# How a Fintech Card Issuer Put Claude to Work in Marketing and Fraud Without Adding Headcount > VGV trained a fintech card issuer’s staff on Claude and stood up two pilots, a fraud alert layer and a marketing skill suite, now run by the teams already doing the work. - Source: https://verygood.ventures/success-stories/fintech-ai-enablement/ - Client: Fintech card issuer - Industry: Financial Services - Services: AI Strategy, AI Literacy Training, Claude Skills & Agents, Agentic Solutions --- How a Fintech Card Issuer Put Claude to Work in Marketing and Fraud Without Adding Headcount This fintech card issuer had always grown by adding people. With applications about to reopen, that model was running out of room. VGV joined the issuer’s team to find where AI could carry the load, trained staff on Claude, and stood up two pilots now run by the people already doing the work. - **80%** Of previously undetected transactional fraud projected to be mitigated - **5 min** To produce a complete newsletter draft, a job that used to take about a week - **1M+** Real transaction records used to validate the fraud detection engine ## At a glance: Scaling the team without scaling headcount - **01, The challenge** The issuer had grown by adding people. With applications set to reopen, marketing and fraud would feel the pressure first, and neither could hire fast enough to keep up. - **02, Our approach** We interviewed 14 stakeholders across six departments, consolidated 91 pain points, and scored each one on value, effort, risk, and maintenance. We trained the team on Claude and chose two pilots each department could own. - **03, The results** An AI-ranked fraud alert layer, validated on 1M+ real records, projected to catch 80% of fraud that previously went undetected. Newsletter drafts in 5 minutes instead of about a week. The client approved a follow-on engagement before the first pilot wrapped. ## The challenge: A growth curve the issuer couldn’t hire its way through The client is a fintech credit card issuer with a lean team. For years it absorbed operational friction the usual way, by adding people. That held while new applications were paused. It would not hold once they reopened. The company’s co-founder and CEO told us at kickoff that the pressure would hit departments in sequence. Marketing would feel it first as acquisition volume climbed. Customer service would follow. Fraud exposure would grow with every new account. Marketing ran on two people, and every email cleared a slow approval chain that ended with the sponsor bank. Even a button color change could take a week. In fraud, the data needed to catch cross-account fraud rings already flowed through the issuer’s pipeline. Nothing was watching it for patterns, so rings surfaced only after the damage was done. The issuer wanted to get ahead of its next wave of growth—with AI built into daily work and in the hands of the teams who know it best. > Having been in this industry for however long it’s been, it’s just crazy what you can do with AI... it is pretty wild > > Chief Technology Officer ## The fraud pattern alert layer: From hiring ahead of growth to an AI-first team The fraud review portal built with the issuer’s team. Fraud experts describe suspicious patterns in plain language, and the system generates the rules that flag them across accounts. Claude ranks every alert by severity, removes duplicates, and suggests a next step, so analysts review the riskiest cases first. ## The fraud pilot: Watching patterns nobody had time to watch The issuer’s fraud data already existed. Login events, transaction authorizations, and app navigation logs flowed into a working pipeline. What was missing was something watching across accounts. Now its fraud experts describe a pattern in plain language, and the system generates the rule and the evals to catch it. We proved it out on five test cases, from shared IP addresses to geographically impossible card-present transactions. A Claude layer then ranks each alert by severity, removes duplicates, and suggests a next action. Every recommendation stays informational, and analysts make every call. Midway through the build, the team moved the review interface from an off-the-shelf tool to a custom portal. We validated the engine against more than one million real records before it moved to production. ## Our approach: Start with the people doing the work ### Listening before building We ran 14 structured interviews across six departments: fraud, customer service, marketing, operations, data and analytics, and technology. More than 100 raw issues consolidated into 91 pain points and 10 cross-cutting themes. Cross-system data reformatting alone accounted for 21 of them. We scored every pain point on value, effort, risk, and long-term maintenance, which produced 12 ranked recommendations. Two rose to the top. Each could be owned by a single department, shared no upstream systems with the other, and needed only the issuer’s internal sign-off rather than a sponsor-bank review. ### Training the team on Claude first Alongside the pilots, we ran an AI Literacy program. Three 2.5-hour sessions ran on two tracks, one for executives and one for individual contributors. Teams worked through Claude fundamentals and prompting, reusable projects, and extending Claude with skills, subagents, connectors, and Cowork automation. People learned on their own work, so the habits held after the sessions ended. [Learn about VGV’s AI Transformation service →](/our-services/ai-enablement/) **Claude Partner Network · Select Services Partner** ### Built by a team that ships with Claude every day VGV is a Select Services Partner in the Claude Partner Network, and our AI engineers are Anthropic-certified. The same people who trained the issuer’s staff built the pilots alongside them, so what we teach comes from what we ship. ## The marketing pilot: A skill suite for a two-person team We packaged the issuer’s marketing workflows into a Claude plugin of skills and agents. A Campaign Designer turns a loose goal into a structured plan. A Campaign Orchestrator carries that plan out end to end. Eight sub-agents run inside those skills, covering brand voice, copy editing, SEO, competitor research, backlinks, social media, and internal communications. The suite plugs into the tools the team already used. It builds emails in the marketing platform, routes newsletters for bank compliance approval, submits them to the review board, reads reviewer feedback, and applies the requested revisions. The team kept its process. The repetitive steps moved to Claude. In the first end-to-end test, a complete newsletter draft took about five minutes. ### Built to stay with the team We designed both pilots to be run by the people already doing the work. The issuer’s fraud analysts send feedback and request improvements directly, and a connector lets them query the fraud portal from Claude. Marketing runs its suite without waiting on engineering time. Because each team helped design the tools it was trained on, the gains compound after we leave instead of decaying. The pattern travels to any department. We interview the team, rank the pain points by ROI, train everyone on Claude, and build agents where the math is obvious. ## What’s next: Agents that work while the team sleeps The pilots put Claude into the daily work of two departments. The next step is teaching the issuer’s teams to build agents that run on their own. These agents don’t wait for someone to open a chat. They run on a schedule overnight and hand people a finished starting point each morning. In fraud, that means an agent that sweeps the previous day’s activity against a growing pattern library, so a ranked review queue is waiting when analysts log in. In marketing, it means topic research, first drafts, and performance summaries ready before the workday starts. We’ll train the teams to design, schedule, and monitor these agents themselves, with the same guardrails as the pilots. Agents recommend, people decide, and IT controls what each agent can reach. The goal is for the issuer to keep adding agents long after our engagement ends. ## Roll credits - **Client:** Fintech card issuer - **Services engaged:** [AI Strategy](/our-services/ai-strategy-workshops/), [AI Literacy Training](/our-services/ai-enablement/), Claude Skills & Agents, [Agentic Solutions](/our-services/agentic-ai-solutions/) - **Built with:** Claude, Claude skills and agents, A connector to the fraud portal - **Delivered:** A fraud pattern alert layer, A marketing skill suite, AI Literacy training on two tracks - **Notes:** The 80% figure is a projection from the client’s data leadership. The newsletter result reflects the first end-to-end test.