Applying Convolutional-GRU for Term Deposit Likelihood Prediction

International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
Volume-11, Issue-3 (June 2021)
www.ijemr.net https://doi.org/10.31033/ijemr.11.3.42
265 This Work is under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Applying Convolutional-GRU for Term Deposit Likelihood Prediction
Shawni Dutta1
, Payal Bose2
, Vishal Goyal3
and Samir Kumar Bandyopadhyay4
1
Assistant Professor, Department of Computer Science, The Bhawanipur Education Society College, Kolkata, INDIA
2
Research Scholar, GLA University, Mathura-Delhi Road, Mathura, Chaumuhan, Uttar Pradesh, INDIA
3
Professor, Department of Computer Science, GLA University, Mathura-Delhi Road, Mathura, Uttar Pradesh, INDIA
4
Professor, Department of Computer Science, The Bhawanipur Education Society College, Kolkata, INDIA
4
Corresponding Author: 1954samir@gmail.com
ABSTRACT
Banks are normally offered two kinds of deposit
accounts. It consists of deposits like current/saving account
and term deposits like fixed or recurring deposits.For
enhancing the maximized profit from bank as well as
customer perspective, term deposit can accelerate uplifting
of finance fields. This paper focuses on likelihood of term
deposit subscription taken by the customers. Bank
campaign efforts and customer detail analysis caninfluence
term deposit subscription chances. An automated system is
approached in this paper that works towards prediction of
term deposit investment possibilities in advance. This paper
proposes deep learning based hybrid model that stacks
Convolutional layers and Recurrent Neural Network
(RNN) layers as predictive model. For RNN, Gated
Recurrent Unit (GRU) is employed. The proposed
predictive model is later compared with other benchmark
classifiers such as k-Nearest Neighbor (k-NN), Decision tree
classifier (DT), and Multi-layer perceptron classifier
(MLP). Experimental study concludesthat proposed model
attainsan accuracy of 89.59% and MSE of 0.1041 which
outperform wellother baseline models.
Keywords-- Term Deposit Subscription, Neural
Network, GRU, Convolutional Layers, DT, MLP, k-NN
I. INTRODUCTION
Banking sector in globe plays a significant role
to boost up socio-economic structure. Actually banks are
service sector and provide support to its account
holders.Deposits by account holders to banks are
essential key factors for sustaining financial health of the
banks. The opening of new bank or banks needs not only
marketing but also campaigns. Mass campaigns target at
different places for general mass and direct marketing
campaigns are made with the target of a specific group.
In direct marketing, the response is very low [1]. Direct
marketing is not always fruitful since people may incline
to the established banks. Due to evolving telemarketing
through Computer technology/mobile it became now
easy to generate a variety of reports through marketing
campaigns and also other types of information require
for the organizations. Several savings schemes offered
by banks include term deposit, recurring deposit, fixed
deposit, and deposits in savings account, current account
and many more [2]. In the paper, only term deposit
investment scheme is considered since it increase health
of the bank as well as health of the account holders as
one of the important investing scheme since it facilitates
the bank as well as the customers. Telemarketing
campaignsinfluenceon term deposit account subscription
and the impact of these campaignsis taken into
consideration by this paper.
A recommender system has been proposed in
this paper that automaticallypredicts the possibilities of
term deposit investments from client side. Term deposit
is beneficial from the client side as well as the bank’s
perspective. Fixed amount of money is locked up for
definite period of time with higher interest rates than
traditional saving accounts. This will assist in gaining
maximized profit not only for customers but for the
banking sectors’ investment as well. Term deposit is
often seen as an outcome of bank market campaigns.
Data mining and knowledge discovery processes
[3]often play interesting role while analyzing and
identifying hidden patterns and/or relationship in an
enormous amount of data. Bank campaign data can be
analyzed using data mining techniques and term deposit
possibilities of clients may be determined beforehand. If
term deposit investment possibilities are known in
advance, bank sectors can look into the matter to attract
clients towards their term deposit schemes.
Machine Learning (ML) techniques are useful
for learning and utilizing the patterns discovered from
large database. ML techniques can be applied on set of
information in order to recognize underlying relationship
patterns from this information set. Later, the learning can
be tested in terms of incoming unknown set of patterns.
