Year-end tax strategies for property investors: accelerated depreciation claims, loss harvesting through asset disposals, and cross-border restructuring before new rules take effect.

Annual survey analysing run-off insurance portfolios globally. Covers capital management strategies, regulatory frameworks, and market consolidation trends affecting closed insurance books.

PwC analysis of global financial services M&A trends for 2025, covering digital transformation catalysts, regulatory environment impacts, and strategic consolidation patterns across banking, insurance and asset management sectors.

PwC analyses global real estate M&A trends for 2025, covering deal activity patterns, sector performance, and key factors driving property investment decisions across commercial and residential markets.

Annual PwC survey tracks crypto hedge fund industry evolution, covering assets under management, investment strategies, performance metrics, and regulatory developments across global markets.

Financial services M&A fell 15% by volume and 23% by value in H1 2024. Higher interest rates and regulatory scrutiny create challenging environment. Banks focus on domestic consolidation whilst insurers pursue cross-border deals.

KPMG's risk and regulation resource centre covers regulatory developments across BFSI sectors including banking supervision, insurance regulation, and asset management compliance requirements.

Chain-of-Query uses multi-agent collaboration to help LLMs understand tabular data through SQL generation. Framework tackles structural complexity issues and error propagation problems in existing approaches.

Researchers propose Nash equilibrium approach to allocate systemic capital requirements among banks. Game theory method addresses how to distribute system-wide capital needs to individual institutions rather than just measuring total risk.

CompressionAttack targets LLM-powered agents' prompt compression modules. Adversarial inputs cause semantic drift, altering AI behavior. Identifies new attack surface in cost-optimized systems.

New SPEAR-MM method solves catastrophic forgetting issue in financial LLMs through selective parameter evaluation and model merging. Preserves general reasoning capabilities needed for customer interactions while enabling domain-specific adaptation.

This study offers a cost-benefit analysis for organizations deciding between on-premise deployment of large language models (LLMs) and commercial LLM services. It evaluates hardware requirements, operational expenses, and performance benchmarks of open-source models like Qwen and Llama. The findings help determine the breakeven point for local deployment versus cloud services.

BIS study finds ChatGPT can perform high-level intraday liquidity management in wholesale payment systems. AI agent maintained precautionary buffers, prioritized urgent payments, and optimized settlement speed vs liquidity trade-offs across simulated scenarios.

The FCA and PRA have introduced measures to boost the mutuals sector, such as a new Mutual Societies Development Unit and reduced application times for new societies. These initiatives aim to double the sector's size, affecting credit unions, building societies, and mutual insurers. This is crucial for enhancing financial inclusion and community support.

The study finds that cybercrime legislation significantly enhances banking stability. This effect is more pronounced in countries with stringent penalties and robust enforcement mechanisms. The laws improve funding liquidity and mitigate operational risk, contributing to a secure banking environment. BFSI professionals should advocate for strong cybercrime laws to bolster stability.

Rory Milsom's study benchmarks large language model (LLM) agents for wealth-management tasks, revealing that workflow reliability and autonomy significantly impact performance. The research introduces a benchmark of 12 task-pairs for wealth management assistants, focusing on retrieval, analysis, and communication. It concludes that incorrect model evaluation has hindered effective benchmarking.

This paper explores the monopoly pricing of weather index insurance through a game-theoretic model involving insurers and farmers. It finds that using convolutional neural networks (CNNs) for modeling index payoffs reduces basis risk and increases insurer profits. The study also shows that expanding pricing flexibility leads to higher equilibrium profits.

ARCADIA, a new AI framework, improves causal discovery in corporate bankruptcy analysis by integrating large-language-model reasoning with statistical diagnostics. It outperforms traditional models such as NOTEARS and GOLEM by producing more reliable causal graphs. This advancement is crucial for sectors dealing with high-stakes financial decisions, offering a fully explainable and intervention-ready pipeline.

The study examines how global banks' net worth shocks influence emerging market economies (EMEs). Positive shocks lead to currency appreciation, reduced borrowing costs, and increased capital flows, boosting investment and credit. These effects are robust and vary by firm leverage and currency exposure, highlighting the importance for EME financial stability.