In 2026, artificial intelligence is profoundly reshaping the underlying logic of the global financial industry. From traditional credit approval to high-frequency quantitative trading, from anti fraud detection to intelligent investment advisory, AI has penetrated into every aspect of financial services. According to the 2025 report by the Bank for International Settlements, over 65% of central and commercial banks worldwide have incorporated machine learning models into their daily risk management systems, which has doubled from three years ago.
Intelligent risk control is one of the most mature applications of AI in the financial field. Traditional risk control models rely on historical data and linear regression, making it difficult to capture complex nonlinear risk associations. And risk control systems based on deep learning can simultaneously analyze thousands of dimensions of features - from transaction flow, social behavior to device fingerprints - to generate real-time dynamic risk scores. Ant Group's intelligent risk control platform "Ant Shield" has been able to achieve millisecond level transaction risk assessment, reducing fraud loss rates to below one in ten thousand. The AI risk control system launched by Industrial and Commercial Bank of China in 2025 has reduced the bad debt rate by 37% and increased approval efficiency by 5 times in credit card application review.
The field of quantitative trading is also undergoing a paradigm shift driven by AI. Traditional statistical arbitrage and factor models are being replaced by reinforcement learning and Transformer architectures. Top hedge funds such as Renaissance Technology and Two Sigma have already made deep learning their core strategic tool. In 2025, JPMorgan Chase launched LOXM 2.0, a trading assistance system based on a big language model that can understand natural language instructions and autonomously execute complex multi asset arbitrage strategies. Its annualized return rate in simulated environments is 12 percentage points higher than traditional quantitative models.
AI robo advisors are also accelerating their popularity. As of early 2026, the global scale of assets managed by robo advisors has exceeded $4 trillion. Top platforms such as Betterment and Wealthfront use deep learning models to dynamically evaluate users' risk preferences and adjust asset allocation portfolios in real-time based on market changes. In China, China Merchants Bank's "Capricorn Intelligent Investment" has served over 20 million users, and its AI model helped users achieve an average positive return of 8.3% in the A-share market volatility in 2025, far exceeding the performance of the Shanghai Composite Index during the same period.
However, the widespread application of AI in the financial sector has also brought new challenges. Model interpretability, algorithmic bias, and systemic risk are the three major focuses of regulatory attention. The EU's Artificial Intelligence Act categorizes AI applications in the financial sector as high-risk, requiring all credit scoring and insurance pricing models to pass interpretability assessments. The People's Bank of China also released the "Management Measures for the Application of Artificial Intelligence in the Financial Sector (Draft for Comments)" in 2025, which put forward clear requirements for the transparency, fairness, and robustness of AI models.
Looking ahead, the integration of AI and finance will enter a deeper level. With the maturity of federated learning technology, banks can jointly train risk control models without sharing raw data, greatly improving cross institutional anti fraud capabilities. And the breakthrough of general artificial intelligence, if it comes, will fundamentally change the operating rules of financial markets.
【 Reference sources 】 Bank for International Settlements 2025 Annual Report, Ant Group Technology White Paper, Industrial and Commercial Bank of China 2025 AI Risk Control System Launch Announcement, Official Text of the EU Artificial Intelligence Act, People's Bank of China's "Management Measures for Artificial Intelligence Applications in the Financial Sector (Draft for Comments)"