Deep learning (DL)[4]often regarded as subfield of ML,
can process information with minimal processing due to
its self-adaptive structure. Deep learning is an expansion
over conventional artificial neural networks since it
facilitates the construction of networks by incorporating
more than two layers[4][5]. DL based framework is
implemented in this paper that is dedicated for
improving the efficiency in term depositsubscription
prediction using bank campaign data.
This paper proposes Convolutional-RNN based
model for determining term deposit probabilities agreed
by customers. To address the mentioned problem,
convolutional layer[4]and Gated Recurrent Unit (GRU)
[4]layers are stacked under single platform for
implementing recommender system. Strength of
campaign results, customer loan history, job profile,

marital status etc. are considered as influential factors
while identifying whether customer will place term
deposit or not. All these features are given as input to the
recommended system implemented in this paper. This
implemented classifier model is compared with other
baseline models such as k-Nearest Neighbor (k-NN) [6],
Decision tree classifier (DT) [7], and Multi-layer
perceptron classifier (MLP) [8].
II. RELATED WORK
Several researches have been carried out for
term deposit subscription prediction. This section
discusses some of the studies carried out in this field.
Term deposit investment prediction is performed using
three classification models such as Decision Trees,
Naïve Bayes and Support Vector Machines[1]. The
predictive results acquired from these models are
compared in with respect to ROC and Lift curve
analysis. Support Vector Machine obtained the best
results. An analysis was applied to extract useful
knowledge from this classification model [1]. Using
SPSS Modeler, both classification and clustering models
are established in [2]. Boosted C5.0 model exhibited the
best performance with highest accuracy in terms of
classification. Next, clustering algorithms are applied to
classify clients who have subscribed to a term deposit in
order to discover and understand customers’ behaviours
and characteristics, social and economic context
attributes[2].
Safia Abbas [9] focused on improving the
efficiency of the marketing campaigns. Number of
interfering features were reduced which will help in
predicting the deposit customer retention criteria based
on potential predictive rules. By applying decision tree
(DT) and rough set theory (RST) classification module
predictive results are obtained. This study concludes that
application of feature reduction process, RST obtains a
better summarization to the data set[9]. Another study
[10] constructed logistic regression model by
considering relationship between success and other
factors. The classifier model predicts the success of bank
telemarketing to identify the top consumer set. Some
basic classifier model including Bayes, Support Vector
Machine, Neural Network and Decision Tree are
implemented and comparedin this study. As a result, the
prediction accuracy and the area under ROC curve prove
the logistic regression model outperforms well in
classifying than other models [10].
The concept of lifetime value (LTV) is used by
Moro et al. in [11] to improve the return and to invest
money/assets into bank marketing. Recency, frequency
and monetary value are considered as parameters for this
purpose. The results in [11] are useful for contact
companies with an improved predictive performance.A
comparative study is drawn in [12] among four models
such as logistic regression, decision trees (DT), neural
network (NN) and support vector machine in terms of
two metrics AUC and ALIFT. Concluding study states
that the NN achieved the best results with AUC of 0.8
and ALIFT of 0.7.
Hung et. al. [13]presented term deposit
subscription prediction model using PySpark and its
machine learning frameworks such as Decision Tree,
Random Forest and Gradient Boosting techniques. Their
study concluded accuracy rates of detection and
classification reach 71% and 86% respectively[13].
Application of deep convolutional neural
network is presented to predict whether a given customer
is proper for bank telemarketing or not. Collection of
45,211 phone calls for 30 months is utilised for such
prediction. Prediction results achieve an accuracy of
76.70% of accuracy which outperforms other
conventional classifiers[14].
III. BACKGROUND
Multiple layers of learning-nodes are stacked in
Deep Learning (DL) system for understanding the
features present in the raw input data. During the process
each layer transforms the output obtained from the
previous layer into a representation at a higher and more
abstract level. The depth allows the system to learn
complex features and enables it to draw
inferences[4].DL technique exemplifies the use of neural
network that mimics human brain like operations for
inferring complex problem solving approach. It
recognizes underlying relationships in a set of data with
the provision of necessary adaptation of changing input.
