Bretton AI secures $75 Million in Series B to advance artificial intelligence against financial crime

Bretton AI, a San Francisco–based artificial intelligence company formerly known as Greenlite AI, announced it has raised $75 million in a Series B funding round to enhance the use of AI tools in detecting and preventing financial crime, including money laundering and fraud, according to Business Wire.

The financing was led by Sapphire Ventures with support from existing backers such as Greylock Partners, Thomson Reuters Ventures, and Canvas Ventures, reflecting growing investor interest in AI applications for regulatory compliance. Bretton AI plans to deploy the capital toward accelerating product development, expanding its offerings for regulated financial institutions, and scaling adoption globally.

Growth and strategy: driving AI innovation in financial compliance

The $75 million Series B round marks a major step in Bretton AI’s evolution from its earlier incarnation, Greenlite AI, into a broader platform aimed at helping banks, fintech companies, and other regulated entities manage financial crime risks more effectively.

Investors participating in the round are notable venture capital firms with experience in fintech and enterprise software, indicating confidence in the commercial potential for AI in compliance operations. One of the strategic goals cited by Bretton AI’s leadership is to standardize how artificial intelligence is integrated into core financial crime functions, such as anti–money laundering (AML) and know‑your‑customer (KYC) processes.

Bretton AI builds machine‑learning‑driven agents that automate the investigation workflows traditionally handled by human compliance teams. These systems connect to existing AML and KYC software, pulling data from internal and external sources to assess alerts, analyze patterns, and produce investigation summaries.

This automation can reduce the manual burden on compliance personnel by streamlining tasks that involve gathering data, applying regulatory rules, and drafting reports for review. Early adopters of Bretton AI’s technology have reported reductions in false positives and greater efficiency in managing regulatory workloads, allowing teams to refocus on higher‑value analytical work rather than routine case handling.

Understanding AI’s role in modern financial compliance

Financial crime, which includes money laundering, fraud, and sanctions evasion, remains a persistent threat to financial systems worldwide. Institutions are subject to complex regulatory frameworks requiring extensive monitoring and reporting.

Traditional approaches often rely heavily on manual review, which can be slow, costly, and inconsistent when scaled across large transaction volumes. AI and machine learning technologies aim to address these challenges by identifying suspicious patterns more rapidly and with greater consistency.

Investors and industry participants increasingly view AI as a tool that can augment human capabilities in compliance. Startups and established vendors alike are focusing on algorithmic detection logic, pattern recognition, and automation of repetitive tasks to reduce operational risks and support regulatory adherence.

Despite the promise, deploying AI for financial crime also raises questions about transparency, bias, and governance, prompting regulators and firms to emphasize explainability and oversight alongside technical innovation.

Bretton AI’s $75 million Series B funding round reflects the growing convergence of artificial intelligence and financial crime prevention. With this capital, the company aims to extend its platform’s capabilities and broaden its reach among banks and fintech firms. As regulatory complexity intensifies and financial institutions seek more efficient compliance solutions, AI‑driven automation is likely to play an increasingly central role. Bretton AI’s progress illustrates both the opportunities and responsibilities that come with integrating advanced technology into critical risk‑management functions.

The ongoing evolution of AI in compliance underscores the need for solutions that not only improve operational efficiency but also maintain clarity, fairness, and alignment with regulatory expectations.