NARENDRA RYALI

RESEARCH, TEACHING, or OTHER INTERESTS

Finance, Economics, Econometrics and Finance, Accounting, Business and International Management

Scopus Publications

AI-Augmented finance for improving risk assessment and portfolio management
Narendra Ryali, Rajani Kandregulaz, Sambhana Srilakshmi, Thulasi Bikku
Connecting Intelligence Trends in Computation and Data Communication, 2026
Traditional portfolio management methods often struggle to capture nonlinear market dynamics and tail risks, necessitating AI-augmented approaches for improved risk assessment. The paper aims to propose a new hybrid model combining feature extraction through Transformers, the use of generative adversarial network (GAN) to generate scenarios, and Black-Litterman portfolio optimization model to improve risk estimation and portfolio optimization. The framework was tested against CAPM, Markowitz mean-variance optimization, LSTM-based allocation, and conventional GAN- Black-Litterman models using the Multi-Indicator Market and Investment Corpus (MiMIC) dataset (20002023), which captures a wide range of macro-financial indicators and market cycles. The results show that the suggested model consistently outperforms the benchmarks, lowering value-at-risk, conditional VaR, and maximum drawdowns while obtaining the greatest Sortino (0.78) and Sharpe (0.85) ratios. Rolling-window back testing also reveals the solidity of the framework under stable and crisis periods, with smoother portfolio weight paths and greater stability indices, reducing rebalancing expenses. The significance of data-driven embeddings and artificial stress scenarios in capturing nonlinear relationships and reducing tail risks is highlighted in discussion. The results demonstrate that Transformer-GAN Black Litterman is a dynamic, data-enhanced tool that institutional investors can use to gain significant, risk-adjusted returns.
DevOps and Secure Cloud-Native Architectures for Finance
Sourabh Sanghi, Sunil Sudhakaran, Vamsi Krishna Koganti, Narendra Ryali
2025 International Conference on Sustainability Innovation and Technology Icsit 2025, 2025
With the ever-evolving and changing nature of financial services in the era of digital transformation, the concept of operational resilience, regulatory compliance, and innovation is highly important, which is achieved through secure cloud-native architectures and agility. This guide offers an end-to-end schema to build systems in the perspective of a financial institution - using DevSecOps, policy-as-code, Zero Trust, and infrastructure-as-code frameworks to build scalable, secure, and compliant systems. The revisit of ten historic research contributions, as well as the offering of a multi-layered model, fulfills the distinctive requirements and objectives of the financial IT-data sovereignty, rapid deployment, and high throughput. The results of the experiments show significant changes in frequency of deployments, risk avoidance and adherence to policies. The model enables trusted and cloud-native innovation in a highly regulated and data-heavy business industry.
Advanced Risk And Performance Management In The Financial Sector: A Deep Learning Approach
Narendra Ryali, S. Nagaraian, Indraganti Sai Sushma Sri Jayalakshmi, Singarmsetti Sri Sai Yaswanth, Pulluri Prudhvi Krishna, Vadlapudi Dinesh Jagannadh
2024 2nd International Conference on Disruptive Technologies Icdt 2024, 2024
The dynamic nature of the financial business, effective risk and performance management must take a proactive approach. Through the application of tactics that are based on deep learning, this study offers a cutting-edge approach to solving growing problems. Traditional financial models typically fall behind the current state of affairs not only because of the intricate nature of the global financial ecosystem but also because of the rapid rate of change. This research investigates how deep learning can be applied to improve financial risk analysis and forecasting in the hopes of helping to close the gap. Our investigation makes use of a robust deep learning architecture, which consists of neural networks and other complex algorithms, in order to sort through vast amounts of information. The methodology places a strong emphasis on real-time data processing, which enables the model to react quickly to developing trends in the market. We evaluate the accuracy of our method by subjecting it to stringent back testing and validation in order to see how well it catches a variety of financial trends. The results reveal a quantum leap forward in comparison to more conventional models in terms of the capability to identify hazards and predict outcomes. Due to the improved accuracy of the deep learning approach in recognizing small market movements and outliers, financial institutions can benefit from a more solid basis for decision-making, which in turn can lead to greater profits.
A Business Strategy for Insurance Companies Using Deep Learning for Risk Assessment and Mitigation
Narendra Ryali, V. Manimegalai, Achanti Nikkhel, Pendem Geetha Nadh, Yaswanth Chowdary Bandaru, Palakonda Venkat Sai
2024 2nd International Conference on Disruptive Technologies Icdt 2024, 2024
An explanation of the model's projections that is straightforward and easy to understand is beneficial to stakeholders, financial institutions, and regulators. Several distinct kinds of tree-based classifiers, including deep neural networks (DNNs), were pitted against one another in this research project. This study's objectives were to (1) classify insurance risk based on historical data and (2) give the appropriate model for risk assessment in order to enhance the risk assessment capabilities of life insurance companies through the application of predictive analytics. The objective was to implement strategies that would simplify machine learning model comprehension for non-experts. The DNN classifier performed the best when compared to other classifiers, achieving an AVC value of 0.86 and an F1-score of 0.56 on the validation set.
Stage by stage E- Ecommerce market database analysis by using machine learning models
Narendra Ryali, Nikita Manne, A Ravisankar, Mano Ashish Tripathi, Ravindra Tripathi, M Venkata Naresh
Eai Endorsed Transactions on Internet of Things, 2024
In the recent era, advertising strategies are far more sophisticated than those of their predecessors. In marketing, business contacts are essential for online transactions. For that, communication needs to develop a database; this database marketing is also one of the best techniques to enhance the business and analyze the market strategies. Businesses may improve consumer experiences, streamline supply chains, and generate more income by analyzing E-Commerce market datasets using machine learning models. In the ever-changing and fiercely competitive world of e-commerce, the multi-stage strategy guarantees a thorough and efficient use of machine learning. Analyzing the database can help to understand the user's or industry's current requirements. Machine Learning models are developed to support the marketing sector. This machine learning model can efficiently operate or analyze e-commerce in different stages, i.e., systematic setup, status analysis, and model development with the implementation process. Using these models, it is possible to analyze the marketing database and create new marketing strategies for distributing marketing objects, the percentage of marketing channels, and the composition of marketing approaches based on the analysis of the marketing database. It underpins marketing theory, data collection, processing, and positive and negative control samples. It is suggested that e-commerce primarily adopt the database marketing method of the model prediction. This is done by substituting the predicted sample into the model for testing. The issue of unequal marketing item distribution may be resolved by machine learning algorithms on the one hand, and prospective customer loss can be efficiently avoided on the other. Also, a proposal for an application approach that enhances the effectiveness of existing database marketing techniques and supports model prediction is made.
Ethical business marketing: The practices to compete successfully in business
Pradeep Reddy K., Venkateswarlu Chandu, Narendra Ryali, Amala Gangula, Debesh Mishra, Swagatika Mishra
Data Driven Approaches for Effective Managerial Decision Making, 2023
Organizations use a variety of strategies to acquire a competitive advantage in today's competitive environment. The question is whether these behaviours are consistent with our organization's beliefs and ideals. Ethical activities are thus the solution. Managers or the top management now have a moral obligation to embrace these methods, thus businesses must incorporate social responsibility into every marketing choice they make. This study emphasizes the significance of employing ethical marketing methods to achieve a competitive advantage over competitors. The significance of manufacturers acting in a socially responsible manner is also highlighted in the study.