This will generate the best possible result without
altering the output criteria[15]. Neural network
comprised of several neurons. Each of these neurons will
accept necessary parameters and apply some activation
functions in order to produce outputs. Activation
functions [16] are useful to perform diverse
computations and produce outputs within a definite
range. In other words, activation function is a step that
associates input signal into output signal. Among several
types of activation function, sigmoid and relu are two
popular activation functions. A brief description of the
functions are discussed as follows-
 Sigmoid activation function[16]transforms
input data in the range of 0 to 1 and it is shown
in equation (1).
) (1)
 Rectified Linear Unit (ReLu) activation
function [16]is the most successful and widely
used faster activation function. It performs a
threshold operation to each input element where
values less than zero are set to zero whereas the
values greater or equal to zeros kept as intact
and it is shown in equation (2).
(2)

Recurrent neural network (RNN) [4]is a type of
neural network architecture that processes both
sequential and parallel information. Similar operations
like human brain can be simulated by incorporating
memory cells to the neural network. The RNN differs
from traditional neural network in terms of relationship
observed among input and outputs. RNN introduces
cycle in its structure which enables it to memorisepast
input data. Gated Recurrent Unit (GRU) is a variant of
RNN. The RNN has disadvantage of having gradient
vanishing problem which is eliminated by implementing
GRU. The gating units present in GRU can control the
flow of information inside the unit, without considering
separate memory cells. GRU lacks of having memory
cells in it and it has a lesser number of gates which are
activated using current input as well as previous output.
GRU controls the information flow from the previous
activation whilecomputing the new, candidate activation,
but does not independently control the amount of the
candidate activation being added. GRU can converge
faster due to reduction of parameters[17].GRU consists
of update gate and reset gate in its structure. The update
gate determines the amount of past information to be
passed to future processing. The amount of information
to be remembered is determined by reset gate[4].
Convolutional Neural Networks (CNNs) are
also improvement over traditional neural network. CNN
can extract underlying hierarchical features by
discovering the local relationship between nodes.
Convolution operation is exhibited by each neighbour
node in order to capture inherent relationship in adjacent
nodes. Convolutional Layers are one of the components
of CNN. The convolutional layer will determine the
output of neurons of which are connected to local
regions of the input through the calculation of the scalar
product between their weights and the region connected
to the input volume. The layers parameters focus around
the use of learnable kernels[18]. In other words, an input
data and a convolution kernel are subjected to particular
mathematical operation to generate a transformed feature
map. Convolution is often interpreted as a filter, where
the kernel filters the feature map for information of a
certain kind. The convolutional layer performs an
operation called a “convolution“. It is a linear operation
that involves the multiplication of a set of weights with
the input. An array of input data and a two-dimensional
array of weights, called a filter or a kernel, are multiplied
for obtaining results. ReLu activation function[16] is
popularly used in Convolutional layer and is proficient in
most situations. Application of non-linearity applied
after convolution assists in successful simulation[4].
Over-fitting is a problem when a network is
unable to learn effectively from the dataset. In order to
handle the problem, use of dropout layers are
recommended. Dropout layers randomly deactivate a
fraction of the units or connections in a network during
each of the training iterations[19].Incorporating the
dropout layers in the deep model construction will assist
in avoiding over-fitting problem.
While stacking several layers into a single
framework, employing an optimizer is necessary. Adam
is often regarded as one of the popular optimizers. This
optimizer is computationally efficient with lower
memory requirement and also easy to implement. This
algorithm is appropriate for first-order gradient-based
optimization of stochastic objective functions, based on
adaptive estimates of lower-order moments. This
algorithm is pretty well acknowledged due to its
applicability on non-stationary objectives and problems
with very noisy and/or sparse gradients [20].
Configuration of the neural model follows by
execution of training process. An epoch is defined to be
one cycle through which training process is executed
where the dataset is partitioned into smaller subsections.
An iterative process is executed through a couple of
batch size that considers subsections of training dataset
for completing epoch execution[21]. Binary cross
entropy function is used as training criterion for solving
binary classification problem. This function measures
the distance from the true value (which is either 0 or 1)
to the prediction for each of the classes. Class-wise
errors are then averages to obtain the final Loss[22].
