AI in Financial Services

AI is becoming a make-or-break factor for banks. But success will not depend on their ability to offer #AI, but on their competence in integrating it. Let’s take a look.   Banking is forecasted to feel the biggest impact from generative AI among sectors and industries as a percentage of their revenues with the additional value calculated between $200 bn and $340 bn annually (source: McKinsey). But why is the impact so powerful? One of the main reasons is because the abrupt surge of gen AI is exponentially increasing the speed with which #banking is being transformed. That is not to say that the transformation has started with or due to AI. On the contrary: during the past 10 to 15 years banking was already in the middle of transforming from a human-based, relationship-first industry to a more automated and technology-driven business following the #fintech revolution and the ascend of nimbler and more innovative competitors. But AI now does 2 things: —  It brings the transition to a new level, across 3 dimensions: speed, outcome and impact. —  It turbo-charges one of the biggest challenges in modern FS: the combination of AI and data that brings under the same roof two inherently opposing forces: mass and customization. In other words, AI seems to find a credible answer to achieving hyper-personalization. In a recent report Deloitte has provided realistic examples on how this is done across both cost efficiency and income growth: Cost efficiency: —  Workforce acceleration efficiencies across the board: 0–15% of total staff cost —  IT development and maintenance acceleration: 10–20% of IT staff cost —  Improved credit-risk assessment leading to 10-15% savings in impairment charges —  Improved FinCrime/fraud detection reducing litigation/redress charges and fraud losses Income growth: —  Next generation market analysis / predictive trading algorithms: 5–7% uplift on trading income —  Improved customer retention: 1–2% uplift on fees & commissions —  Improved customer acquisition through hyper-personalised marketing: 5-10% uplift from interest income and fees & commissions —  Tailored loan pricing based on credit risk assessment: 2–3% increase on net interest income Despite all the excitement around these estimated benefits, success will not be a walk in the park. It will depend on the banks’ ability to integrate AI in a seamless way into their day-to-day operations. Going forward AI will be re-writing much of the scenarios and use cases of the banking value chain. That doesn’t necessarily mean that they will all be different, but most will certainly be enhanced with impact spanning both across the back-end and the front-end. Given that resources are limited, one of the main challenges will be how to identify the ones to focus on. Factors such as #strategy, potential impact and a match with the existing skillset should be guiding the selection process.   Opinions: my own, Graphic source and use cases: Deloitte

AI agents may face one of their first real stress tests in the office of the CFO. A lot of AI can still be vague and get away with it. Finance can’t. Here, agents are judged less by how fluent they sound, and more by whether the output is accurate, traceable, and usable in real workflows. That’s part of why Sema4.ai is an interesting example here. It’s building at the enterprise agent platform layer, but the value becomes much clearer when that platform gets applied to a specific function like the Office of the CFO. In that setting, the work is very concrete: reconciliation, accounts payable, payment processing, remittance matching, and financial documents. 𝐈𝐭’𝐬 𝐬𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐥𝐚𝐲𝐞𝐫 is trying to make that more workable by letting teams: → query financial data in natural language across databases, spreadsheets, and documents → ground analysis in SQL-powered calculations so outputs are tied to logic that can actually hold up → turn financial documents into structured, queryable data instead of leaving critical information trapped in PDFs and reports That is where this starts to get more interesting. Not just AI that sounds useful in finance, but AI built to operate closer to 𝐭𝐡𝐞 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐬 𝐟𝐢𝐧𝐚𝐧𝐜𝐞 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐬. That’s the difference between something that sounds good in theory and something that can actually hold up in a real finance environment. Sema4.ai publicly highlights outcomes such as raising auto-match rates 𝐟𝐫𝐨𝐦 𝟐𝟎% 𝐭𝐨 𝟖𝟎%+, improving invoice processing by up to 4x, and reducing some workflows from 24–48 hours to around 10 minutes. 📍Read more here: https://lnkd.in/gpF7eeaW

We spent a decade building lending apps nobody wanted to use. Then AI picked up the phone. Bajaj Finance just announced their AI voice bots will disburse ₹5,300 crore in FY26. That’s not a pilot—that’s the new playbook. Here’s what everyone is missing: True digital transformation in financial services is NOT about digital journeys with bad UX. It’s about natural language interfaces with AI. The whole app-based, portal-driven self serve digitization wave? It mostly flopped Why are AI voice bots actually working now? Two reasons: India is massively credit-starved (huge demand) + Voice is how Indians actually prefer to transact. For document-heavy loans like LAP, I see voice + WhatsApp bots becoming the killer combo. Voice builds trust. WhatsApp handles documents. The outcome: Lower operational costs → Lower lending rates → Better credit access → Industry efficiency. Bajaj is targeting 90% reduction in service workloads and 50% drop in operations costs by FY30. The lenders who crack natural language interfaces will own the next decade of Indian lending. What’s your take? Are we finally seeing real digital transformation in financial services?

McKinsey & Company 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗼𝗿 𝗵𝗼𝘄 𝗯𝗮𝗻𝗸𝘀 𝗰𝗮𝗻 𝗲𝘅𝘁𝗿𝗮𝗰𝘁 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗔𝗜 ↓ 𝟭. 𝗛𝘆𝗽𝗲𝗿-𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 AI enables banks to move from one-size-fits-all services to fully personalized experiences at scale.  • Multimodal conversational banking (text, voice, video)  • Personalized product recommendations (credit, savings, investments)  • Proactive nudges (fraud alerts, savings reminders, financial wellness tips) → Direct value: Higher customer loyalty, better cross-selling, and increased lifetime value. 𝟮. 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗠𝗮𝗸𝗶𝗻𝗴 Banks can embed AI agents, copilots, and autopilots into daily workflows.  • Faster and more accurate credit decisioning  • Real-time fraud detection and transaction monitoring  • Automated legal, tax, and compliance assistants → Direct value: Reduced risk exposure, faster turnaround times, and improved regulatory compliance. 𝟯. 𝗡𝗲𝘅𝘁-𝗚𝗲𝗻 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 By using predictive and generative AI models, banks can anticipate needs and act before customers ask.  • Predicting churn and offering targeted retention strategies  • Optimizing collections with personalized repayment plans  • Intelligent upselling/cross-selling at the right moment → Direct value: Increased revenues, lower default rates, and more efficient operations. 𝟰. 𝗖𝗼𝗿𝗲 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 AI value is unlocked only if backed by robust data and infrastructure:  • Vector databases + LLM orchestration for knowledge retrieval  • Automated MLOps for faster deployment of models  • Secure, compliant, and scalable data pipelines → Direct value: Lower cost-to-serve, faster innovation cycles, and stronger resilience. 𝟱. 𝗔𝗜-𝗘𝗻𝗮𝗯𝗹𝗲𝗱 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 AI is not just a tool, it reshapes how banks operate.  • Autonomous business and technology teams using AI orchestration  • AI “control towers” monitoring value creation across the bank  • Agile ways of working + culture of continuous learning → Direct value: Sustainable transformation, measurable ROI, and ability to compete with fintech disruptors. 𝗕𝗮𝗻𝗸𝘀 𝘁𝗵𝗮𝘁 𝘀𝘂𝗰𝗰𝗲𝗲𝗱 𝘄𝗶𝘁𝗵 𝗔𝗜 rewire their enterprise for impact. They go beyond isolated pilots and build the solid data and technology foundations needed to scale. They embed trust and responsible use into every decision, while reimagining customer engagement to be seamless, personalized, and always-on. AI won’t transform banks. Banks will transform with AI.

Will Accounting Be Replaced? 🤖 💼 Everyone's asking if AI will replace accountants... Let me settle this once and for all. ➡️ WHAT WILL TRANSFORM ADVISORY SERVICES are becoming the heart of what we do. Gone are the days when accountants just crunch numbers. Now we guide strategic decisions using real data insights. Companies need advisors who understand both numbers AND business strategy. FORENSIC ACCOUNTING gets supercharged with advanced analytics. Finding fraud used to be like searching for a needle in a haystack... With AI-powered anomaly detection, we spot patterns humans would miss. The fraudsters are getting smarter, but so are our tools. AUDIT & RISK ASSESSMENT will never go away, but everything about it is changing. Instead of sampling transactions once a year, we're moving to continuous auditing with real-time data. AI review systems flag issues as they happen, not months later when it's too late. FINANCIAL ANALYSIS & FORECASTING is where accountants shine brightest. Sure, AI can run calculations, but humans bring context to numbers. Our forecasting is getting enhanced by predictive analytics and scenario modeling that processes variables faster than ever before. CLIENT COMMUNICATION is shifting completely. We're moving from transaction processors to trusted advisors. ➡️ WHAT WILL BE REPLACED Let's be honest... some parts of accounting are tedious and perfect for automation. MANUAL DATA ENTRY is already on its way out. AI-driven data capture and OCR tools process invoices and receipts in seconds, without the errors humans make after hours of monotonous work. ROUTINE BOOKKEEPING tasks are getting automated through cloud accounting software. Bank feeds, automatic categorization, and machine learning mean the days of manually reconciling every transaction are numbered. BASIC TAX PREPARATION for standard situations will be handled by smart platforms. E-filing tools get smarter every tax season. The complex tax strategy work? That's still all us. INVOICE MATCHING & RECONCILIATION is perfect for automation. AI bots can match thousands of invoices to purchase orders in minutes, with real-time reconciliation systems keeping everything in sync. COMPLIANCE MONITORING no longer needs accountants to manually check every rule. Automated alerts and built-in compliance checks flag issues instantly, letting us focus on solving problems rather than finding them. ➡️ THE FUTURE ACCOUNTANT The accountants who will thrive aren't fighting against technology... They're embracing it. The future belongs to those who combine technical accounting knowledge with: - Strategic thinking - Business acumen - Technology fluency - Communication skills === What parts of your accounting job do you think will change the most with AI? Which skills are you developing to stay ahead? Join the discussion in the comments below 👇

Most insurance companies don’t have a product problem. They have a 𝐬𝐢𝐠𝐧𝐚𝐥 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. Trouble shows up early for customers… and late for leadership. McKinsey’s 2025 analysis shows that only a small fraction of insurers capture meaningful value from AI and the reason isn’t model quality. It’s because 𝐝𝐚𝐭𝐚 𝐬𝐢𝐭𝐬 𝐢𝐧 𝐬𝐢𝐥𝐨𝐬 across underwriting, claims, support, and policy servicing. Another study highlights that predictive analytics when actually integrated can reduce loss ratios, speed up claims, and improve risk accuracy. But most insurers never reach that stage because their systems can’t surface early patterns. So what happens? A spike in confusion calls. Customers misusing features. Renewal expectations not matching policy reality. Claim friction rising quietly for weeks. By the time these signals hit dashboards, the damage is already in motion: lower NPS, rising churn, operational load, regulatory exposure. This is why insurance needs an 𝐈𝐂𝐔 - 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐂𝐨𝐫𝐫𝐞𝐜𝐭𝐢𝐨𝐧 𝐔𝐧𝐢𝐭. A team that: 1. Connects disparate data into a single, queryable layer. 2. Builds early-warning models for churn, fraud, sentiment, and claims delay. 3. Flags mismatches between expectation and experience in real time. 4. Routes insights directly into underwriting, ops, and customer teams. When insights arrive early, transformation doesn’t arrive late. And in insurance, 𝐭𝐡𝐞 𝐞𝐚𝐫𝐥𝐢𝐞𝐬𝐭 𝐬𝐢𝐠𝐧𝐚𝐥 𝐢𝐬 𝐭𝐡𝐞 𝐮𝐥𝐭𝐢𝐦𝐚𝐭𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐭𝐨 𝐰𝐢𝐧. #InsuranceIndustry #DataAnalytics #CustomerExperience #PredictiveAnalytics

One thing I’m seeing clearly across Singapore and the region: payment choice is exploding, and expectations are rising just as fast. Consumers don’t want more cards or options for the sake of it. They want control. The ability to switch, optimise rewards, use instalments, or pay with points, all with visibility and confidence. When that experience feels fragmented, trust erodes quickly. From my perspective, the real gap for many banks isn’t just infrastructure. It’s decisioning. Turning data into real-time, personalised experiences that genuinely help people make better financial choices. AI is accelerating this shift. As AI begins to guide purchasing decisions, products must be discoverable, accurate and trusted within those journeys. At the same time, AI can remove friction within banks, streamlining servicing, dispute resolution, and everyday interactions. At Visa, our focus is on helping institutions bridge insight and execution, so choice feels simple and secure. #CustomerExperience #AI #FutureOfFinance

In today’s fast-evolving financial landscape, artificial intelligence (AI) has become indispensable for private wealth management. By automating data analysis, AI enables personalized, data-driven investment strategies that enhance client portfolios, minimize risks, and increase overall efficiency. For Monaco, a hub of global finance and private wealth, AI offers immense opportunities: • Predictive analytics help identify market trends, allowing more agile and informed decision-making. • Enhanced customer experiences through personalized financial planning, improving satisfaction and retention. • Risk management using AI-driven insights to forecast and mitigate potential financial risks. • Operational efficiency, reducing costs through automation and enabling Monaco’s financial professionals to focus on high-value services. By embracing AI, Monaco’s financial industry can not only strengthen its competitive edge but also continue to provide world-class services to its international clientele. This technological shift is key to positioning the Principality as a leader in the future of finance. #Monaco #AI #WealthManagement #FinanceInnovation #FutureofFinance #PrivateBanking #DigitalTransformation Monaco For Finance Robert LAURE

🚨Inside Indian fintech's new AI playbook: Why Model Context Protocol is gaining popularity Indian fintechs like Zerodha, Razorpay, and Fi Money are building a new layer of infrastructure that lets AI tools act on real financial data securely, in real-time, and with context. It’s called Model Context Protocol (MCP), and it's quietly emerging as the bridge between AI assistants like ChatGPT and internal company systems.* Instead of using dashboards or navigating APIs, users can now talk to AI agents in plain language to check their portfolio, ask for spending summaries, or even initiate payment workflows. “AI tools have become so good that you don’t need a UI anymore,” Zerodha CEO Nithin Kamath wrote in a recent post, sharing screenshots of users querying their investments via AI assistants on the Kite platform. MCP is what makes that possible. It acts as a secure protocol layer, a wrapper over existing APIs, that lets AI assistants access company data or perform actions, but only with user authentication and full control. Think of it as plugging your private data into a smart, conversational interface, but without the privacy risks of just pasting information into large language models like ChatGPT or Gemini. Companies like Razorpay, PayU, and Cashfree are among the early adopters. They are integrating AI assistants to handle tasks ranging from generating payment links to initiating refunds, all with just a simple prompt. "To have intelligent, personalised financial recommendations, you need two things: powerful AI and live, accurate data. That’s why fintechs are building MCPs,” said Tanuj Bhojwani , an independent technology expert. At Zerodha, India’s largest stockbroker, investors can now query their portfolio using natural language through AI assistants like Claude and Cursor, running backtests or analysing stock movements in conversation-like exchanges. According to Bhojwani, MCP does not require deep AI expertise or large infrastructure investments. “Very honestly, it doesn't cost much. It's just a wrapper around existing APIs, with AI coding, a developer can set it up in less than a day or two,” he said. “You can ask AI any personalised question about your financial data,” said Sumit Gwalani, co-founder of Fi Money. “People are calling it their CFO or their CA.” Caution and guardrails As MCP becomes more mainstream, the risk of sensitive data exposure has raised red flags. Zerodha CTO Kailash Nadh cautioned against over-reliance on AI-driven decision-making, especially when users begin delegating trading actions to opaque AI systems. By Bhavya Dilipkumar https://lnkd.in/gd2XBzgi

Most finance leaders talk about “the future of Finance”. Very few have a concrete 𝘱𝘭𝘢𝘯 for their Finance Function 2035. Here's how to make it: If AI will run almost all transactional work, the real question becomes: How do you redesign Finance so humans can focus on impact? Here’s a simple step‑by‑step guide you can use with your team: 𝟭. 𝗦𝗲𝘁 𝗮𝗻 𝗔𝗜‑𝗳𝗶𝗿𝘀𝘁 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻: For every task, ask: “How 𝘤𝘰𝘶𝘭𝘥 AI do this?” Assume automation by default. Humans must justify why they still do the work. 𝟮. 𝗠𝗮𝗽 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸 𝗶𝗻𝘁𝗼 𝟯 𝗯𝘂𝗰𝗸𝗲𝘁𝘀: List all activities under: Compliance, Control, Advisory. This gives you a clear view of where value is (and isn’t) created. 𝟯. 𝗗𝗲𝘀𝗶𝗴𝗻 𝘆𝗼𝘂𝗿 𝗿𝗲𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗿𝗼𝗮𝗱𝗺𝗮𝗽: Decide, by 2035, what % of each bucket should be done by AI vs humans. Then build a year‑by‑year shift from today to that target mix. 𝟰. 𝗥𝗲𝗱𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗙𝗶𝗻𝗮𝗻𝗰𝗲 𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻: Clarify the role of Operational, Specialized (e.g., FP&A, Tax), and Business Finance. Make Business Finance (and some Specialized Finance) the “home” of human strategic work. 𝟱. 𝗖𝗼𝗺𝗺𝗶𝘁 𝘁𝗼 𝗮 𝘀𝗸𝗶𝗹𝗹𝘀 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗳𝗼𝗿 𝗵𝘂𝗺𝗮𝗻𝘀: Select your critical human skills (e.g., analytical thinking, complex problem‑solving, leadership, creativity, active learning). Turn them into a concrete learning agenda, not a slide. 𝟲. 𝗥𝘂𝗻 𝗔𝗜‑𝗵𝘂𝗺𝗮𝗻 𝗽𝗶𝗹𝗼𝘁𝘀: Pick one process in each bucket and redesign it with AI in the lead and humans in the loop. Document time saved, quality improved, and decisions enhanced. 𝟳. 𝗥𝗲𝘃𝗶𝗲𝘄 𝗮𝗻𝗱 𝗿𝗲‑𝗮𝗹𝗹𝗼𝗰𝗮𝘁𝗲 𝗮𝗻𝗻𝘂𝗮𝗹𝗹𝘆: Every year, re‑assess tasks, skills, and structure. Finance 2035 is not a one‑time design; it’s a decade of deliberate redistribution. P.S. Save this as your checklist for the next Finance leadership offsite, and bring it along when you discuss what your Finance Function 2035 should really look like.