5 ways Brex is using AI [Case Study] [2026]
Brex, a financial technology innovator, leverages artificial intelligence (AI) to optimize operations, deliver real-time insights, and enhance customer experiences. The company maintains its position at the forefront of modern financial services by seamlessly integrating AI-driven tools. This approach ensures it consistently adapts to shifting market demands and emerging technologies. These solutions power streamlined corporate credit cards, automated expense management, and sophisticated data analytics capabilities. This case study explores five ways Brex applies AI to its suite of financial products and services, all geared toward reducing manual processes, minimizing risk, and empowering clients to make data-driven decisions. Key focus areas include real-time fraud detection, advanced credit risk modeling, predictive analytics for expense categorization, automated compliance checks, and personalized customer support. By harnessing the power of AI, Brex not only ensures faster, more accurate outcomes but also drives continuous innovation within a highly competitive fintech environment. Ultimately, these AI-driven initiatives serve as a strategic roadmap for other financial organizations looking to improve efficiency, security, and customer satisfaction.
Related: Wells Fargo using AI [Case Study]
5 ways Brex is using AI [Case Study] [2026]
Case Study 1: Automated Expense Categorization with AI
Challenge
Brex recognized that many clients spent considerable time and resources manually categorizing expenses. Traditional expense management processes often led to inaccuracies and inconsistencies, which, in turn, affected overall financial visibility. The reliance on manual data entry meant employees had to review each transaction individually, classify it, and reconcile it within corporate accounting systems. This method frequently resulted in errors and delays because financial transactions move quickly and at high volume. As Brex scaled its corporate credit card offerings, the company realized it needed a more efficient way to process these growing transactions. Manual intervention dragged down routine operations and raised the risk of non-compliance with both internal policies and external regulations. Ultimately, improving accuracy, saving time, and preserving data integrity became the key drivers behind Brex’s decision to employ an AI-based categorization solution.
Solution
Brex integrated machine learning algorithms into its expense management platform to solve these challenges. These algorithms were trained on historical transaction data, including merchant information, spending patterns, and industry-specific categorizations. The system learned to identify patterns and assign appropriate categories to new expenses in real-time by analyzing large amounts of labeled and unlabeled data. Furthermore, the AI-driven categorization engine continuously refined its accuracy through feedback loops, which allowed it to recognize emerging spending categories and adapt to evolving client needs. This robust, self-learning model replaced the manual review processes that previously bogged down finance teams. As a result, the categorization workflow became more reliable and scalable, enabling the platform to handle a higher volume of transactions without diminishing accuracy.
Result
The immediate outcome was significantly reduced time spent on expense review and classification. Finance teams could allocate fewer resources to data entry and redirect them toward strategic financial planning, forecasting, and other higher-value activities. Errors decreased substantially as the machine learning algorithms demonstrated improved consistency over human-led processes. The platform processes large datasets swiftly and accurately. As a result, Brex’s clients enjoy near real-time visibility into their corporate finances. This heightened transparency helped businesses monitor spending patterns, identify outliers, and implement corrective measures faster. Moreover, reducing classification errors improved the quality of financial reporting, fostering greater trust in the data used for important operational and investment decisions.
Impact
By automating expense categorization with AI, Brex enhanced the overall user experience and solidified its reputation as a fintech innovator. Clients appreciated the ease of onboarding and the robustness of the analytics, which increased customer loyalty and a broader client base. Compliance concerns diminished with improved data accuracy and reduced human error, allowing finance departments to ensure that purchases aligned with organizational policies and accounting standards. This automation also positioned Brex to rapidly scale its services across various industries and sectors, meeting the growing demand for digital-first, data-driven financial solutions. In addition, the success of Brex’s AI-powered model served as an industry benchmark, encouraging other financial service providers to explore machine learning technologies in their expense management processes.
Brex overcame a fundamental challenge in financial operations through strategic investment in automated expense categorization. By harnessing AI’s predictive and adaptive capabilities, the company alleviated administrative burdens and elevated the entire expense management lifecycle, ultimately driving client satisfaction and reinforcing its market leadership.
