How Snowflake Powers AI in Banking: Examples in Customer 360, Fraud Detection, and More

We have an insightful session with Ashish Dibouliya (PhD), who dives into "Building an AI Data Governance Framework for Event-Driven Banking Systems". Learn how AsyncAPI is used to build frameworks in the banking sector. Don't miss out on this opportunity to learn from an expert in the field. 🗒️Check out the agenda: 🔗 https://buff.ly/vrNSADX #AsyncAPIOnline2025 #EventDriven #OpenSource #AsyncAPIConf — Thulie

📉 Core Banking Pain is Real. AI Can Help — But Only If You Get the Playbook Right. Most banks are stuck trying to graft flashy AI tools onto fragile legacy cores. The result? Wasted spend, fragmented workflows, and frustrated teams. That’s why D&T Consulting flips the script. 🧠 We don’t just “do AI.” We bring a pragmatic roadmap that turns hype into operational wins: ✅ Automate KYC and onboarding. ✅ Deploy predictive risk scoring for lending. ✅ Enable real-time compliance alerts. ✅ Reengineer back-office inefficiencies. And we’ve done it ...not in theory, but in practice. From stablecoin integrations to fintech partnerships producing $6M+ in fee income, we’ve helped banks modernize with ROI in mind. 📊 Legacy cores aren’t going anywhere. But clever layering, clean APIs, and execution-first strategy can turn that limitation into your launchpad. Want the playbook? Let’s talk. 🔗 Schedule a call #AIforBanks #CoreBanking #BankTransformation #RiskManagement #KYC #Stablecoin #FintechStrategy #DataDriven #DandTConsulting #BankingInnovation #DigitalBanking #AIExecution #BoardReady

🌐 The Power of Data Science in Modern Banking In today’s rapidly evolving financial world, data science has become the heartbeat of innovation and decision-making in banking systems. Banks generate enormous volumes of data every day from transactions, customer behavior, and risk assessments to fraud detection and compliance monitoring. The challenge isn’t just collecting this data but transforming it into actionable intelligence. That’s where data science plays a critical role. Through advanced analytics, machine learning, and AI, banks can: ✅ Detect fraudulent activities in real time ✅ Personalize customer experiences and product offerings ✅ Enhance credit risk modeling and decision accuracy ✅ Streamline operations through predictive insights ✅ Ensure better compliance with regulatory requirements The true value of data science lies not in the numbers themselves, but in the clarity and foresight it brings to decision-makers. By integrating data-driven insights into banking systems, financial institutions can build more resilient, customer-centric, and future-ready operations. As we move deeper into the digital era, banks that embrace data science as a strategic asset, not just a technical tool, will define the future of finance. #DataScience #BankingInnovation #DigitalTransformation #Analytics

🧊 𝗜𝗻 𝗯𝗮𝗻𝗸𝗶𝗻𝗴, 𝗱𝗮𝘁𝗮 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 𝗮𝗿𝗲 𝗲𝘃𝗼𝗹𝘃𝗶𝗻𝗴—𝘧𝘳𝘰𝘮 𝘣𝘢𝘤𝘬-𝘰𝘧𝘧𝘪𝘤𝘦 𝘥𝘰𝘤𝘶𝘮𝘦𝘯𝘵/𝘴𝘵𝘳𝘦𝘢𝘮𝘪𝘯𝘨 𝘥𝘢𝘵𝘢 𝘴𝘵𝘰𝘳𝘢𝘨𝘦 𝘵𝘰 𝘤𝘰𝘯𝘵𝘳𝘰𝘭 𝘱𝘭𝘢𝘯𝘦𝘴 𝘧𝘰𝘳 𝘈𝘐, 𝘳𝘪𝘴𝘬, 𝘢𝘯𝘥 𝘳𝘦𝘢𝘭-𝘵𝘪𝘮𝘦 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘪𝘯𝘨. At Ideanics, we’ve been working with regional and BaaS-focused banks seeking practical paths to modernization. Snowflake is showing up as a scalable foundation—not just for structured data, but increasingly for unstructured sources too. That includes: • Loan docs, onboarding notes, customer emails, streaming data, wire narratives, fraud alerts, KYC files • All integrated securely and governed to feed AI models and GenAI sutomations without risk exposure • Used in real-time pipelines—AML, GenAI personalization, anomaly detection 📄 We just published a CXO brief: 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 𝗳𝗼𝗿 𝗕𝗮𝗻𝗸𝘀 & 𝗙𝗶𝗻𝘁𝗲𝗰𝗵𝘀 → https://lnkd.in/g73FFDuz Community and regional banks are proving that meaningful automation (AML, fraud checks, exception processing, case triage) can scale without replatforming, as long as there’s:  • A unified data foundation (structured + unstructured together)  • Built-in policy zoning and lineage  • Ability to orchestrate across vendors and partners — not just internal systems 💬 Curious how others are doing this? Let’s compare notes. #SnowflakeForBanks #DocumentData #BackofficeAutomation #DataforScaledAI #BankingAI #UnstructuredData #DataModernization

