Passages from papers I've been reading. Highlighted, underlined, argued with. Not summaries — just the lines that made me stop.
In practice, effective time series forecasting rarely follows a single-pass procedure. Instead, experienced practitioners treat forecasting as a sequential decision process. They examine historical patterns and contextual information, identify informative features, select forecasting models, and reason over intermediate results to assess forecast reliability. As new contextual evidence emerges, predictions are often revised, highlighting that high-quality forecasting involves a series of interdependent decisions rather than a one-shot model inference.
→ this is literally just describing what analysts do. why has nobody built this beforeLLMs pretrained on internet-scale text corpora have likely been exposed to financial news, analyst reports, and market commentary from periods that overlap with academic test sets. This creates a form of data leakage that is distinct from look-ahead bias and considerably harder to detect. The model may pattern-match on absorbed associations between companies, sectors, and outcomes — associations that constitute implicit knowledge of the test period — without any single piece of future information being directly accessible. Reported directional accuracy figures may be substantially inflated as a result.
AI-powered solutions reduce false positives by 90–95%, automate labor-intensive compliance tasks, and detect sophisticated money laundering patterns in real time. Financial institutions using AI for AML achieve faster detection, lower compliance costs (from $180+ billion annually), and better regulatory outcomes.
← vendor report so grain of salt — but the direction is rightStandard AI-assisted forecasting treats the market as an exogenous system. Reflexivity theory holds otherwise: prices shape fundamentals, and every forecaster is a participative agent in the loop it analyzes. We evaluate three frontier models — GPT-5, Claude Sonnet 4.6, and Gemini 3 Pro — under four accumulating zero-shot conditions across two historically distinct episodes: the dot-com bubble (1996–2001) and the global financial crisis (2004–2009).
→ Soros in a prompt. of course someone did thisThe system constructs dynamic transaction graphs, extracts structural and contextual features, and classifies suspicious behavior using a graph neural network. A retrieval-augmented generation module generates natural language explanations aligned with regulatory clauses for each flagged transaction. Experiments conducted on a simulated stream of financial data show that the proposed method achieves superior results, with 98.2% F1-score, 97.8% precision, and 97.0% recall.
← simulated stream. always simulated.Most banks are stuck in what the industry has come to call "pilot purgatory" — running dozens of isolated experiments that never scale. The conventional wisdom says the only way out is "rip and replace" transformation: tear out the legacy core, rebuild from scratch, accept 18-month procurement cycles and eight-figure budgets. But this narrative is both paralyzing and wrong. Banks that cannot reason and act in real time across the entire customer journey will not merely fall behind — they risk becoming operationally irrelevant.
→ the rip-and-replace story is used to justify inaction. most useful compliance AI doesn't need greenfield infraOnly about one-third of organizations report maturity levels of three or higher in strategy, governance, and agentic AI governance. This imbalance suggests that while technical and risk management capabilities are advancing, organizational alignment and oversight structures are struggling to keep pace with the rapid expansion of AI use. Security and risk concerns are the top barrier to scaling agentic AI. Inaccuracy and cybersecurity remain the most frequently cited AI risks as adoption expands.
← deploying models faster than building governance. that ends badly in a regulated industry.Global AML fines hit $10.4 billion in 2024, surpassing previous records set in 2023. Enforcement is projected to exceed $15 billion in 2026. Norton Rose Fulbright's Boon predicts focus on AI misuse in laundering. Boon states: "Regulators will demand explainable AI in AML systems."
→ $15B in fines and the industry is running rules written in 2003. the business case writes itself.Most institutions still run separate stacks and teams for fraud, AML and sanctions. Fraud and sanctions screening typically happen pre-transaction while AML often triggers post-event. Tools remain predominantly rule-based: banks must pre-define scenarios, which leads to poor detection of emerging patterns and a constant trade-off between missing risk (false negatives) and overwhelming operations teams (false positives).
← three separate systems, three separate teams. criminals operate across all three simultaneously.Building a compliant AI governance program is not a technology project. It is an organizational transformation. The most important thing to understand about AI regulation in financial services is that it is simultaneously more complex and more imminent than most firms appreciate. The EU AI Act classifies AI systems used for credit scoring as "high risk" and introduces additional safeguards — with full application expected by August 2026.
→ "not a technology project" is doing a lot of work here. most firms are treating it as one.