Case Study 2: Real-Time Fraud Detection and Prevention
Challenge
Brex identified a growing need among its clients for robust fraud prevention measures to safeguard financial transactions and sensitive data. In a world where digital payments and online banking solutions have become the norm, illicit activity and data breaches can significantly jeopardize business operations and consumer trust. While traditional fraud detection systems rely heavily on batch processing—triggering alerts or flags after fraudulent transactions have been completed—this approach often leads to delayed response times and higher financial losses. Brex wanted to move beyond these reactive processes and instead adopt a proactive solution. The primary challenge was to detect potential fraud in real-time without compromising the speed of legitimate transactions. This balance between security and operational efficiency required a cutting-edge, AI-enabled strategy that could identify even subtle anomalies in transaction patterns.
Solution
To address this challenge, Brex deployed a machine learning-powered system to track and analyze transaction data in real-time. This system leverages advanced algorithms trained on historical fraud and transaction datasets, enabling it to recognize typical patterns, deviations, and emerging threats quickly. The system temporarily halts the transaction when a potential issue is identified—such as a significant purchase from an unusual location or an atypical spending frequency. It alerts the Brex risk team for further investigation. Automated workflows streamline these investigations by allowing risk analysts to input feedback on the transaction’s legitimacy. As it processes more data, the AI continuously fine-tunes its detection capabilities. By assessing different factors—ranging from IP addresses and geolocation to device fingerprinting and user behavior analytics—the machine learning model rapidly builds a real-time risk profile for every transaction.
Result
The immediate benefit of implementing this real-time fraud detection system was a marked decrease in fraudulent activities across Brex’s platform. While no system can guarantee zero fraud, the powerful AI engine substantially reduced the scale of successful attacks and subsequent financial losses. Importantly, the speed of legitimate transactions remained largely unaffected. The streamlined, automated approach often expedited approvals for routine transactions, eliminating the need for extra verification steps. This automated process also contributed to a positive user experience, given that most valid transactions were processed smoothly without hindrance. Meanwhile, flagged transactions were evaluated promptly, reducing the likelihood of fraudulent charges being finalized. Overall, Brex’s fraud detection rates improved, leading to a surge in customer confidence and reinforcing the platform’s credibility.
Impact
Adopting an AI-driven, real-time fraud detection framework had immediate and long-term effects on Brex’s business and clientele. By reducing manual reviews, risk teams gained more bandwidth for higher-level tasks. This shift enabled them to refine existing algorithms and address complex cases requiring human expertise. Consequently, they elevated the system’s overall accuracy and reliability. At the enterprise level, Brex’s reputation as a secure, forward-thinking fintech provider attracted more partners and clients seeking robust protection for their financial transactions. This trust advantage contributed to increased usage of Brex’s core services and successful expansions into additional product lines and markets. Moreover, the heightened security enabled Brex’s customers to scale confidently, knowing their payment activities were under constant, intelligent scrutiny. Overall, the real-time fraud detection platform solidified Brex’s position as a leader in secure, AI-enhanced financial solutions, driving growth and fostering long-term customer loyalty.
Case Study 3: Intelligent Credit Risk Analysis
Challenge
Brex encountered the complex challenge of accurately assessing the creditworthiness of its corporate clients in an environment characterized by diverse financial histories and rapidly changing market conditions. Traditional credit risk models often rely on manual data input and a limited set of financial metrics, leading to potentially outdated evaluations. Further complicating matters, many fast-growing businesses—particularly startups—possess limited credit histories, making it difficult to use conventional scoring approaches. This lack of nuanced insight into a company’s financial profile risked underestimating and overestimating credit risks. For Brex, striking a balance between ensuring sufficient lending caution and offering competitive credit terms became paramount. An ineffective or overly rigid credit evaluation system could lead to increased defaults or missed opportunities, respectively.
Solution
To address these constraints, Brex developed an AI-powered credit assessment model that ingests various data points, from bank statements and cash-flow analytics to real-time revenue trends and operational metrics. Rather than relying solely on static credit scores, the model applies machine learning algorithms to interpret and weigh each data source, generating a multifaceted view of a client’s financial health. By incorporating predictive analytics, the system can forecast a company’s growth trajectory and potential financial challenges, assigning credit limits and terms that reflect a more accurate risk profile. Additionally, Brex’s AI-driven solution adapts to market fluctuations and evolving client data, recalculating credit limits in near-real time. This dynamic approach ensures that businesses are neither overextended nor hindered by insufficient credit lines.