🏦 𝐖𝐡𝐲 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐝𝐚𝐭𝐚 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐞𝐯𝐞𝐫 𝐢𝐧 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐛𝐚𝐧𝐤𝐢𝐧𝐠. AI in banking thrives on structured, consistent, and connected data whether it’s detecting fraud, assessing credit risk, offering personalized experiences, or simplifying regulatory reporting. But here’s the challenge: too many banks still operate with legacy systems and data silos, limiting their AI potential. The shift toward data modernization unified platforms, strong governance, and clean data pipelines is what’s helping financial institutions move from insights to real impact. 🔍 Structured data isn’t just an IT upgrade it’s a strategic advantage. #NexusGroupe #AIinBanking #Fintech #DataStrategy #DigitalTransformation #CoreBanking #Innovation #StructuredData

The banking sector consistently grapples with these challenges: 1. Fragmented data across core, LOS, and external platforms 2. Fragile or manual ETL workflows 3. Challenges in assessing profitability and risk with high precision 4. Limited scalable or cloud-based infrastructure 5. Intricacies in integrating fintech or partner solutions. Stellar Insights is designed with these issues in mind, offering an architecture that addresses each one. #FintechSolutions #BankingInnovation

Rational Exponent: AI and automation for transforming banking operations - Rational Exponent provides an AI-powered compliance and operations platform tailored to the policy and risk management needs of the financial industry. Their re:agent platform ingests and analyzes data from core banking systems, enterprise applications and external channel services in order to automate anomaly detection and incident resolution workflows. https://lnkd.in/e8nQpM5Y

2025 The Voice of the Makers: Core Banking Modernization and AI Enterprise-Wide | prepared by IBM This report examines the accelerating transformation of the global banking sector as institutions pursue full-scale modernization of core systems underpinned by enterprise-wide artificial intelligence. It positions 2025 as a decisive inflection point, where legacy infrastructure—often decades old—meets a new digital paradigm defined by intelligent automation, modular architectures, and data-driven decision engines. The study synthesizes insights from global banking executives, highlighting that modernization is no longer a technical upgrade but a strategic imperative for resilience, agility, and customer-centric growth. Quantitatively, the report reveals that 78% of surveyed banks have initiated core modernization projects, with 42% integrating AI into mission-critical functions such as credit decisioning, fraud detection, and liquidity management. Institutions deploying cloud-native AI architectures report processing efficiency gains exceeding 60%, while data latency across transaction systems has dropped by up to 70%. IBM’s findings emphasize that modernization success depends on three pillars: platform interoperability, governance-enabled AI deployment, and cultural readiness for continuous innovation. Banks that unify data pipelines and operational workflows through AI orchestration are capturing early competitive advantage, converting operational scalability into strategic differentiation. In conclusion, the report argues that the convergence of core modernization and enterprise AI represents the foundation of a new financial operating model—one that is intelligent, adaptive, and fundamentally customer-driven. As AI matures from augmentation to autonomy, the financial industry faces both a challenge and an opportunity: to rebuild the digital core not as a back-office function but as the engine of value creation for the coming decade. #CoreBanking #AIinFinance #EnterpriseAI #IntelligentAutomation #FutureOfBanking #IBMResearch #FinancialInnovation #TechModernization #CognitiveBanking #DigitalTransformation

Bud launches MCP server for AI-driven banking Bud Financial has introduced its new MCP server, built on the open Model Context Protocol (MCP), designed to accelerate the creation of AI agents with a deep, real-time understanding of users’ finances. It allows AI applications to integrate seamlessly with Bud’s platform, enabling banks to deploy both internal and customer-facing AI systems while safely leveraging customer data. Bud’s models, trained specifically on banking data, help employees perform tasks that once required weeks of data analysis, now in seconds. The Bud MCP server is available now for banks, credit unions, FinTechs, and partners. Source: https://lnkd.in/eSYr9SwA This and more in the newsletter! Sign up here: https://lnkd.in/ecvqiKBY Find this helpful? [ 𝗿𝗲𝗽𝗼𝘀𝘁 ] Anything to add about this subject? [𝗶𝗻𝘃𝗶𝘁𝗲𝗱 𝘁𝗼 𝗰𝗼𝗺𝗺𝗲𝗻𝘁] Nice story. Next! [ 𝗹𝗶𝗸𝗲 ]