IV. PROPOSED METHODOLOGY
The objective of this study is to determine
customer term deposit subscription behaviors in
advance. In this context, supervised classification
algorithms assist in establishing predictive model by
learning and discovering the relationship between a set
of feature variables and a target variable. The feature
variables include dominant reasons such as customer’s
age, job profession, marital status, education
qualification, taken personal loan or not, taken home
loan or not, has credit details or not, contact details,
related details with the last contact of current campaign
in terms of day, month, contact duration, related details
with contact details, number of days passed, outcome of
previous campaign. The above factors are acquired for
identifying customer term deposit subscription, the target
variable of the classification. The framework
implemented in this paper proceeds through following
series of steps.
1. Dataset Used
In order to fulfill the objective of the study,
Portugal bank marketing campaigns results are obtained
from kaggle [23] as a collection of 45211 numbers of
records and each of having 17 attributes. The attributes
infer the related factors that affect campaign results. The
target variable identifies whether a customer place term
deposit or not. Hence a binary classification problem is
addressed in this paper. Table1 provides details of
attributes present in the dataset in terms of types of
attributes and usage of them. Fig1 depicts the
distribution of term deposit subscription tendency in the
dataset. The attribute named as y is kept as the
dependent variable during the classification procedure.

Attribute Name Type of attribute Description Values Present
Age Numeric Customer’s Age 18-95
Job Categorical Customer’s Profession 'management',
'technician',
'entrepreneur', 'blue',
'unknown', 'retired',
'admin', 'services', 'self',
'unemployed',
'housemaid', 'student'
Marital Categorical Marital Status of
Customer
'married', 'single',
'divorced'
education Categorical Education Qualification 'tertiary', 'secondary',
'unknown', 'primary'
Default Categorical Whether the customer has
credit in default
'no', 'yes'
Balance Numeric Balance present in
account
-8019-102127
Housing Categorical Whether the customer has
housing loan or not
'yes', 'no'
Loan Categorical Whether the customer
acquireshousing loan or
not
'no', 'yes'
Contact Categorical contact communication
type
'unknown', 'cellular',
'telephone'
Day Numeric last contact day 1-31
Month Categorical last contact month of year 'may', 'jun', 'jul', 'aug',
'oct', 'nov', 'dec', 'jan',
'feb',
'mar', 'apr', 'sep'
Duration Numeric last contact duration, in
seconds
0-4918
Campaign Numeric number of contacts
performed during this
campaign and for this
client
1-63
Pdays Numeric number of days that
passed by after the client
was last contacted from a
previous campaign
1 - 871
Previous Numeric number of contacts
performed before this
campaign and for this
client
0-275
Poutcome Categorical outcome of the previous
marketing campaign
'unknown', 'failure',
'other', 'success'
Y Numerical Whether client subscribed
a term deposit or not
0,1
Table 1: Detailed description of attributes present in the dataset

Figure 1: Subscription distribution in the dataset
Once the dataset is collected, pre-processing
techniques are applied to obtain cleaned dataset. All the
categorical attributes present in the dataset is encoded
into numeric data. This will be followed by scaling
values of every feature with large set of data points.
Feature scaling will assist the classifier to work using
normalized data with an enhanced efficiency. Large set
of data points are scaled down within the range of 0 to 1
using feature scaling operation. Once this feature scaling
operation is performed, feature vector is fitted to
classifier model as training purpose. The pre-processed
dataset is bi-furcated into training and testing dataset
with the ratio of 7:3. The training and testing dataset is
mainly distinguished by the presence of dependent
variable. The target variable is kept in training dataset
whereas it is eliminated from the testing dataset. The
classifier learns by extracting patterns from the training
dataset during training phase. Later, prediction is
acquired for the testing dataset.
2. Methodology
Classification procedure is applied in this
framework that is applied on the Portugal bank
marketing campaigns results dataset in order to obtain
term deposit subscription prediction in advance. The
proposed methodology uses deep neural network which
is Classification strategy is implemented by designing
hybrid neural network model that assembles
Convolutional and RNN layers under a single platform.
While designing this model it is necessary to fine-tune
hyper-parameters in order to achieve maximized
efficiency. This section describes specification of the
model along with its hyper-parameters.
