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p-Index From 2021 - 2026
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This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Central Banking Law and Institutions
Mohd Shamim Ansari
Aligarh Muslim University
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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A novel evolutionary optimization algorithm based solution approach for portfolio selection problem Mohammad Shahid; Mohd Shamim; Zubair Ashraf; Mohd Shamim Ansari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp843-850

Abstract

The portfolio selection problem is one of the most common problems which drawn the attention of experts of the field in recent decades. The mean variance portfolio optimization aims to minimize variance (risk) and maximize the expected return. In case of linear constraints, the problem can be solved by variants of Markowitz. But many constraints such as cardinality, and transaction cost, make the problem so vital that conventional techniques are not good enough in giving efficient solutions. Stochastic fractal search (SFS) is a strong population based meta-heuristic approach that has derived from evolutionary computation (EC). In this paper, a novel portfolio selection model using SFS based optimization approach has been proposed to maximize Sharpe ratio. SFS is an evolutionary approach. This algorithm models the natural growth process using fractal theory. Performance evaluation has been conducted to determine the effectiveness of the model by making comparison with other state of art models such as genetic algorithm (GA) and simulated annealing (SA) on same objective and environment. The real datasets of the Bombay stock exchange (BSE) Sensex of Indian stock exchange have been taken in the study. Study reveals the superior performance of the SFS than GA and SA.
AI in India’s Financial Sector: Navigating the Regulatory Landscape Mohammed Afzal; Maryam Meraj; Mohd Shamim Ansari; Mohammed Afzal; Maryam Meraj; Mohd Shamim Ansari
Journal of Central Banking Law and Institutions Vol. 5 No. 2 (2026)
Publisher : Bank Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21098/jcli.v5i2.419

Abstract

Artificial Intelligence (AI) integration in India’s financial sector offers transformative potential but poses challenges like algorithmic bias, data privacy risks, and regulatory fragmentation. This study employed both one-on-one interviews and surveys with various stakeholders in the financial services sector to analyse India’s AI governance framework through expert interviews and a comparative policy analysis of global models (the EU’s risk-based AI Act and the US sector-specific guidelines). Findings reveal gaps in accountability, transparency, and enforcement mechanisms, particularly for high-risk applications like credit scoring. This study proposes a hybrid regulatory model that combines binding rules for high-risk AI systems (e.g., fraud detection) with co-regulation for low-risk tools, emphasising scientific risk assessment, consumer grievance mechanisms, and iterative policymaking. While leveraging India’s existing financial laws (e.g., Reserve Bank of India guidelines), we recommend AI-specific updates to address explainability, bias audits, and systemic risk monitoring. However, this study is limited by its reliance on publicly available regulatory documents and expert interviews, and by its focus on the Indian context, which may overlook cross-border AI governance challenges. Stakeholder collaboration and phased implementation are critical to balancing innovation with ethical safeguards in India’s evolving digital economy.
Co-Authors Maryam Meraj Maryam Meraj Mohammad Shahid Mohammed Afzal Mohammed Afzal Mohd Shamim Mohd Shamim Ansari Zubair Ashraf