AI in Financial Services

How can businesses go beyond using AI for incremental efficiency gains to create transformative impact? I write from the World Economic Forum (WEF) in Davos, Switzerland, where I’ve been speaking with many CEOs about how to use AI for growth. A recurring theme is that running many experimental, bottom-up AI projects — letting a thousand flowers bloom — has failed to lead to significant payoffs. Instead, bigger gains require workflow redesign: taking a broader, perhaps top-down view of the multiple steps in a process and changing how they work together from end to end. Consider a bank issuing loans. The workflow consists of several discrete stages: Marketing -> Application -> Preliminary Approval -> Final Review -> Execution Suppose each step used to be manual. Preliminary Approval used to require an hour-long human review, but a new agentic system can do this automatically in 10 minutes. Swapping human review for AI review — but keeping everything else the same — gives a minor efficiency gain but isn’t transformative. Here’s what would be transformative: Instead of applicants waiting a week for a human to review their application, they can get a decision in 10 minutes. When that happens, the loan becomes a more compelling product, and that better customer experience allows lenders to attract more applications and ultimately issue more loans. However, making this change requires taking a broader business or product perspective, not just a technology perspective. Further, it changes the workflow of loan processing. Switching to offering a “10-minute loan” product would require changing how it is marketed. Applications would need to be digitized and routed more efficiently, and final review and execution would need to be redesigned to handle a larger volume. Even though AI is applied only to one step, Preliminary Approval, we end up implementing not just a point solution but a broader workflow redesign that transforms the product offering. At AI Aspire (an advisory firm I co-lead), here’s what we see: Bottom-up innovation matters because the people closest to problems often see solutions first. But scaling such ideas to create transformative impact often requires seeing how AI can transform entire workflows end to end, not just individual steps, and this is where top-down strategic direction and innovation can help. This year's WEF meeting, as in previous years, has been an energizing event. Among technologists, frequent topics of discussion include Agentic AI (when I coined this term, I was not expecting to see it plastered on billboards and buildings!), Sovereign AI (how nations can control their own access to AI), Talent (the challenging job market for recent graduates, and how to upskill nations), and data-center infrastructure (how to address bottlenecks in energy, talent, GPU chips, and memory). I will address some of these topics in future posts. [Original text: https://lnkd.in/gbiRs2mi ]

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

500+ execs shared with Google the ROI of AI Agents in Finance Here are the core things every leader should learn... Finance is one of the major adoption markets for Agentic AI. If you've been keeping up with my AI Agents updates, You will see Perplexity, Claude, ChatGPT, and others launching agents for financial services. But how big is the impact? Or the ROI of these agents in Financial Services? 📌 Google + National Research Group surveyed 500+ Senior leaders to identify the ROI. Among a lot of details, here are the 5 things they've found: 1/ Fraud & Risk Management - 40%+ of banks use AI agents to detect fraud and manage risk, finding 2–4x more suspicious activity and reducing compliance costs. 2/ Customer Experience - 42% of firms deploy agents for 24/7 query handling. 3/ Finance & Accounting - Over a third use agents to automate reporting and reconciliation, freeing analysts. 4/ Client Onboarding & KYC - 4 in 10 firms use agents to verify identities faster + ensure smoother compliance. 5/ Security Operations - Nearly half of the surveyed firms use agents for threat detection and faster response. These are not made-up numbers but rather defined by surveying those who have actually been put into production. (Bonus) Here is "The AI agent ROI checklist" from Google for easier adoption for every organization: 1/ Find executive champions - Secure C-suite sponsorship to drive AI initiatives, clear roadblocks, and align teams to measurable ROI. 2/ Demonstrate business value early - Show quick, tangible results to justify AI budgets and gain stakeholder confidence. 3/ Create an AI rulebook "now" - Establish clear enterprise-wide policies for data privacy, compliance, and responsible AI use. 4/ Start with high-impact, repeatable tasks - Focus on automating simple, repetitive workflows where ROI is easiest to prove. 5/ Build trust and internal capability - Keep a human-in-the-loop, ensure secure data access, and invest in AI education for your teams. Check out the entire report for Statements and strategies from Deutsche Bank, Scotiabank, and much more. Full report in comments 👇 Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents

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?

