Volume 13 | Issue 11 |

 Abstract

Carbon Footprint Optimization Optimize AI-powered Carbon footprint prediction for Sustainable Logistics is a project that is expected to predict and optimize the carbon emission at different points in the supply chain. The system measures carbon footprint using machine learning models including the Random Forest, LSTM and XGBoost, and GRU applications that offer precise predictions of carbon footprint. In the project, generative AI is integrated to produce summaries, offer actionable sustainability information, and possible ESG risk hotspots. The dataset captures the factors like procurement, energy usage, modes of transportation and external factors like weather, which contribute towards the emissions. It is an HTML, CSS, JavaScript, Python (Flask), and hosted on Google Cloud Platform (GCP) platform which provides an easy to use interface with modules such as Home, Register, Login, dashboard and Logout. Some of the dashboard features include predictions, SHAP plot, and ESG insights, which help organizations to reduce the environmental impact. This system is aimed at facilitating the decision-making process and ensuring sustainability through areas of the improvement of emissions management. The suggested generative AI will complement the entire system with proposals on how to streamline the workings of the system, minimize emissions, and increase the sustainability of the supply chain.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Carbon Footprint, Machine Learning, Sustainability, ESG, Emissions Prediction, Generative AI, Supply Chain, Optimization, Random Forest, LSTM, XGBoost, GRU, Flask.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Fintech driven financial services in India have created a payments environment that is instant, interoperable, API based, and deeply embedded in everyday economic activity. Unified Payments Interface volumes, NPCI rails for IMPS, AePS, FASTag, and QR based collections, together with smartphone penetration, have produced a payment layer in which value moves at a speed that legacy banking controls did not anticipate. This speed has carried a parallel surge in frauds and techno economic crimes such as social engineering-based UPI authorisations, card not present misuse routed through merchant accounts, identity theft to open mule accounts, pig butchering through unregistered apps, unauthorised digital lending with coercive recovery, and cross border laundering of virtual digital assets through exchanges that fall outside Indian supervision. RBI, MeitY, FIU IND, ED, CERT In and NPCI have responded with overlapping standards, for instance the 2017 customer liability circular, the 2019 turnaround time framework, the 2022 CERT In six hour reporting requirement, the 2023 and 2024 amendments to the IT Intermediary Rules, the 2022 RBI digital lending guidelines, and the DPDP Act 2023 obligations for data fiduciaries including financial sector entities, yet offenders continue to exploit jurisdictional gaps, inconsistent attribution of liability, long investigation cycles under the BNSS 2023, and the absence of a unified fintech crime code. The problem is aggravated by the emergence of VDA service providers that came under PMLA only in March 2023 and that continue to receive show cause and penalty actions for non-compliance in 2024 and 2025, which confirms that AML controls have not travelled at the same pace as fintech innovation in India. A critical analysis of these laws shows that the legal tools exist, from "Section 66C" and "Section 66D of the Information Technology Act, 2000" to "Section 43A" civil compensation, from "Section 13 of the Prevention of Money Laundering Act, 2002" to "Section 318 of the Bharatiya Nyaya Sanhita, 2023", but they are fragmented across regulators, triggered on different thresholds, and often written for a pre-UPI environment. The present legal research study therefore argues for a tighter articulation of fintech crime categories, a harmonised attribution of loss and restitution across RBI and NPCI rails, stronger data protection overlays for fintech lenders, and a policing procedure that preserves electronic evidence in a manner that meets BSA and BNSS requirements for trial. It also points toward the growing role of self-regulatory organisations in fintech under the RBI's 2024 framework as a bridge between rule writing and day to day market behaviour, especially for merchant monitoring, LSP governance, and API security.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fintech crime; digital payments; UPI fraud; PMLA; IT Act; DPDP Act; digital lending; payment aggregators; intermediary liability; electronic evidence

  License

Creative Commons Attribution 4.0 and The Open Definition