Result
The immediate outcome of the new credit analysis framework was a significant increase in accuracy when determining appropriate credit limits. Companies previously considered high-risk under traditional methods received better-aligned credit offerings, reflecting their capacity to manage and repay obligations. Similarly, businesses that posed higher risks received tighter controls, helping mitigate potential losses. The AI-driven evaluation process also led to expedited credit approvals. By automating much of the due diligence and leveraging real-time financial indicators, Brex reduced turnaround times. Enhanced underwriting improved operational efficiency and bolstered client satisfaction, as companies could secure credit lines more rapidly. The refined accuracy in risk assessment decreased default rates, stabilizing Brex’s lending portfolio and boosting investor confidence in the fintech’s growth strategy.
Impact
By rolling out intelligent credit risk analysis, Brex elevated the standard for digital lending and redefined how credit should be extended to modern, data-centric businesses. The robust, adaptable underwriting model garnered trust from enterprise clients who desired customized, flexible financing solutions aligned with their growth trajectories. Furthermore, the dynamic approach to credit limits helped foster stronger relationships, as companies saw Brex as both a partner and a financial enabler. This alignment translated into greater loyalty and expanded usage of Brex’s suite of financial products. The success of the AI-based credit analysis platform also positioned the company as an industry thought leader in data-driven decision-making. Other fintech firms and traditional financial institutions looked to Brex’s model as a blueprint for integrating artificial intelligence in credit operations. Overall, this proactive, intelligence-oriented stance on credit risk solidified Brex’s market presence, enabling it to continue advancing toward a future where adaptive, real-time financial strategies are the norm. This evolution underscores Brex’s commitment to refining AI-driven tools for success.
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Case Study 4: AI-Driven Spend Management Insights
Challenge
Brex found that many clients relied on scattered data sources and disjointed tools for tracking and managing corporate spending. Traditional methods often involved spreadsheets and legacy systems, making obtaining timely, actionable insights difficult. Without integration, finance teams struggled to spot inefficiencies or anomalies and relied on outdated information. Static reports quickly lost relevance in the fast-paced business environment, preventing proactive budgeting and cost optimization. Brex sought a solution to unify disparate data, align departments, and empower stakeholders with real-time analytics to foster more informed spend management decisions.
Solution
Brex deployed an AI-driven spend management platform, aggregating transactional data from multiple sources into a unified dashboard. Machine learning algorithms categorized expenses in real-time recognized emerging trends and flagged suspicious transactions for review. These models also used historical data to forecast potential budget overruns or savings opportunities. The platform offered dynamic reporting, enabling finance teams to build customized dashboards and drill down into specific categories—such as travel or vendor expenses—to uncover inefficiencies. Real-time updates gave department heads clearer budget visibility, encouraging collaboration and preventing overspending. Additionally, integration with ERP systems reduced duplicate data entry and streamlined data flow across organizational units.
Result
Implementing the AI-based spend management platform yielded swift advantages. Clients accessed accurate, holistic reporting on company-wide expenses, eliminating the need to reconcile multiple spreadsheets. Teams allocated resources more effectively, quickly identifying overspending risks. Real-time data helped leaders make agile decisions—such as reallocating budgets mid-quarter—to adapt to shifting conditions. Anomaly detection flagged questionable transactions early, raising the likelihood of timely intervention. Moreover, finance executives benefited from improved forecasting, leveraging AI-driven insights to anticipate future budgetary needs and shape strategic planning.
Impact
Brex improved clients’ ability to maintain fiscal discipline and foster sustainable growth by delivering advanced spend management insights. A consolidated view of all expenses enabled senior management to accurately measure returns on various investments, ensuring strategic resource allocation. The streamlined workflow elevated finance team productivity, eliminating the need for manual spreadsheets or chasing missing data. Automated data integration enhanced internal transparency, keeping departments accountable for their budgets. Consequently, decisions were guided by real-time analytics rather than retrospective, error-prone data. Brex’s spend management solution thus became a unique value driver, strengthening its reputation as a trusted fintech partner for companies seeking to optimize financial operations. Adopting an AI-first approach to expense monitoring and reporting, Brex cemented its position in the market and opened new pathways for innovation.
Brex provided ongoing training and support to enhance user adoption, illustrating how to configure dashboards and utilize real-time analytics effectively. This guidance helped teams across various departments become more self-sufficient, reducing dependence on external consultants or specialized data analysts. In addition, the AI-driven insights equipped finance leaders to negotiate better contracts with vendors, leveraging past spending data to secure volume-based discounts or more favorable payment terms. These benefits ultimately translated into improved profitability, as streamlined processes and intelligent budgeting contributed to sustainable cost savings.