AI is reshaping how mortgage brokers work Not in theory. In real business workflows. I recently interviewed Richard Wang - A true mortgage industry expert ↳ JD, MBA, CPA, lifelong loan originator, ultra athlete, true wine connoisseur, master networker, giver... Honestly, the list could fill a page ↳ Combines legal and finance background with deep lending expertise ↳ Runs Veridian Mortgage LLC with an awesome team operating across 6 states Here are some sharp insights from Richard: ↳ AI tools now extract data from tax returns and loan documents in minutes ↳ Brokers can upload a competitor’s loan estimate and instantly generate smarter client options ↳ Some lenders, like United Wholesale Mortgage, have launched ChatGPT-style tools for loan guidance ↳ AI assistants are now handling client calls, scheduling, follow-ups and routine queries Key takeaway AI is no longer optional in residential finance It is becoming core to how brokers compete and deliver better service The next 12 to 18 months will separate those who adopt early from those who fall behind 🔔 Follow Gaurav (Rav) Mendiratta for weekly updates on how AI is transforming real-world businesses #AI #Mortgage #RealEstateTech #SmallBusiness #Innovation #DigitalTransformation

AI agents are disrupting wall st. 📈 The next Goldman Sachs might be built by engineers, not bankers: Right now, AI Twitter is full of "We built a hedge fund with AI!" Most of them are doing basic things badly. But here's the thing about mediocre MVPs in finance: They have a habit of becoming industry behemoths. ⛰️ Three paths are emerging: 1. AI-Augmented Firms (already live) 2. AI-First Firms (coming soon) 3. AI-Agent-only Firms (wild but possible) 👩💻 Today's AI Agents are like MS-DOS: • Powerful in expert hands • Useless for everyone else • Great demos, terrible products But a handful are figuring out how to deliver consistency. That's the key. 👉 Why capital markets are perfect for AI • Massive unstructured data (PDFs, emails, Excel) • Complex domain knowledge • High-value repetitive tasks • Very expensive analysts to replace Disruption is coming. While traditional firms debate AI governance in boardrooms, crypto is running live experiments with AI agents managing real money. $10M+ under AI management might sound small. But that's how disruption starts. 🏰 Old moats are crumbling: • Scale matters less when AI replaces 100 analysts • HFT and statistical advantages are replaced by the ability to analyze every market data signal (supply chains, earnings, news) • Advantage moves from quants to AI analysts But new ones are emerging... 🌱 The real revolution happens when: • AI-first asset managers hit $1T AUM • AI agents run autonomous treasuries • Machines, not committees, allocate capital Today's experiments look primitive. But so did every financial innovation at first. Remember: Stablecoins were once a crazy crypto thing too. ✍️ I wrote a deep dive on this transformation on my newsletter Fintech Brainfood (find it on my profile) Follow me for more analysis on how AI is reshaping finance. What do you think? Is finance ready for AI-native institutions?

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.

