Collaborating Authors

How Machine Learning Helps in Financial Fraud Detection?


The financial services sector is undergoing digital transformation, and the driving force behind it is machine learning (ML). ML provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. As the finance sector operates on tons of personal data and billions of critical transactions every second, it becomes especially vulnerable to fraudulent activities. Scammers are always seeking to crack the servers to get valuable data for blackmailing. According to PwC's Global Economic Crime and Fraud Survey 2020, respondents reported losses of a whopping $42 billion over the past 24 months due to fraudulent activities.

Walker Appoints Risch to Head Financial Institutions

U.S. News

Risch was a lobbyist for the bankers group for four and a half years before joining the state Department of Financial Institutions in February 2015. He also previously worked as an aide to state Sen. Alberta Darling and former Sen. Cathy Stepp, who is now secretary of the Department of Natural Resources.

La veille de la cybersécurité


When Bitcoin was first launched in 2009, financial institutions were spooked and were quite skeptical about its value and performance. Financial institutions like global banks kept warning initial cryptos investors regarding the fact that they are a new and unregulated asset class. One significant reason banks and other traditional financial institutions may have stayed away from cryptocurrencies and blockchain technology for so long is that they are a massive threat to them. In centralized financial systems, money always goes through the banks. But since cryptocurrencies provide a new, efficient and decentralized method of payments and transactions, most individuals were getting attracted towards investing in these digital currencies. But despite the many arguments portrayed by the centralized financial institutions in the beginning, there are currently many banks that have invested in cryptocurrencies.

Towards Using Rule-Based Multi-agent System for the Early Detection of Adverse Drug Reactions

AAAI Conferences

Adverse Drug Reactions (ADRs) represent troublesome and potentially fatal side effects of medication treatment. To address the burden induced by ADRs, a preventive approach is necessary whereby clinicians are provided with new data-driven decision-support systems to foresee the factors leading to ADRs and plan precautionary activities effectively. We develop a multi-agent system which monitors the factors leading to the onset of ADRs using information found in the patient records in a hospital setting. The system uses a fuzzy rule-based reasoning engine utilising decision rules developed by clinicians. We evaluate the ability of the framework to identify the cause of ADRs from patient records in a case study involving records of metal health patients. Our work is the first preventive agent-based aid tool.

How artificial intelligence makes financial services institutions more efficient


The financial landscape has been rapidly evolving with the rise of financial technology (fintech) companies and startups that are more agile and technologically advanced. This has led financial services institutions (FSIs) to revise their business models and evaluate how they can integrate technology into their operations. Robotic process automation (RPA) is no longer a foreign term in the financial field. Pairing RPA with artificial intelligence (AI) creates intelligent process automation (IPA) that works as a catalyst in digital transformation in FSIs. Like many other industries, the financial field is heavily reliant on documents and legacy systems.