How AI and Fintech Work Together

𝗧𝗿𝗮𝗱𝗲𝗰𝗿𝗮𝗳𝘁 𝗔𝗜: 𝗧𝗵𝗲 𝗡𝗲𝘅𝘁 𝗪𝗮𝘃𝗲 𝗼𝗳 𝗙𝗶𝗻𝘁𝗲𝗰𝗵 𝗜𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 There's a growing sense that fintech investing is back. If so, the question is: Where will the money go? 𝗠𝘆 𝘁𝗮𝗸𝗲: It's not going into new neobanks. Instead, it's going to go into an emerging segment best described as Tradecraft AI. Tradecraft AI is the fusion of applied domain knowledge and AI technology. It captures the tacit, apprentice-learned knowledge traditionally acquired through years of experience and embeds it into software with the precision, nuance, and adaptability of a seasoned expert. Tradecraft AI sits at the intersection of three powerful investment theses: 1️⃣ 𝗩𝗲𝗿𝘁𝗶𝗰𝗮𝗹 𝗦𝗮𝗮𝗦. These companies are application-first and built for workflows, not just data. 2️⃣ 𝗔𝗽𝗽𝗹𝗶𝗲𝗱 𝗔𝗜. The tools that apply AI to real, valuable problems will extract significant economic rent. 3️⃣ 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. As I noted in a recent post "AI tools and technologies are now infrastructure—technology capabilities upon which to build business capabilities and processes." What sets tradecraft AI apart from vertical AI is its depth of specialization--it understands the jobs-to-be-done and translates that understanding into software that thinks, recommends, and acts like a domain expert. Companies emerging in the new tradecraft AI space include: ▶️ MOGOPLUS provides agentic AI solutions for lenders. Its AI agents automate critical components of the consumer and SME loan lifecycle, including income verification, creditworthiness analysis, and application processing. ▶️ UPTIQ offers pre-built AI agents tailored to fintech workflows covering lending, fraud detection, customer support, financial planning and analysis, and loan servicing. Enables rapid deployment with zero coding required. ▶️ Covecta is an agentic AI platform for commercial lending and credit teams. AI agents autonomously handle end-to-end loan lifecycle tasks—from lead intake and customer profiling to covenant testing and portfolio monitoring. ▶️ Binkey classifies purchase transactions in real time to determine if they’re FSA/HSA eligible based on IRS rules, then automatically routes reimbursements to credit cards, bank accounts, or loyalty balances. ▶️ Lama AI assists commercial loan originators with tasks like lead pre-qualification, underwriting data preparation, and peer benchmarking to accelerate approval cycle time. According to Michael Degnan, founder of VC firm Darrery Capital: “Tradecraft AI is built on the belief that expert systems can be more than brittle rule engines—they can be adaptive, empathetic, and programmatic.” For more on Tradecraft AI, see the #Fintech Snark Tank post 𝙒𝙝𝙮 𝙑𝘾𝙨 𝘼𝙧𝙚 𝘽𝙚𝙩𝙩𝙞𝙣𝙜 𝘽𝙞𝙜 𝙊𝙣 𝙏𝙧𝙖𝙙𝙚𝙘𝙧𝙖𝙛𝙩 𝘼𝙄 𝙄𝙣 𝙁𝙞𝙣𝙖𝙣𝙘𝙞𝙖𝙡 𝙎𝙚𝙧𝙫𝙞𝙘𝙚𝙨 https://lnkd.in/eT-Hf4Za

Your AI project will succeed or fail before a single model is deployed. The critical decisions happen during vendor selection — especially in fintech where the consequences of poor implementation extend beyond wasted budgets to regulatory exposure and customer trust. Financial institutions have always excelled at vendor risk management. The difference with AI? The risks are less visible and the consequences more profound. After working on dozens of fintech AI implementations, I've identified four essential filters that determine success when internal AI capabilities are limited: 1️⃣ Integration Readiness For fintech specifically, look beyond the demo. Request documentation on how the vendor handles system integrations. The most advanced AI is worthless if it can't connect to your legacy infrastructure. 2️⃣ Interpretability and Governance Fit In financial services, "black box" AI is potentially non-compliant. Effective vendors should provide tiered explanations for different stakeholders, from technical teams to compliance officers to regulators. Ask for examples of model documentation specifically designed for financial service audits. 3️⃣ Capability Transfer Mechanics With 71% of companies reporting an AI skills gap, knowledge transfer becomes essential. Structure contracts with explicit "shadow-the-vendor" periods where your team works alongside implementation experts. The goal: independence without expertise gaps that create regulatory risks. 4️⃣ Road-Map Transparency and Exit Options Financial services move slower than technology. Ensure your vendor's development roadmap aligns with regulatory timelines and includes established processes for model updates that won't trigger new compliance reviews. Document clear exit rights that include data migration support. In regulated industries like fintech, vendor selection is your primary risk management strategy. The most successful implementations I've witnessed weren't led by AI experts, but by operational leaders who applied these filters systematically, documenting each requirement against specific regulatory and business needs. Successful AI implementation in regulated industries is fundamentally about process rigor before technical rigor. #fintech #ai #governance

