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alda_mobile_money_transaction_fraud_detection

Detecting a fraudulent mobile money transaction is the focus of our work. As mobile transactions continue to increase, online fraud detection continues to become a bigger issue. Although fraud via smartphones is increasing at a faster pace than general PC/laptop based fraud, smartphones have the potential to become as secure a channel as the web through the use of advanced encryption and authentication technologies. By paying close attention to red flags and suspicious activities, you can avoid merchant services fraud. According to the 2018 Global fraud report, it is evident that out of the digital market place consumers 91% of customers use smartphone out of which 88% use for personal banking and it has been noted that 72% cite fraud as a growing concern over the past twelve months and 63% report higher level of fraudulent losses over that same period. One such activity is cited in the rise of Synthetic identities. Synthetic identities come from accounts not held by actual individuals, but by fabricated identities created to perpetuate fraud. These identities are created by combining the credentials and information of a mixed set of individuals to create a completely new ID. Criminals use this kind of technique to commit frauds in the area of healthcare, utility services and taxes. The research in the area of combatting such kind of frauds, motivates us to find a robust system to detect fraudulent transactions. Smartphones have been an easy target for fraudsters as it lacks the security level that other mobile devices have. Fraudsters know that it is generally easier to take over an account by phishing, spear phishing (targeting an individual) or Smishing (phishing via a mobile device), than to open a new account using a real or ‘synthetic’ identity, which is why the risk of account takeover is one of the most alarming trends in fraud.

awesome-graph-classification

A collection of important graph embedding, classification and representation learning papers with implementations.

continual-transformers

Official Pytorch Implementation for "Continual Transformers: Redundancy-Free Attention for Online Inference"

evolvegcn

EvolveGCN Evolving Graph Convolutional Networks for Dynamic Graphs

fisher1991.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

generating-neural-networks-with-neural-networks

Code that goes along with paper http://arxiv.org/abs/1801.01952

gpfl

Graph Path Feature Learning (GPFL) - An Inductive Rule Learner for Knowledge Graph Completion

hypercl

Continual Learning with Hypernetworks. A continual learning approach that has the flexibility to learn a dedicated set of parameters, fine-tuned for every task, that doesn't require an increase in the number of trainable weights and is robust against catastrophic forgetting.

hypercrl

Code for the paper "Continual Model-Based Reinforcement Learning with Hypernetworks"

meta-add

metadistil

Code for ACL 2022 paper "BERT Learns to Teach: Knowledge Distillation with Meta Learning".

ontozsl

orsvr

an online robust support vector regression

posterior_replay_cl

Continual learning of task-specific approximations of the parameter posterior distribution via a shared hypernetwork.

sionna

Sionna: An Open-Source Library for Next-Generation Physical Layer Research

smallfisher

sublime

A PyTorch implementation of "Towards Unsupervised Deep Graph Structure Learning", WWW-22

tcyb-2020

zero-shot-knowledge-graph-relational-learning

Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs