How AI is Transforming Banking Services

AI isn’t just a technology shift — it’s a work shift. And in financial services, that shift is already underway. It starts small: automating tasks. Then it changes how entire jobs function. Eventually, it redefines entire departments. Here’s what that looks like in practice: 🔹 Step 1: AI transforms tasks AI works with you — helping professionals get more done, faster. A loan officer drafts approval notes instantly with AI. An underwriter summarizes 50-page claims files in seconds. A relationship manager personalizes client updates at scale. Most banks and insurers are here today — using AI as a productivity co-pilot. 🔹 Step 2: AI transforms jobs AI works for you — driving outcomes, not just efficiency. A claims agent auto-triages and settles low-risk cases. A KYC bot collects documents, flags risks, and pre-fills onboarding forms. A customer agent handles 70%+ of routine inquiries — end to end. This is where the job itself starts evolving. Less grunt work. More time for strategic judgment and exception handling. 🔹 Step 3: AI transforms functions Entire workflows become agent-led. This shifts how teams are designed. Contact centers turn into experience hubs. Loan ops becomes real-time decisioning. Compliance becomes continuous, not reactive. Role ratios change. Skillsets shift. Firms start hiring for orchestration, design, and oversight — not just execution. What does this mean for growth? Financial institutions can scale smarter — not just by adding headcount, but by rethinking how work happens altogether. AI isn’t replacing jobs. It’s redesigning them — one workflow at a time. And for those who lean in early, that’s a major edge.

Your AI Copilot Isn’t Replacing You — It’s Promoting You 🚀 Remember when Excel first landed in offices? The people who mastered it didn’t get replaced. They got promoted. We’re living through that moment again—only now, it’s with AI. Your AI copilot—whether it’s ChatGPT, Claude, or a custom tool—isn’t here to take your job. It’s here to multiply your impact. Take my week, for example: 🧠 Summarized a 20-page whitepaper in 90 seconds ✍️ Drafted 3 client emails—in my voice, not some generic template 💡 Reframed an investor pitch deck using insights from a different industry None of that replaced me. It amplified me. And what I’m seeing personally? It’s happening at scale in fintech. AI in Fintech: Quiet Revolution, Massive Impact The same AI that’s helping me move faster is now transforming how fintech operates — not someday, but right now. 1. Smarter Risk Management ↳ AI flags fraud in real time, predicts loan defaults before they happen. ↳ JPMorgan cut false positives in fraud detection by 40%. 2. Personalization That Actually Works ↳ Hyper-relevant offers, proactive chatbots, AI-driven wealth advisors. ↳ Result? 5–10% uplift in revenue through more engaged customers. 3. Less Ops, More Innovation ↳ KYC checks, compliance reviews, documentation—automated. ↳ Your team spends less time chasing files, more time chasing growth. PwC predicts over $1 trillion in AI-driven value for financial services by 2030. Deloitte shows major gains in both cost reduction and revenue growth. This isn’t just an upgrade. It’s a shift in how fintech runs. At Netevia, we are already making this a reality. We are currently integrating AI into two core fintech processes: risk assessment and underwriting. These processes are being enhanced with AI to improve accuracy, speed, and decision-making. This integration enables our teams to focus on higher-level insights while AI handles complexity at scale. 💬 If you treat AI as competition, you’ll get left behind. 💡 If you treat it as a collaborator, you’ll move ahead. So let’s make this real: How are you using AI as your copilot? Drop your favorite use case in the comments—let’s crowdsource the next fintech playbook. #AI #Fintech #FutureOfWork #ArtificialIntelligence #ChatGPT #Productivity #CareerGrowth #BankingInnovation

🏦 𝐁𝐚𝐧𝐤 𝐂𝐄𝐎𝐬 𝐀𝐫𝐞 𝐁𝐞𝐭𝐭𝐢𝐧𝐠 𝐁𝐢𝐥𝐥𝐢𝐨𝐧𝐬 𝐨𝐧 𝐀𝐈: 𝐓𝐡𝐞𝐢𝐫 𝐒𝐮𝐫𝐩𝐫𝐢𝐬𝐢𝐧𝐠 5️⃣ 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐓𝐡𝐚𝐭 𝐀𝐫𝐞 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐅𝐢𝐧𝐚𝐧𝐜𝐞 𝐅𝐨𝐫𝐞𝐯𝐞𝐫 The recently published Euromoney “𝐀𝐈 𝐢𝐧 𝐁𝐚𝐧𝐤𝐢𝐧𝐠 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐏𝐥𝐚𝐲𝐛𝐨𝐨𝐤 2025" offers unprecedented insights into how leading FIs are strategically implementing AI. 𝐬𝐨 𝐰𝐡𝐚𝐭 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐭𝐨𝐩 𝐛𝐚𝐧𝐤𝐢𝐧𝐠 𝐀𝐈 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬: 1️⃣ 𝐂𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲, 𝐃𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧: JPMorgan Chase has given 200,000+ employees (2/3 of staff) access to their proprietary LLM Suite platform, allowing model flexibility while maintaining security. 2️⃣ 𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐢𝐧 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦s: Goldman Sachs deployed a firm-wide developer platform connecting AI models to proprietary data with appropriate safeguards, resulting in an AI assistant available to 10,000+ employees. 3️⃣ 𝐑𝐞𝐚𝐥 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐆𝐚𝐢𝐧𝐬 HSBC documented 15-30% efficiency improvements after implementing GitHub Copilot across 10,000 developers. 4️⃣ 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫-𝐅𝐚𝐜𝐢𝐧𝐠 𝐀𝐈 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 NatWest's Cora+ chatbot implementation achieved a remarkable 150% increase in customer satisfaction metrics and 50% reduction in human agent handoffs. 5️⃣ 𝐒𝐦𝐚𝐥𝐥 𝐌𝐨𝐝𝐞𝐥𝐬 𝐌𝐞𝐞𝐭𝐢𝐧𝐠 𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐍𝐞𝐞𝐝𝐬 BNP Paribas partnered with French AI firm Mistral to develop models that can run on private infrastructure for sensitive contract and transaction data. 𝐌𝐲 𝐓𝐨𝐩 🔟 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧𝐬 1. Banking AI strategy has shifted significantly from scattered use cases to “𝘱𝘭𝘢𝘵𝘧𝘰𝘳𝘮-𝘣𝘢𝘴𝘦𝘥 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩𝘦𝘴 𝘸𝘪𝘵𝘩 𝘤𝘦𝘯𝘵𝘳𝘢𝘭 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦”. 2. 2/3 of JPMorgan's staff already have AI access—showing enterprise-wide commitment 3. Major banks are building abstraction layers (Goldman's developer platform, JPMorgan's LLM Suite) rather than betting on single vendors 4. UBS's exponential AI adoption curve (1M prompts in January 2025 vs 1.75M for all 2024) demonstrates momentum 5. Customer-facing implementations are moving cautiously with human oversight 6. Bank of America's Erica evolution (65% to 95% accuracy) demonstrates measured development 7. The European approach (BNP Paribas with Mistral) shows greater emphasis on data sovereignty 8. Agentic banking concepts are emerging but remain experimental 9. Human oversight frameworks will determine speed of adoption in regulated environments 10. Voice-based interactions appear to be the next frontier beyond text-based systems Most promising implementations will be combining deep domain expertise with cutting edge technical expertise And thoughtfully integrating AI into processes, culture and customer relationships. #Banking #ArtificialIntelligence #FinTech #AIStrategy #Innovation

McKinsey & Company 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗳𝗼𝗿 𝗵𝗼𝘄 𝗯𝗮𝗻𝗸𝘀 𝗰𝗮𝗻 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗲𝘅𝘁𝗿𝗮𝗰𝘁 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗔𝗜: ⬇️ This is a full-stack, enterprise-grade architecture — built on agents, orchestration, and rewired workflows. The AI bank stack consists out of 4 key layers: ⬇️ 𝟭. 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗟𝗮𝘆𝗲𝗿 This is the user layer — customers and employees. McKinsey calls for fully reimagined, intelligent, personalized experiences across all channels. → Multimodal chat (text, voice, image) → Omnichannel UX across mobile, contact center, branch → Digital twins for customer simulation and workforce training It’s all about a UI refresh and UX overhaul grounded in real AI. 𝟮. 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗠𝗮𝗸𝗶𝗻𝗴 This is the brain of the AI-first bank. And it’s not just predictive models anymore — it’s orchestrated agent ecosystems. → AI Orchestrators: Plan, reason, delegate across workflows → Domain Agents: Specialize in credit policy, fraud, risk, legal → Copilots: Embedded in workflows to guide users and automate decisions McKinsey reports 20–60% productivity gains in decision-making with this approach. 𝟯. 𝗖𝗼𝗿𝗲 𝗧𝗲𝗰𝗵 & 𝗗𝗮𝘁𝗮 The foundation layer most banks underestimate — until GenAI models stall in production. → Vector databases → LLM orchestration and FinOps → Search and retrieval engines → ML pipelines → Secure data architecture → API infrastructure The goal: make data accessible, tools reusable, and infra invisible to the business. Without this, nothing scales. 𝟰. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 This is where the transformation wins or fails. Without rewiring the org, the tech doesn’t matter. → AI control towers to track value and set guardrails → Cross-functional teams across business, tech, and AI → Platform operating model for speed and alignment → Enterprise-wide reuse of AI capabilities If you're building isolated projects without shared assets or central coordination, you’re not transforming — you’re experimenting. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗮𝗹𝗹 𝗮𝗱𝗱𝘀 𝘂𝗽 𝘁𝗼? The banks that win won’t be the ones with the most pilots. They’ll be the ones that industrialize agents, orchestration, and rewired workflows, with full-stack coordination. Full McKinsey article: https://lnkd.in/dPaJzVK4 --- Need an AI Consultant or help building your career in AI? Message me now

AI just made its move into financial services. Anthropic announced a new tailored offering: Claude for Financial Services. Let’s break it down. • Claude connects directly to your internal data stack: Snowflake, Databricks, S&P, PitchBook, FactSet, and more. • It’s not a consumer chatbot. It’s a task-specific analyst, tuned for high-stakes environment. • It doesn’t train on your data. Privacy and compliance are foundational. • Oh yeah, and it can do Monte Carlo simulations. Where it creates value: • Investment teams can analyze portfolios, trends, and risk exposures in real time, without toggling across 12 dashboards or waiting on data prep. • Compliance and audit functions can use Claude to summarize regulatory updates, track adherence, and flag anomalies, before the next quarterly fire drill. • Client-facing teams can generate custom pitch decks, scenario models, and account insights on demand, without pulling an associate off a deliverable.    For CFOs • Increase visibility into financial drivers by asking natural-language questions across systems and models • Pressure-test scenarios in real time using up-to-date financial and macro inputs • Generate investor-ready insights faster and more consistently For FP&A Transformation leaders • Automate recurring analysis cycles such as forecast variance, budget rollups, and board package creation • Embed Claude into planning workflows to assist with driver modeling, commentary, and contextualization • Scale insight delivery without increasing headcount For GenAI Transformation leads • Operationalize AI within high-stakes workflows without reengineering existing systems • Launch proof-of-concepts with measurable productivity impact in under 90 days • Build a business case grounded in time saved, accuracy improved, and risk reduced Real results: • AIG accelerated underwriting by 80% while increasing data quality from 75% to 90% • Norway’s NBIM saved over 213,000 hours in a single deployment with a 20% productivity lift across finance teams If you’re leading a team inside a Fortune 500 and wondering where to start: Identify high-friction, high-repetition tasks in finance, ops, or risk. Don’t wait for a firm-wide transformation plan. Start small with one workflow Claude could automate or accelerate. Pilot. Measure. Expand. ----------------------- Follow me for GenAI Transformation, Training, and News.

𝐓𝐡𝐞 𝐑𝐨𝐚𝐝 𝐭𝐨 𝐆𝐞𝐧𝐀𝐈 𝐢𝐧 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 — everything you need to know 👇 — 𝐓𝐡𝐞 𝐃𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧: ► 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 (#GenAI) is a groundbreaking technology that embeds intelligence at every layer of financial services, transforming core banking functions, payments, fraud detection, and customer experiences. ► Unlike traditional AI models, GenAI works with both structured and unstructured data, making banking systems more predictive, interactive, and automated. ► 97% of banks have already adopted a GenAI strategy, but scaling it enterprise-wide remains a challenge due to regulatory hurdles and legacy infrastructure. — 𝐀 𝐍𝐞𝐰 𝐄𝐫𝐚 𝐢𝐧 𝐁𝐚𝐧𝐤𝐢𝐧𝐠: The GenAI Impact on Payments ► Payments are no longer just a back-office function; they are now a strategic advantage for businesses. ► GenAI is reshaping the payments landscape by enabling: ✔ Conversational checkout experiences (AI-driven assistants for seamless transactions) ✔ Automated transaction processing (reducing manual intervention & errors) ✔ Enhanced fraud detection (real-time anomaly detection) ✔ More personalized payment journeys (AI-powered recommendations & insights) ► With real-time payments becoming faster and more complex, banks need AI-driven automation to process transactions 30-40% faster and reduce errors by 70%. — 𝐓𝐡𝐞 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐟 𝐆𝐞𝐧𝐀𝐈 𝐢𝐧 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 🔹 𝐅𝐫𝐨𝐧𝐭-𝐄𝐧𝐝 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬: ✔ More streamlined checkout processes (i.e., AI-powered conversational checkout) – 44% ✔ Improved customer support & engagement – 44% ✔ More personalized transaction experiences – 41% 🔹 𝐁𝐚𝐜𝐤-𝐎𝐟𝐟𝐢𝐜𝐞 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬: ✔ Optimization of working capital decisions through better insights – 49% ✔ More accurate cash flow forecasting – 41% ✔ More efficient fraud detection & prevention – 41% ✔ Enhanced real-time analytics & reporting – 36% ✔ Stronger security measures – 21% — 𝐑𝐞𝐠𝐢𝐨𝐧𝐚𝐥 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐞𝐬 𝐭𝐨 𝐆𝐞𝐧𝐀𝐈 𝐢𝐧 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬, while GenAI adoption is global, priorities differ by region: 🌎 𝐔𝐒 & 𝐀𝐏𝐀𝐂: ► Focused on using GenAI for competitive advantage, with 53% of US and 54% of APAC banks prioritizing AI to differentiate in the market. 🇪🇺 𝐄𝐮𝐫𝐨𝐩𝐞: ► Primarily focused on operational efficiency, with 48% of banks leveraging AI to streamline workflows and optimize internal processes. 🇮🇳 𝐈𝐧𝐝𝐢𝐚: ► GenAI is being deployed for straight-through processing (STP) of payments, reducing manual intervention in high-volume transactions (83% adoption). 🌎 𝐋𝐀𝐓𝐀𝐌: ► Focused on streamlining payment operations, addressing inefficiencies and improving financial inclusion. — Source: NTT DATA — ► Sign up to 𝐓𝐡𝐞 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐁𝐫𝐞𝐰𝐬: https://lnkd.in/g5cDhnjC ► Connecting the dots in payments... & Marcel van Oost #AI #Payments #FinTech #Technology

