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In this digital era, a huge amount of money had been laundered via digital frauds, which mainly occur in the timeframe of electronic payment transaction made by first-time credit/debit card users. Currently, Finance organizations are facing several fraud attempts and it likely happens due to the current infrastructure, which only has an older database.. The current infrastructure diminishes the working environment of any finance organization sector with frequent fraud attempts. In this perspective, the roposed research article provides an overview for the development of an automated prevention system for any finance organization to protect it from any fraudulent attacks. The proposed automated case management system is used to monitor the expenses of the behavior study of users by avoiding the undesirable contact. The proposed research work develops a new management procedure to prevent the occurrence of electronic fraud in any finance organization. The existing procedure can predict digital fraud with an old updated database. This creates disaster and destructive analysis of the finance segment in their procedure. The cyber fraud phenomenon prediction is used to predict the fraud attempt with content-based analysis. The lack of resources is one of the enormous challenges in the digital fraud identification domain. The proposed scheme addresses to integrate all safety techniques to safeguard the stakeholders and finance institutions from cyber-attacks.

Gradient-based meta-learning (GBML) with deep neural nets (DNNs) has become a popular approach for few-shot learning. However, due to the non-convexity of DNNs and the complex bi-level optimization in GBML, the theoretical properties of GBML with DNNs remain largely unknown. In this paper, we first develop a novel theoretical analysis to answer the following questions: Does GBML with DNNs have global convergence guarantees? We provide a positive answer to this question by proving that GBML with over-parameterized DNNs is guaranteed to converge to global optima at a linear rate. The second question we aim to address is: How does GBML achieve fast adaption to new tasks with experience on past similar tasks? To answer it, we prove that GBML is equivalent to a functional gradient descent operation that explicitly propagates experience from the past tasks to new ones. Finally, inspired by our theoretical analysis, we develop a new kernelbased meta-learning approach. We show that the proposed approach outperforms GBML with standard DNNs on the Omniglot dataset when the number of past tasks for meta-training is small. The code is available at https://github.com/ AI-secure/Meta-Neural-Kernel .Preprint. Under review.