35 research outputs found
ATM Cash demand forecasting in an Indian Bank with chaos and deep learning
This paper proposes to model chaos in the ATM cash withdrawal time series of
a big Indian bank and forecast the withdrawals using deep learning methods. It
also considers the importance of day-of-the-week and includes it as a dummy
exogenous variable. We first modelled the chaos present in the withdrawal time
series by reconstructing the state space of each series using the lag, and
embedding dimension found using an auto-correlation function and Cao's method.
This process converts the uni-variate time series into multi variate time
series. The "day-of-the-week" is converted into seven features with the help of
one-hot encoding. Then these seven features are augmented to the multivariate
time series. For forecasting the future cash withdrawals, using algorithms
namely ARIMA, random forest (RF), support vector regressor (SVR), multi-layer
perceptron (MLP), group method of data handling (GMDH), general regression
neural network (GRNN), long short term memory neural network and 1-dimensional
convolutional neural network. We considered a daily cash withdrawals data set
from an Indian commercial bank. After modelling chaos and adding exogenous
features to the data set, we observed improvements in the forecasting for all
models. Even though the random forest (RF) yielded better Symmetric Mean
Absolute Percentage Error (SMAPE) value, deep learning algorithms, namely LSTM
and 1D CNN, showed similar performance compared to RF, based on t-test.Comment: 20 pages; 6 figures and 3 table
Privacy-Preserving Chaotic Extreme Learning Machine with Fully Homomorphic Encryption
The Machine Learning and Deep Learning Models require a lot of data for the
training process, and in some scenarios, there might be some sensitive data,
such as customer information involved, which the organizations might be
hesitant to outsource for model building. Some of the privacy-preserving
techniques such as Differential Privacy, Homomorphic Encryption, and Secure
Multi-Party Computation can be integrated with different Machine Learning and
Deep Learning algorithms to provide security to the data as well as the model.
In this paper, we propose a Chaotic Extreme Learning Machine and its encrypted
form using Fully Homomorphic Encryption where the weights and biases are
generated using a logistic map instead of uniform distribution. Our proposed
method has performed either better or similar to the Traditional Extreme
Learning Machine on most of the datasets.Comment: 26 pages; 1 Figure; 7 Tables. arXiv admin note: text overlap with
arXiv:2205.1326
FedPNN: One-shot Federated Classification via Evolving Clustering Method and Probabilistic Neural Network hybrid
Protecting data privacy is paramount in the fields such as finance, banking,
and healthcare. Federated Learning (FL) has attracted widespread attention due
to its decentralized, distributed training and the ability to protect the
privacy while obtaining a global shared model. However, FL presents challenges
such as communication overhead, and limited resource capability. This motivated
us to propose a two-stage federated learning approach toward the objective of
privacy protection, which is a first-of-its-kind study as follows: (i) During
the first stage, the synthetic dataset is generated by employing two different
distributions as noise to the vanilla conditional tabular generative
adversarial neural network (CTGAN) resulting in modified CTGAN, and (ii) In the
second stage, the Federated Probabilistic Neural Network (FedPNN) is developed
and employed for building globally shared classification model. We also
employed synthetic dataset metrics to check the quality of the generated
synthetic dataset. Further, we proposed a meta-clustering algorithm whereby the
cluster centers obtained from the clients are clustered at the server for
training the global model. Despite PNN being a one-pass learning classifier,
its complexity depends on the training data size. Therefore, we employed a
modified evolving clustering method (ECM), another one-pass algorithm to
cluster the training data thereby increasing the speed further. Moreover, we
conducted sensitivity analysis by varying Dthr, a hyperparameter of ECM at the
server and client, one at a time. The effectiveness of our approach is
validated on four finance and medical datasets.Comment: 27 pages, 13 figures, 7 table
The Next Wave of CRM Innovation: Implications for Research, Teaching, and Practice
Globalization and customers’ ever-changing needs have created a hyper-competitive market. As a result, customer relationship management (CRM) has become a core topic of interest among both practitioners and academics. Further, over the years, with the advancements in the technology landscape, such as digital technologies, CRM has improved in myriad ways. This paper summarizes a panel discussion on CRM innovations held at the 2016 Pacific Asia Conference on Information Systems (PACIS 2016) in Chiyai, Taiwan. The panel discussed CRM fundamentals and how traditional CRM systems work in organizations. Then, the panel focused on the advancement in technology landscape such as big data, analytics, Internet of things, and artificial intelligence and how such technologies have transformed innovations in the CRM landscape. Finally, the panel highlighted the limitations in the current CRM curricula in the universities and how the curriculum today needs to reflect such advancements to enhance the union between the CRM curricula and the industry needs. Further, this paper provides future research ideas for academia and contributes to research interests on CRM in general
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection
Gaining the trust of customers and providing them empathy are very critical
in the financial domain. Frequent occurrence of fraudulent activities affects
these two factors. Hence, financial organizations and banks must take utmost
care to mitigate them. Among them, ATM fraudulent transaction is a common
problem faced by banks. There following are the critical challenges involved in
fraud datasets: the dataset is highly imbalanced, the fraud pattern is
changing, etc. Owing to the rarity of fraudulent activities, Fraud detection
can be formulated as either a binary classification problem or One class
classification (OCC). In this study, we handled these techniques on an ATM
transactions dataset collected from India. In binary classification, we
investigated the effectiveness of various over-sampling techniques, such as the
Synthetic Minority Oversampling Technique (SMOTE) and its variants, Generative
Adversarial Networks (GAN), to achieve oversampling. Further, we employed
various machine learning techniques viz., Naive Bayes (NB), Logistic Regression
(LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF),
Gradient Boosting Tree (GBT), Multi-layer perceptron (MLP). GBT outperformed
the rest of the models by achieving 0.963 AUC, and DT stands second with 0.958
AUC. DT is the winner if the complexity and interpretability aspects are
considered. Among all the oversampling approaches, SMOTE and its variants were
observed to perform better. In OCC, IForest attained 0.959 CR, and OCSVM
secured second place with 0.947 CR. Further, we incorporated explainable
artificial intelligence (XAI) and causal inference (CI) in the fraud detection
framework and studied it through various analyses.Comment: 34 pages; 21 Figures; 8 Table