The deep model contains two 1-dimensional
Convolutional layers with filter size of 256 and 128
respectively. These layers are adjusted with kernel size
of 1. Next, two GRU layers are stacked into this model
with 64 and 32 nodes respectively. These four layers are
followed by dropout layers with dropout rate of 0.2.
Finally, four dense layers are incorporated into the deep
model with 8,4,2,1 numbers of nodes respectively. In
this context, either sigmoid orrelu activation functions
are applied in each of these specified layer. Finally these
aforementioned layers are compiled using adamsolver by
means of 30 epochs and with a batch size of 64.
Adjustment of the hyper-parameters assists the model to
attain best predictive result. The deep neural network
receives a total of 80,089parameters and trains those
parameters for achieving prediction. Components of the
model in terms of layers, shape of output data from each
layers, and number of parameters received in each layers
are described in Table 2. The employed hyper-
parameters while designing the proposed deep model are
summarised in Table 3. The experiment has been
conducted in Windows 10 Home with Intel Core i5-
9300H (9th Gen), 8GB memory, and an NVIDIA
GeForce GTX 1650 GPU.

Layer Type Number of Nodes/
filter size/ Rate
Output Shape Number of received
parameters
Activation
function Used
conv1d_1 (Conv1D) 256 None, 16, 256 512 ReLu
dropout_1
(Dropout)
Dropout Rate-20% None, 16, 256 0 None
conv1d_2 (Conv1D) 128 None, 16, 128 32896 ReLu
dropout_2
(Dropout)
gru_1 (GRU) 64 None, 16, 64 37056 ReLu
dropout_3
(Dropout)
gru_2 (GRU) 32 None, 32 9312 ReLu
dropout_4
(Dropout)
Dropout Rate-20% None, 32 0 None
dense_1 (Dense) 8 None,8 264 None
dense_4 (Dense) 1 None,1 3 Sigmoid
Table 2: Summary of the Stacked Convolutional-GRU model
Number of Epochs 30
Batch Size 64
Loss function Binary Cross-entropy
Optimizer Used Adam
Table 3: Specification of Parameters
Other Baseline Classifier Models
Classification is a supervised machine learning
technique that analyses specified set of features and
identifies data as belonging to a particular class.
Different classification algorithms such as decision trees,
K-nearest neighbour classifier are used to predict the
target class.
K nearest neighbour(K-NN)[6]is often
considered as lazy learner which considers instances
during classification process. It is known as lazy learners
because during training phase it just stores training
samples. This identifies objects based on closest
proximity of training examples in the feature space. The
classifier considers k number of objects as the nearest
object while determining the class. The main challenge
of this classification technique relies on picking up the
suitable value of k [6].
A Decision Tree (DT) [7] is a classifier that
exemplifies the use of tree-like structure. Top-down
learning approach is exhibited by this model. Several
smaller datasets are acquired from the source dataset
using a statistical measure, often in the form of the Gini
index or information gain via Shannon entropy. It gains
knowledge on classification. Each target class is denoted
as a leaf node of DT and non-leaf nodes of DT are used
as a decision node that indicates certain test. The results
of those tests are identified by either of the branches of
that decision node. Traversing from the beginning at the
root this tree and going through it until a leaf node is
reached- is the process of retrieving classification results
from DT[7].
Multi-layer perceptron (MLP) [8] can be used
as supervised classification tool by incorporating
optimized training parameters. This classification
algorithm is inclined to having neural network structure.
MLP is often considered to be a feed-forward artificial
neural network model that associates sets of input data
onto a set of appropriate outputs. For a given problem,
the number of hidden layers in a multilayer perceptron
and the number of nodes in each layer can differ.
Deciding the correct parameters depends on the training
data and the network architecture[8].
These aforementioned classifiers are
implemented in this framework with necessary
parameter tuning. The decision tree classifier
implemented in this paper uses Gini index while
choosing objects from dataset. The nodes of the decision
tree are expanded until all leaves are pure or until all
leaves contain less than minimum number of samples. In
this case, minimum number of samples is assigned a
value as 2. The K-NN classifier gives a promising result
for the value k=4 considering all the evaluating metric.
The MLP classifier is implemented by incorporating
hidden layers sizes 128, 64, 32,16,8 respectively.
The proposed hybrid deep neural network as
well as baseline classifiers including decision tree, k-NN
classifiers, and MLP classifier are implemented and
evaluated in terms of some pre-defined metrics. These
metrics will support the comparison platform while
inferring the best problem-solving approach.

V. PERFORMANCE MEASURE
METRICS
In order to justify performance skill of a model,
it is necessary to employ metrics to estimate the
evaluation. The predictions obtained for the testing data
is verified with actual labelled data. The verification
process requires considering some pre-defined metrics.
For this purpose, following metrics are used to identify
the best relevant problem-solving approach.
1. Accuracy [24] is a metric that detects the ratio
of true predictions over the total number of
instances considered.
2. Mean Squared Error (MSE) [24] is another
evaluating metric that measures absolute
differences between the prediction and actual
observation of the test samples. Non-negative
floating value is produced as MSE. A value
near to 0.0 signifies best predictive model. It
can be defined as follows-
MSE= ( 2
/ N) where Xi is the actual
value and Xi’ is the predicted value.
3. Accuracy does not capture the effect of false
predictions during evaluation. So, to eliminate
this problem, two more metrics known as,
Recall and Precision are taken into
consideration. Precision[24] identifies the ratio
of correct positive results over the number of
positive results predicted by the classifier.
Recall [24]denotes the number of correct
positive results divided by the number
of all relevant samples. F1-Score or F-
measure[24]is a parameter that is concerned for
both recall and precision and it is calculated as
the harmonic mean of precision and recall.
A model that exhibits lower MSE value and higher
accuracy and f1-score result turns out to be the best
problem-solving approach. Mathematically, accuracy,
f1-score can be defined as follows with given True
Positive, True Negative, False Positive, False Negative
as TP, TN, FP, FN respectively.
Accuracy= TP+TN/(TP+FP+TN+TP)
Recall= TP/(TP+FN) Precision= TP/(TP+FP)
F1- Measure or F1-Score= 2* Recall * Precision / (Recall + Precision)
VI. EXPERIMENTAL RESULTS
During training, while fitting the training data
into the stacked Convolutional-GRU classifier, the
training process is evaluated in terms of accuracy as well
as loss. For each epoch, data loss and accuracy is
calculated. The best performing model will show
accuracy to be increased as the number of epochs is
increased. Similarly, the best model will show loss to be
decreased when the number of epochs is increased.
Accuracy and loss obtained for each epoch during
training process of the classifier is shown in Fig 3.
After completion of training process, testing
data is used for acquiring predictions. The prediction
result is evaluated in terms of accuracy, f1-score and
MSE. The evaluated results are shown in Table 4.
Baseline classifiers such as K-NN, Decision Trees, and
MLP are also evaluated with respect to aforementioned
metrics. Table 4 draws comparative study between the
proposed deep model and traditional ML based baseline
classifiers. The comparative study indicates that the
proposed model performs quite well in terms of term
deposit investment possibilities.
Models Performance Measure
Metrics
Accuracy MSE F1-Score
Proposed Model Stacked Convolutional-
GRU Model
89.59% 0.1041 0.896
Baseline
Classifier
Decision Tree Classifier 83.01% 0.17 0.83
MLP Classifier 86.13% 0.14 0.861
K-NN Classifier 88.83% 0.11 0.883
Table 4: Performance Summarization of specified classifiers
Figure 3: Accuracy and loss acquired for each epoch of Stacked Convolutional-GRU model

VII. CONCLUSION
This study applies data mining techniques to
forecast customers’ term deposit subscription behaviours
and comprehend customers’ features to improve the
effectiveness and accuracy of bank marketing. A hybrid
deep neural model is proposed in this paper that
assembles Convolutional layers and RNN based GRU
layers under single platform for determining subscription
behaviours at an early stage. The proposed model
exemplifies fine-tuning of necessary hyper-parameters in
order to maximise efficiency of prediction results.
However, this method is compared with baseline
classifiers such as MLP, k-NN and DT. The comparative
study concludes that implemented method indicates
superior result with an accuracy of 89.59%, f1-score of
0.896and MSE 0.1168. Predictive results shown by the
proposed method assist bank financial sectors to take
informed decision in customer attraction process towards
term deposit subscription.
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