RECENT SCHOLAR PUBLICATIONS

Ethical Business Marketing: The Practices to Compete Successfully in Business
P Reddy, V Chandu, N Ryali, A Gangula, D Mishra, S Mishra
Data-Driven Approaches for Effective Managerial Decision Making, 259-276 , 2023
2023
Citations: 1
Algorithmic Trading-A Conceptual Acquaintance
N Ryali
India’s Financial Markets – Emerging Trends , 2017
2017
Average Spread to Working Funds Ratio-A Measure of Profitability in Banks
P Veni P, N Ryali
“KAAV International Journal of Economics, Commerce & Business Management 4 … , 2017
2017
Role of Asset Liability Committee (ALCO) In Commercial Banks
N Ryali
GE – International Journal of Management Research 2 (1), 14-21 , 2016
2016
Risk Management in Banks – A Basel Norms Perspective
NR Prof. P. Veni
International Journal of Innovation Technology and Management 2 (I), 27 - 30 , 2016
2016

MOST CITED SCHOLAR PUBLICATIONS

Ethical Business Marketing: The Practices to Compete Successfully in Business
P Reddy, V Chandu, N Ryali, A Gangula, D Mishra, S Mishra
Data-Driven Approaches for Effective Managerial Decision Making, 259-276 , 2023
2023
Citations: 1
Algorithmic Trading-A Conceptual Acquaintance
N Ryali
India’s Financial Markets – Emerging Trends , 2017
2017
Average Spread to Working Funds Ratio-A Measure of Profitability in Banks
P Veni P, N Ryali
“KAAV International Journal of Economics, Commerce & Business Management 4 … , 2017
2017
Role of Asset Liability Committee (ALCO) In Commercial Banks
N Ryali
GE – International Journal of Management Research 2 (1), 14-21 , 2016
2016
Risk Management in Banks – A Basel Norms Perspective
NR Prof. P. Veni
International Journal of Innovation Technology and Management 2 (I), 27 - 30 , 2016
2016