AI is transforming financial services. It’s also transforming financial crime. A recent global analysis reported that banks and insurers are now facing a new wave of 𝐀𝐈-𝐞𝐧𝐚𝐛𝐥𝐞𝐝 𝐟𝐫𝐚𝐮𝐝, 𝐜𝐲𝐛𝐞𝐫𝐚𝐭𝐭𝐚𝐜𝐤𝐬, 𝐚𝐧𝐝 𝐭𝐡𝐢𝐫𝐝-𝐩𝐚𝐫𝐭𝐲 𝐯𝐮𝐥𝐧𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 as they digitize core operations. And the risk curve is steep. Deepfake transactions. Synthetic identities. Model-driven phishing. Automated credential stuffing. Real-time manipulation of underwriting or claims workflows. In parallel, IBM’s 2024 Cost of a Data Breach Report found that 𝐟𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐫𝐞𝐦𝐚𝐢𝐧𝐬 𝐨𝐧𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭-𝐭𝐚𝐫𝐠𝐞𝐭𝐞𝐝 𝐬𝐞𝐜𝐭𝐨𝐫𝐬, with breach costs exceeding 𝐔𝐒𝐃 𝟓.𝟗𝐌 𝐩𝐞𝐫 𝐢𝐧𝐜𝐢𝐝𝐞𝐧𝐭 on average. It implies, AI won’t just accelerate legitimate operations. It will accelerate criminal ones. And this is where leadership matters. Because customers don’t just evaluate financial institutions on product or price. They evaluate them on 𝐭𝐫𝐮𝐬𝐭, the confidence that their data, identity, and money are safe in an increasingly automated world. That’s why AI adoption must move hand-in-hand with: 1. Clear governance frameworks 2. Transparent decision systems 3. Continuous monitoring of model behaviour 4. Strong third-party risk controls 5. Human-in-the-loop safeguards for high-impact decisions AI can make financial systems smarter. But only governance makes them trustworthy. In the next decade, 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐚𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 𝐰𝐨𝐧’𝐭 𝐛𝐞 𝐀𝐈 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐲, 𝐢𝐭 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐀𝐈 𝐢𝐧𝐭𝐞𝐠𝐫𝐢𝐭𝐲. #FinancialServices #AIGovernance #CyberSecurity

20 AI Use Cases for Finance AI is no longer “nice to have” in finance — it’s already transforming how we analyze, forecast, and report. Here’s what you can do with AI today: Financial Statement Analysis – spot trends, ratios, and red flags instantly Forecasting & Scenario Planning – build multi-scenario models in minutes Budget Variance Analysis – explain deviations automatically KPI Dashboard Creation – generate visual dashboards with no manual work Cash Flow Forecasting – run rolling projections with accuracy Working Capital Optimization – detect liquidity risks and improve cash cycles Cost Structure Analysis – classify and benchmark expenses and more. 🛠️ Tools you can use for these use cases: ChatGPT – analysis, commentary, draft presentations, expense insights Genspark – full 3-statement models, DCF, dashboards, scenario planning Perplexity – due diligence, benchmarking, industry trends, risk checks Claude – budget analysis, investor memos, structured outputs Excel Copilot – AI-driven formulas, variance analysis, forecasting Power BI with AI add-ins – dashboards, anomaly detection, visualization 💡 Bottom line: AI won’t replace finance professionals — but it will replace hours of manual work with sharper insights, better storytelling, and faster decisions. 👉 Check the visual below for a complete list of 20 AI use cases in finance. AI is already transforming reporting, forecasting, and modeling. The question is — will you adapt or fall behind? 👉 Join the 𝗖𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗙𝗶𝗻𝗮𝗻𝗰𝗲 𝗛𝘂𝗯 and stay ahead with AI-powered finance. https://bojanfin.com/ 👉 Or, if you only want to sharpen your 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 skills, start with my dedicated course. https://lnkd.in/dpi_5gAa Cheers, see you inside.

AI has been everywhere for three years. In strategy decks. In board conversations. In earnings calls. In billion-dollar capex announcements. But here is the real question for 2026: Where is the productivity? McKinsey now calls 2026 the first true bottom-line year for AI. Many companies have begun budgeting for AI-driven productivity gains in their financial plans. That is a shift from experimentation to expectation. At the same time, capital markets are showing signs of impatience. Massive investments from the Magnificent 7 have not yet translated into a broad productivity surge. The narrative is strong. The macro data is still catching up. For finance leaders, this tension matters. Because the real transformation is not technical. It is professional. Finance must move from doing the work to qualifying the work, and ultimately into a judgment role. When AI agents build models, generate scenarios, and draft commentary, the value of finance lies in interpreting their meaning and deciding how to act on them. In the article, I explore: • Why 2026 may mark the shift from AI pilots to P&L accountability • How AI agents are redefining FP&A, audit, and M&A workflows • What must finance do to become the scoreboard for AI value creation • Why judgment, not modeling, becomes the core capability If you are a CFO or FP&A leader, this is not a technology discussion. It is a leadership discussion. The question is simple: Will finance shape the AI agenda, or consume its outputs? I would be interested in your perspective. Have you budgeted AI productivity gains for 2026, or are you still in experimentation mode?