Moreover, by centralizing spend analytics, Brex enabled finance teams to benchmark performance across subsidiaries or product lines, driving healthy competition and pursuing cost-saving best practices. This holistic view further empowered executives to identify underperforming areas and reallocate funds to the most promising, revenue-generating initiatives.
Case Study 5: Predictive Customer Support and Service
Challenge
Brex recognized the critical importance of providing rapid, accurate support to its growing client base, spanning early-stage startups to well-established enterprises. However, conventional support channels often relied on reactive measures, responding to user issues only after they were raised. This approach resulted in delayed resolutions, inconsistent service quality, and higher operational costs. Further, the traditional ticket-based system provided limited insight into recurring problems or potential trends, making it difficult to pinpoint systemic issues before they escalated. For a company operating in a competitive fintech landscape, these support bottlenecks threatened client satisfaction and retention, prompting the need for a solution that could both predict and prevent customer service lapses.
Solution
To address these challenges, Brex deployed an AI-driven predictive support model that leverages data from various customer touchpoints, including chat logs, email inquiries, user feedback surveys, and product usage metrics. Machine learning algorithms sifted through this data to detect emerging patterns, such as repeated login difficulties or confusion around specific product features. When the system identified a likely support issue, it proactively generated recommendations to Brex’s customer success team, encouraging immediate outreach or preemptive fixes. This AI-enhanced framework also integrated with the company’s knowledge base, updating relevant help articles automatically when patterns indicated a gap in existing documentation. Moreover, the predictive service model included natural language processing (NLP) tools for more nuanced sentiment analysis, helping Brex prioritize sensitive issues and escalate them quickly when required.
Result
Implementing predictive customer support yielded immediate resolution times and overall service quality benefits. Clients experienced faster response rates as common issues were flagged and addressed before they became critical. Brex’s customer success team, freed from sifting through repetitive inquiries, could devote more time to complex cases requiring human insight. The AI-driven system provided consistent, data-backed recommendations, reducing the variability often seen when different support agents handle similar issues. As a result, user satisfaction scores rose, and the volume of recurring tickets declined. The proactive measures also helped minimize downtime, especially when system performance updates or maintenance windows could be communicated beforehand. Over time, the platform’s learning mechanism improved, enabling even more accurate predictions and resource allocation.
Impact
By implementing an AI-powered, predictive support strategy, Brex transformed customer service from a purely reactive function to a proactive, value-adding component of the user experience. The company’s reputation for prompt, efficient assistance attracted new clients seeking reliability in financial services. Existing clients developed deeper trust and were more inclined to explore additional Brex products, boosting cross-selling opportunities. Internally, the enriched insights helped product teams pinpoint areas of confusion, leading to interface improvements and targeted feature upgrades that enhanced user engagement. Meanwhile, support teams benefited from reduced ticket volume, enabling them to tackle complex queries effectively and refine their skill sets. This cultural shift toward preemptive problem-solving solidified Brex’s standing as a forward-thinking fintech leader, reinforcing that robust customer support goes hand-in-hand with sustainable business growth. By infusing artificial intelligence into each stage of the support lifecycle, Brex positioned itself to meet—and exceed—rising client expectations in an evolving digital landscape.
Alongside these immediate benefits, the predictive support model fostered a culture of continuous improvement, with newly identified challenges prompting knowledge base updates and escalation paths.
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Conclusion
Brex’s utilization of AI underscores the growing importance of technology-driven innovation in the financial sector. Through advanced fraud detection, refined credit risk modeling, predictive insights, automated compliance checks, and personalized client interactions, Brex consistently leverages cutting-edge solutions to optimize customer experiences and expand its market reach. As the fintech industry evolves, businesses that integrate robust AI frameworks will be better positioned to meet the rapidly shifting demands of modern enterprises. Brex’s approach highlights how strategic investment in AI streamlines daily processes and paves the way for long-term organizational growth. By capitalizing on precise, data-based decision-making, the company continues to refine its service offerings and improve operational efficiency. AI-centric methodologies can inspire other financial institutions to explore, adopt, and scale innovative solutions. In doing so, they enhance client confidence, foster trust and resiliency, and propel the fintech landscape into a future defined by intelligent, automated systems.
