure. Our region embedding network usestwo graph convolutional layers each with 32 hidden units,and thus outputs a 32-dimension hidden vector as regionrepresentation. The trajectory embedding network generatesa 128-dimension trajectory representation with one layer ofGRU. We use such a design of the network structure becauseit performs well in the validation data set.

Method AUC

Manual-LR 0.701Manual-RF 0.695Manual-NN 0.707

CreditPrint without REN 0.732CreditPrint without TEN 0.723CreditPrint (full model) 0.784

Table 2: Evaluation results

0.50.550.60.650.70.750.8

Distance

Interaction

Correlation Fu

ll

AUC

Figure 5: Region graphs

0.50.550.6

0.650.7

0.750.8

Full no reg.sim.

no traj.sim.

AU

C

Figure 6: Region/Trajectorysimilarity regularizer.

0.50.550.6

0.650.7

0.750.8

16 32 64

AU

C

Region Embedding Dimensions

0.50.550.6

0.650.7

0.750.8

32 64 128 256

AU

C

Trajectory Embedding Dimensions

Figure 7: Region/Trajectory Embedding Dimensions.

ResultsTable 2 shows the evaluation results of CreditPrint andbenchmark approaches. First, we can see all of the Cred-itPrint variants perform significantly better than manual-feature-based benchmark approaches. This indicates thatconsidering the characteristics of a user’s visited locationscan actually give more credit-related information besides themobility features manually extracted in literature. Particu-larly, our full CreditPrint model can increase AUC by morethan 10% compared to manual feature based benchmarks.

Second, we observe that our full CreditPrint model canalso outperform other CreditPrint variants, verifying the ef-fectiveness of each component designed in CreditPrint. Par-ticularly, compared to CreditPrint without REN/TEN, ourfull model can increase AUC by around 7.1/8.4%. This re-sult highlights that both region embedding and trajectoryembedding of CreditPrint are important for final credit as-sessment. Next, we verify more detailed design considera-tions in CreditPrint.

Region Graph. To verify the effectiveness of combiningmultiple region graphs (distance, interaction, and correla-tion graphs), we use only one graph to learn the region em-bedding for final credit assessment and compare to our fullmodel with three graphs. Figure 5 shows the results and re-veals that, among three graphs, the correlation graph cancontribute most to the credit investigation. It also demon-strates that combining the three graphs together can furtherimprove the prediction performance in terms of AUC.

Region and Trajectory Similarity Regularizer. In Cred-itPrint, we have introduced the region similarity regularizer(Eq. 7) and trajectory similarity regularizer (Eq. 18) to as-sist the region and trajectory embedding learning. To verifytheir effects, we perform the experiment by removing theregion similarity regularizer (set γREN = 0 in Eq. 8) or thetrajectory similarity regularizer (set γT-CAN = 0 in Eq. 19),and then check the credit prediction results (Figure 6). Par-ticularly, by removing the region similarity regularizer, AUCis reduced from 0.784 to 0.775; by removing the trajectorysimilarity regularizer, AUC is reduced from 0.784 to 0.756.These results verify that both region and trajectory similarityregularizers can help learn better representation for a user toreflect her/his credit level.

Dimension of Region and Trajectory Embedding. Webriefly show how the results change according to the changeof the dimensions of region and trajectory embedding in Fig-

ure 7. In general, we can see that the AUC does not changea lot by varying the dimensions of region embedding or tra-jectory embedding. We choose the region embedding dimen-sion as 32, and the trajectory embedding dimension as 128,as these settings perform better than the others.

Discussion and Conclusion

This paper proposes a new credit investigation frameworkleveraging users’ geographic footprint data, called Credit-Print. The novelty of CreditPrint lies in two aspects. First,a credit-aware region embedding network is proposed to en-code regions’ characteristics and cross-region relationshipsto generate a credit-related region embeddings. Second, a hi-erarchical credit assessment network is constructed to learna user’s trajectory embedding from region embeddings, andfurther merge a user’s multiple trajectory embeddings intoan integrated user footprint feature vector with attentions forcredit assessment. Evaluation results on real user mobilitydataset verify the effectiveness of CreditPrint compared tothe traditional manual crafted feature based methods.

It is worth noting that, while this paper focuses on us-ing users’ geographic footprints for credit investigation, theCreditPrint framework can be easily combined with otherfeatures that commonly used for credit assessment, suchas income and education. Specifically, once we have suchusers’ information, we can simply put it together with thehand-crafted footprint features and concatenate them to theusers’ footprint embeddings, as shown in Figure 4.

Our research can also have implications for other researchworks depending on geographic footprint data mining. Forexample, persons’ moving patterns have been revealed to becorrelated to their mental health (Huang et al. 2016). Simi-lar to CreditPrint, we may develop a framework to first learnregions’ mental health-related embeddings and then extractusers’ footprint features by merging these region embed-dings. This may enrich features for mental health predictionin (Huang et al. 2016).

In the future, we plan to extend CreditPrint in several as-pects. First, we will try to find more scenarios, e.g., peer-to-peer lending, to evaluate whether users’ geographic foot-prints can also indicate their credit well. Second, we will ex-plore how to transfer the credit investigation model learnedby CreditPrint from one area to another area, so as enhanceits generalizability and applicability.

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