JPMorgan, Citi, and Wells Fargo Are Transforming AML, Thanks to AI Tools

As global regulators increase scrutiny and criminals adopt more sophisticated tactics, the world’s leading banks are turning to artificial intelligence to stay up-to-date in their approach to financial crime compliance. 

Traditional anti-money laundering (AML) systems, long burdened by false positives and manual processes, are being replaced or enhanced by AI-powered platforms capable of real-time decision-making, advanced pattern recognition, and intelligent alert triage. 

J.P. Morgan, Citigroup, and Wells Fargo are at the forefront of this transformation, deploying AI to streamline AML investigations, and strengthen name screening processes. Their approaches differ in structure and emphasis but they share the common goal of balancing technological innovation with regulatory accountability.

How J.P. Morgan Deploys AI for Financial Crime Prevention

J.P. Morgan’s AI Research program is a cornerstone of the bank’s strategy to remain at the forefront of innovation in financial services. Established to explore cutting-edge advances in artificial intelligence, the lab focuses on areas including machine learning, natural language processing, explainable AI, privacy-preserving techniques, and quantum computing. The overarching goal is to build intelligent systems that are not only technologically advanced but also secure, responsible, and scalable in the context of real-world financial operations.

A particularly ambitious pillar of the lab’s agenda is its work on AI to battle financial crime. The research team investigates proactive systems capable of predicting and preventing fraud, money laundering, and other financial threats before they occur. This includes developing anomaly detection models, behaviour-based monitoring systems, and synthetic data techniques to enable safer and more robust training environments for algorithms.

What sets J.P. Morgan’s AI Research program apart is its tangible impact across the organisation. Several research initiatives have successfully transitioned from lab to production. For example, advanced fraud detection models developed through the lab have been deployed to monitor real-time transaction flows, significantly improving detection rates while reducing false positives. In anti-money laundering (AML), AI models have enhanced case prioritisation and alert quality, resulting in improved investigator productivity.

Additionally, tools emerging from the AI lab’s work on natural language processing are used internally to analyse large volumes of unstructured data, such as legal documents and client communications, allowing faster insights and better risk assessment.

By tightly integrating research with the firm’s business and operational units, J.P. Morgan ensures its AI innovations deliver measurable value. The AI Research program continues to shape not only the bank’s technological trajectory but also the future of intelligent financial services more broadly.

Scaling Intelligence: Citigroup’s AI Strategy in AML and KYC

Citigroup is actively leveraging artificial intelligence to strengthen its global defenses against financial crime, particularly in the areas of fraud detection, anti-money laundering (AML), and know-your-customer (KYC) compliance. By moving beyond traditional rule-based systems, Citi has adopted machine learning and behavioural analytics to monitor transactions in real time, detect anomalies, and identify suspicious activity more accurately.

These AI systems are designed to reduce false positives, enabling compliance teams to focus on higher-risk cases while improving operational efficiency. Citigroup has implemented these capabilities across its Treasury & Trade Solutions division, allowing real-time risk scoring and transaction monitoring at scale.

Natural language processing (NLP) tools are used to extract key information from regulatory updates, screen unstructured data, and support automated KYC and AML workflows. These tools assist in alert triage, enabling analysts to prioritise cases more effectively and reduce investigation times.

Citigroup has also made strategic investments in AI technologies that surface hidden financial crime risk indicators from large, disparate datasets. These capabilities support advanced alert scoring and enhance decision-making for risk professionals.

Many of these AI innovations are not experiments but are already being used in day-to-day operations. Citigroup has integrated AI into its fraud detection and AML framework globally, using models that continuously learn from patterns and behaviour to strengthen protection across markets.

Through its combined focus on AI-driven innovation, data analytics, and governance, Citi is building a more adaptive, responsive, and intelligent approach to combating financial crime within a rapidly evolving risk landscape.

Responsible and Explainable AI in Action at Wells Fargo

Wells Fargo recently announced that it is rolling out AI Agents and tools from Google cloud to all of its 215,000 employees, but the bank’s financial crime compliance teams have been using advanced artificial intelligence to strengthen its defences against financial crime for years. Wells Fargo has deployed AI for fraud detection, anti-money laundering (AML), and name screening operations. 

At the core of its approach is the use of machine learning models that analyse transactions in real time, detecting anomalies and suspicious patterns with greater accuracy than traditional rule-based systems. These models are continuously trained on behavioural and contextual data, allowing them to adapt to evolving fraud tactics while reducing false positives and minimising customer disruption.

For AML, Wells Fargo uses AI to aggregate risk signals across multiple data sources, including transactional behaviour, biometric indicators, and external threat intelligence. This helps identify potential money laundering activity that may otherwise go unnoticed through manual or static monitoring. AI tools designed to perform name screening and entity resolution, like Silent Eight’s Iris 6 platform, assist large banks in matching customer records against sanctions lists and other watchlists, enhancing the precision of alerts, reducing the manual burden on compliance teams. This then frees human teams to focus on high risk cases, reducing investigation times and increasing auditability.  

Wells Fargo places a strong emphasis on responsible and explainable AI. It has developed a modular enterprise data science infrastructure that supports governance, transparency, and integration of both internal and third-party models. This framework ensures that AI-driven decisions – such as risk classifications and alert prioritisation – are auditable and compliant with regulatory expectations.

Wells Fargo emphasises that the AI it uses is designed to augment, not replace, human analysts. By triaging alerts and elevating high-risk cases for review, these systems improve the efficiency and effectiveness of compliance operations. AI solutions specifically designed to support this kind of human-in-the-loop compliance, like the solutions provided by Silent Eight, allow analysts to focus on strategic decisions while AI resolves routine investigations in seconds. Altogether, Wells Fargo’s use of AI in financial crime prevention reflects a mature, operationalised approach that balances innovation with oversight.

As AI becomes a strategic pillar in financial crime compliance, banks like J.P. Morgan, Citi, and Wells Fargo are among the leading banks setting the standard for intelligent, scalable and explainable AI in AML operations.