The AI wave has officially hit Wall Street. And Anthropic has a new set of powerful tools. Historically, industries like finance, law, and government were considered "safe" from major tech disruption. The work was too esoteric, the data was too sensitive, and the regulations too rigid. But that moat is vanishing fast. With the launch of Claude’s Financial Analysis Solution, Anthropic is making a bold play: → Bring LLMs directly into the workflows of financial analysts → Integrate real-time data from Box, PitchBook, S&P Global, Databricks, and Snowflake → Offer secure enterprise-grade tooling, right on the Amazon Web Services (AWS) Marketplace This isn’t a side feature. It’s a direct replacement for hundreds of hours of analyst work. We're not yet in the era where the financial analyst role is obsolete. But it’s becoming radically reshaped. So what does this mean if you work in financial services? Master these tools before they become mandatory. Those who learn to wield Claude and other AI copilots with precision will outperform peers who don’t. Your edge is no longer access to information. It's your ability to interpret, prompt, verify, and synthesize at speed, as "Experience" won’t be enough. Insights and smart tool usage will redefine who rises. And if you’re a founder? There is a brief window of opportunity to build: → Vertical SaaS tools layered on top of these models → Fintech copilots for workflows not yet automated → Niche AI assistants tailored to private equity, hedge funds, compliance teams, etc. The new buyer is no longer skeptical, they’re actively shopping. And the budgets are shifting rapidly toward solutions that cut cost and compound insight. Finance, law, and government are waking up to AI. If you have a compelling solution set, are you already building for them? Lmk in the comments! 👇🏾 ---— 👋🏾 Want more startup advice and tech news? Follow me here: Justin Gerrard And check out my podcast: Rush Hour Podcast ♻️ Repost if you think someone in your network would benefit! #anthropic #claude #finance

Just read a fascinating paper on "TradingAgents: Multi-Agents LLM Financial Trading Framework" from UCLA and MIT researchers. https://lnkd.in/ecxu-2jP The paper outlines an approach to financial trading that mirrors how actual trading firms operate, using specialized AI agents working together: • Fundamental analysts examine company financials • Sentiment analysts track market mood • Technical analysts study price patterns • Risk managers monitor exposure • Even "bull" and "bear" researchers who debate different market perspectives! What's particularly interesting is how these AI agents collaborate through structured communication protocols to make trading decisions. The results? The framework supposedly outperformed traditional trading strategies, showing: ✨ Superior cumulative returns 📈 Better Sharpe ratios 📉 Lower maximum drawdowns Key innovation: Unlike previous approaches that used single agents or disconnected multi-agent systems, this framework replicates the actual organizational structure of trading firms, complete with specialized roles and debate-driven decision making. Now, there are a gazillion details missing before anything like this could actually find application in the real world but directionally this appears correct to me. Exciting times ahead! #ArtificialIntelligence #FinTech #Trading #MachineLearning #Finance #Research [Note: Paper by Xiao et al., 2024 - UCLA/MIT]

🚀 Agentic AI is transforming fintech. Ant International, a spin-off from Ant Group, is taking financial services to new heights with its latest AI-as-a-Service (AIaaS) platform—the Alipay+ GenAI Cockpit. This groundbreaking tool empowers fintech companies to develop agentic AI systems, enabling AI-native financial services that streamline payments and enhance compliance checks. In an industry where precision and efficiency are paramount, AI-driven automation is becoming a necessity rather than a luxury. ❓What Makes This Innovation Stand Out? 🔹 Agentic AI Systems – Unlike traditional AI, agentic AI isn’t just reactive; it makes autonomous decisions based on evolving data patterns, optimizing transactions without constant manual oversight. 🔹 AI-as-a-Service (AIaaS) – By offering AI capabilities as a scalable service, fintech firms can integrate advanced AI without heavy infrastructure investments, fostering rapid deployment and customization. 🔹 Payments & Compliance Automation – The platform ensures that payments are processed efficiently while meeting strict regulatory requirements, reducing risks and improving fraud detection. ❓Why It Matters for Fintech? 💡 Financial institutions and payment networks must operate at peak efficiency while staying ahead of compliance regulations. AI-native financial services powered by agentic AI can handle complex tasks such as: ✅ Real-time fraud detection across massive transaction volumes. ✅ Automated compliance checks that evolve with regulatory changes. ✅ Smart payment routing to maximize speed and cost efficiency. 💡Shaping the Future of Digital Transactions Fintech is moving toward a self-optimizing infrastructure, where AI agents interact dynamically with financial ecosystems to deliver hyper-efficient and secure solutions. Companies leveraging agentic AI will not only gain a competitive edge but also redefine trust and transparency in digital finance. #AI #Fintech #AgenticAI #Payments #Compliance #DigitalFinance #JPMorgan