In decentralized financial systems, robust and efficient Federated Learning (FL) is promising to handle diverse client environments and ensure resilience to systemic risks. We propose Federated Risk-Aware Learning with Central Sensitivity Estimation (FRAL-CSE), an innovative FL framework designed to enhance scalability, stability, and robustness in collaborative financial decision-making. The framework's core innovation lies in a central acceleration mechanism, guided by a quadratic sensitivity-based approximation of global model dynamics. By leveraging local sensitivity information derived from robust risk measurements, FRAL-CSE performs a curvature-informed global update that efficiently incorporates second-order information without requiring repeated local re-evaluations, thereby enhancing training efficiency and improving optimization stability. Additionally, distortion risk measures are embedded into the training objectives to capture tail risks and ensure robustness against extreme scenarios. Extensive experiments validate the effectiveness of FRAL-CSE in accelerating convergence and improving resilience across heterogeneous datasets compared to state-of-the-art baselines.

All Indian Reprints of O'Reilly are printed in Grayscale Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python.

Credit is the oldest form of finance. Identifying, measuring, and managing credit risk is therefore one of the oldest financial problems that mankind has ever encountered. The popularity of credit derivatives such as CDS since the US sub-prime crisis has created an urgent need for investors to accurately assess and quantify credit risk. Deep learning, or neural networks, could provide an effective solution when dealing with complex finance models. Its strong predictive power and broad application have raised significant attraction among researchers in the field of financial risk management.

Many applications, or use "cases," of AI and machine learning already exist. The adoption of these use cases has been driven by both supply factors, such as technological advances and the availability of financial sector data and infrastructure, and by demand factors, such as profitability needs, competition with other firms, and the demands of financial regulation. As a subbranch of financial modeling, financial risk management has been evolving with the adoption of AI in parallel with its ever-growing role in the financial decision-making process. In his celebrated book, Bostrom (2014) denotes that there are two important revolutions in the history of mankind: the Agricultural Revolution and the Industrial Revolution. These two revolutions have had such a profound impact that any third revolution of similar magnitude would double the size of the world economy in two weeks. Even more strikingly, if the third revolution were accomplished by AI, the impact would be way more profound.

Artificial intelligence (AI) and other disruptive technologies came under the spotlight in a series of thought-provoking sessions at our annual Financial & Risk Summit in Toronto. This year's event -- "Revving the Innovation Engine" -- brought together thought leaders and practitioners from across financial markets and risk and compliance communities for a full-day series of interactive discussions. It was also an opportunity to introduce members of our #TRFinRiskCanada40 list, which showcases Canada's top social media voices in finance, innovation, and risk. One of the highlights of the summit was the Reuters Newsmaker interview with Geoffrey Hinton, a vice-president with Alphabet Inc's Google, who is often referred to as "the godfather of deep learning." He discussed how artificial intelligence is being used today by internet companies such as Google and Facebook, and also in a wide variety of other industries, from financial services and healthcare, to automotive and manufacturing.

Financial institutions need to continually weigh the risks of their transactions, and they determine their risk level through credit scoring. Leading up to the 2008-09 financial crisis, almost all large banks used credit scoring models based on statistical theories; that crisis, largely brought about by underestimating risk, proved the need for better accuracy in their scoring. The combination of increased requirements and the development of advanced new technologies has given rise to a new era: credit scoring using machine learning. Machine Learning for IBM z/OS gives organizations the ability to quickly ingest and transform data. They can now create, deploy, and manage high quality self-learning behavioral models, using large corporate data sets residing on IBM Z.