🚀 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

5 Ways AI Is Reshaping Finance Right Now (Banks and financial firms are using AI to cut risks, boost profits, and make smarter decisions.) 1. Fraud Detection ↳ AI scans millions of transactions in real-time, flagging suspicious activity instantly. Banks using AI for fraud prevention have cut losses by 50%. 2. Algorithmic Trading ↳ AI-driven systems execute 60%+ of stock trades, reacting to market shifts in milliseconds. This improves accuracy, reduces human error, and maximizes returns. 3. Credit Risk Assessment ↳ AI-powered credit scoring analyzes thousands of data points, helping banks approve loans 30% faster while reducing default risk. 4. Personalized Banking ↳ AI chatbots and virtual assistants handle 80% of routine banking questions, cutting wait times and improving customer satisfaction. 5. Wealth Management ↳ AI-driven robo-advisors manage over $1 trillion in assets, offering smart investment strategies with lower fees. AI is transforming finance - are you using it to stay ahead? ______________________ AI Consultant, Course Creator & Keynote Speaker Follow Ashley Gross for more about AI

The future of banking isn’t just about digital transformation or AI innovation. The most successful institutions will need to combine both effectively. According to the Banking Disruption Index, 44% of U.S. consumers are open to integrating AI in their banking experience—but with clear caveats. Here are 3 key takeaways from using AI in banking: 1. Prioritize Real-Time Fraud Monitoring 👉 35% of consumers are looking for enhanced fraud protection, making it a top priority. 👉 AI in fraud detection enhances customer trust and security. 👉 Traditional banks can lead this effort, especially after last year's fraud surge. 2. Offer Tailored Savings Assistance 👉 90% of U.S. consumers want to save more in 2024, with nearly a third seeking AI assistance. 👉 Personalized savings advice enhances customer experience and delivers genuine value. 3. Understand Generational Preferences for AI 👉 Gen Z embraces AI in banking at 35%, while only 10% of Boomers remain skeptical. 👉 Tailoring AI applications to different age groups can boost adoption and satisfaction. 👉 Address Boomers' concerns while showcasing benefits for younger consumers. Traditional banks should prioritize AI in real-time fraud monitoring and savings assistance to maintain consumer trust and satisfaction. In a world where digital transformation is non-negotiable, the path to success lies in implementing AI that resonates with customer needs and preferences. What steps is your organization taking to integrate AI responsibly in banking? Let’s connect and share insights! #Banking #AI #DigitalTransformation #Fintech #CustomerExperience #Simform

AI's $2.4T Banking Revolution: The Hidden Story Behind 200,000 Job Cuts Here's what's really happening in banking's AI transformation → A deep dive that shocked me: The Scale is Staggering: → $770B value creation in Risk & Legal alone → $642B in Corporate Banking → $612B in Retail Banking But here's the fascinating part ↳ While headlines scream about 200k job cuts, the real story is about value creation: • Banks project 12-17% higher profits by 2027 • 54% of jobs will transform (not disappear) • 80% of banks expect 5%+ productivity surge The Hidden Pattern I Found 🔍 ↳ Back office automation isn't the end game ↳ Banks are using AI for value CREATION, not just cost-cutting ↳ JPMorgan's AI chief confirms: "AI is augmenting, not replacing" The Most Surprising Discovery: Risk & Legal functions → Highest AI value potential This suggests the real revolution isn't in customer service, but in the complex decision-making backbone of banking. Key Insight: We're witnessing the largest transformation of financial services since the 2008 crisis, but this time it's about building value, not just surviving. 🎯 My Prediction: Banks that view this as a transformation opportunity (not just cost-cutting) will capture the lion's share of the $2.4T value creation. 🔥 Want more breakdowns like this? Follow along for insights on: → Building with AI at scale → AI go-to-market playbooks → AI growth tactics that convert → AI product strategy that actually works → Large Language Model implementation Happy Sunday! #AI #Banking #FinTech #Innovation #DigitalTransformation #FutureOfWork