no code implementations • 31 Jul 2023 • Satyam Kumar, Yelleti Vivek, Vadlamani Ravi, Indranil Bose
This paper presents a comprehensive survey of 37 papers published during 1992-2023 and concerning the application of causal inference to banking, finance, and insurance.
no code implementations • 9 Apr 2023 • Polaki Durga Prasad, Yelleti Vivek, Vadlamani Ravi
Protecting data privacy is paramount in the fields such as finance, banking, and healthcare.
no code implementations • 8 Mar 2023 • Yelleti Vivek, Vadlamani Ravi, Abhay Anand Mane, Laveti Ramesh Naidu
In both contexts, RF turned out to be the best model.
no code implementations • 25 Feb 2023 • Pavan Venkata Sainadh Reddy, Yelleti Vivek, Gopi Pranay, Vadlamani Ravi
In this paper, we performed VAE-based adversary sample generation and applied it to various problems related to finance and cybersecurity domain-related problems such as loan default, credit card fraud, and churn modelling, etc., We performed both Evasion and Data-Poison attacks on Logistic Regression (LR) and Decision Tree (DT) models.
no code implementations • 15 Dec 2022 • K. S. N. V. K. Gangadhar, B. Akhil Kumar, Yelleti Vivek, Vadlamani Ravi
The effectiveness of C-VAE is demonstrated on the health insurance fraud and auto insurance datasets.
no code implementations • 19 Nov 2022 • Yelleti Vivek, Vadlamani Ravi, Abhay Anand Mane, Laveti Ramesh Naidu
Owing to the rarity of fraudulent activities, Fraud detection can be formulated as either a binary classification problem or One class classification (OCC).
no code implementations • 7 Sep 2022 • Eduru Harindra Venkatesh, Yelleti Vivek, Vadlamani Ravi, Orsu Shiva Shankar
After the advent of big data, as data gets generated at a brisk pace, it is imperative to analyze it as soon as it is generated owing to the fact that the nature of the data may change dramatically in short time intervals.
no code implementations • 19 Aug 2022 • Satyam Kumar, Vadlamani Ravi
A specific class of algorithms known as counterfactuals may be able to provide causability.
no code implementations • 18 Aug 2022 • Satyam Kumar, Mendhikar Vishal, Vadlamani Ravi
To the best of our knowledge, no work is reported that offers an Explainable Reinforcement Learning (XRL) method for trading financial stocks.
no code implementations • 4 Aug 2022 • Syed Imtiaz Ahamed, Vadlamani Ravi
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.
no code implementations • 18 Jul 2022 • Satyam Kumar, Vadlamani Ravi
Providing feature importance is the most important and popular interpretation technique used in shallow and deep neural networks.
no code implementations • 26 May 2022 • Syed Imtiaz Ahamed, Vadlamani Ravi
The techniques are combined with various Machine Learning models and even Deep Learning Networks to protect the data privacy as well as the identity of the user.
no code implementations • 23 Feb 2022 • Mohammad Arafat Ali Khan, Chandra Bhushan, Vadlamani Ravi, Vangala Sarveswara Rao, Shiva Shankar Orsu
This paper proposes nowcasting of high-frequency financial datasets in real-time with a 5-minute interval using the streaming analytics feature of Apache Spark.
no code implementations • 8 Feb 2022 • Yelleti Vivek, Vadlamani Ravi, P. Radhakrishna
Hence, we embed Logistic and Tent chaotic maps into the BDE and named it as Chaotic Binary Differential Evolution (CBDE).
no code implementations • 27 Jan 2022 • Prateek Kate, Vadlamani Ravi, Akhilesh Gangwar
In the first, we propose an oversampling method to generate synthetic samples of minority class using Generative Adversarial Network (GAN).
no code implementations • 26 Nov 2021 • P. Shanmukh Kali Prasad, Vadlamani Madhav, Ramanuj Lal, Vadlamani Ravi
This paper proposes non-dominated sorting genetic algorithm-II (NSGA-II ) in the context of technical indicator-based stock trading, by finding optimal combinations of technical indicators to generate buy and sell strategies such that the objectives, namely, Sharpe ratio and Maximum Drawdown are maximized and minimized respectively.
no code implementations • 26 Jun 2021 • Yelleti Vivek, Vadlamani Ravi, Pisipati Radhakrishna
Owing to the emergence of large datasets, applying current sequential wrapper-based feature subset selection (FSS) algorithms increases the complexity.
no code implementations • 23 Feb 2021 • Vangala Sarveswararao, Vadlamani Ravi, Sheik Tanveer Ul Huq
We also proposed a 3-stage hybrid, wherein the 3rd stage invokes NSGA-II too in order to solve the problem of constructing PIs from the point prediction obtained in 2nd stage.
no code implementations • 2 Jul 2020 • Akhilesh Kumar Gangwar, Vadlamani Ravi
In this paper, we propose a novel hybrid architecture viz., BGCapsule, which is a Capsule model preceded by an ensemble of Bidirectional Gated Recurrent Units (BiGRU) for several text classification tasks.
no code implementations • 10 May 2020 • Vishal Vyas, Kumar Ravi, Vadlamani Ravi, V. Uma, Srirangaraj Setlur, Venu Govindaraju
For citation analysis, we developed eight datasets comprising citation sentences, which are manually annotated by us into three sentiment polarities viz.
no code implementations • 7 May 2020 • Shaik Tanveer ul Huq, Vadlamani Ravi, Kalyanmoy Deb
In this paper, we propose two variants of a three-objective formulation using a customized non-dominated sorting genetic algorithm III (NSGA-III) to find community structures in a network.
no code implementations • 31 Mar 2020 • Vadlamani Ravi, Vadlamani Madhav
The proposed 2-stage strategy of using them in tandem is beneficial to the decision-makers within a bank who can try to achieve the optimal or near-optimal values of the financial ratios in order to maximize the reliability which is tantamount to safeguarding their bank against solvency or bankruptcy.
no code implementations • 20 Mar 2020 • Shaik Tanveer ul Huq, Vadlamani Ravi
In this paper, two multi-objective optimization frameworks in two variants (i. e., NSGA-III-ARM-V1, NSGA-III-ARM-V2; and MOEAD-ARM-V1, MOEAD-ARM-V2) are proposed to find association rules from transactional datasets.
no code implementations • 7 Nov 2019 • B. Shravan Kumar, Vadlamani Ravi, Rishabh Miglani
Financial forecasting using news articles is an emerging field.
no code implementations • 9 Oct 2019 • Gutha Jaya Krishna, Vadlamani Ravi
In management, business, economics, science, engineering, and research domains, Large Scale Global Optimization (LSGO) plays a predominant and vital role.
no code implementations • 23 May 2019 • Rohit Gavval, Vadlamani Ravi, Kalavala Revanth Harshal, Akhilesh Gangwar, Kumar Ravi
With the widespread use of social media, companies now have access to a wealth of customer feedback data which has valuable applications to Customer Relationship Management (CRM).