Banking Software Innovations

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

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.

API Banking Ecosystem Supporting Payment Services 💡 In the fast-evolving digital age, the landscape of payments, money movement, and financial transactions is undergoing a remarkable transformation. Customers increasingly expect instantaneity, accessibility, and unparalleled convenience from their digital applications. Financial services remain an exception, with high risk and switching costs, and a poor mobile experience does not necessarily drive consumers to the competition. As the Trading life cycle transitions from T+2 to T+1, and soon to a T+0-based same-day settlement, transparent access to trade data is regarded as table stakes. Powered by the Application Program Interface (API) micro-services architecture, money movement is undergoing a major transformation with adoption of real-time payment solutions and faster payment rails. Banks will need to build/enhance their API infrastructure to provide real-time access to bank account information, initiate transactions, and make core credit/debit updates ⏱ Now that innovative practices such as Banking as a Service (Baas) are allowing a diverse range of players such as Fintechs, third-party developers, and other businesses to innovate and provide value-added services on top of established banking infrastructure, the time is right for the world of wealth, retirement, and brokerage to take notice and begin to plan for the future of their business 🔎 The rapid rise of API Banking is forcing an evolution of the payments landscape, transforming the way customers and companies interact with money and unlocking innovations that were previously confined by legacy systems and manual processes. With its capacity to enable real-time 24/7 transactions, seamless integrations, and unprecedented levels of customization, API Banking is setting a new standard for financial services. By shifting to a relatively standardized, online based toolset, API Banking also facilitates collaboration between traditional financial institutions and third-party providers, blurring traditional distinctions and creating a dynamic ecosystem where the convenience, security, and innovation demanded by today’s diverse range of users can be met. As the payments ecosystem continues evolving, more companies will begin adopting and developing API Banking solutions and exposing their products and services to third parties. The next steps are ensuring data security, navigating regulatory landscapes, addressing integration complexities, and innovating. These vital aspects each demand careful consideration and mandate companies proactively make investments to remain competitive, differentiated, and relevant 👨💻 Source: Deloitte - https://t.ly/eddiS   #Innovation #Fintech #Banking #OpenBanking #API #BaaS #Microservices #FinancialServices #CoreBanking #Payments #Transaction #Clearing #Settlement

The Symbiotic Relationship Between Open Banking, Banking as a Service, and Embedded Finance In the rapidly evolving landscape of financial technology, three independent but interconnected concepts have emerged as drivers of innovation: 1️⃣ Open Banking and/or Open Finance 2️⃣ Banking as a Service (#BaaS) and/ or other business models incumbents will assume to monetise the opportunity, and 3️⃣ Embedded Finance. These paradigms are creating a #symbiotic #ecosystem that is starting to reshape the entire financial services industry. ⏹️ Open Banking: The Foundation Open Banking serves as the foundation of this ecosystem. It refers to the practice of banks sharing financial data and services with third-party providers through secure APIs. This initiative, often driven by regulatory changes, aims to increase competition, foster innovation, and improve customer experiences in the financial sector. 🔼 Banking as a Service: The Bridge Building upon the #infrastructure of Open Banking, #BankingasaService (BaaS) acts as a bridge between traditional banks and innovative fintech companies. BaaS providers offer a range of banking functions—such as account management, payments, and lending—as white-label services that can be integrated into other products or platforms. This allows non-bank entities to offer banking services without the need for a full #banking license or infrastructure. 🔼 Embedded Finance: The Ultimate Expression #EmbeddedFinance represents the culmination of these trends, seamlessly embedding financial services into non-financial products, platforms, or services. By leveraging #OpenBanking #APIs and BaaS offerings, companies across various industries can incorporate financial products directly into their customer journeys, creating more holistic and frictionless experiences. This symbiosis drives innovation, improves accessibility to financial services, and creates new revenue streams for both incumbent banks and non-financial companies. It also empowers consumers by offering more choice, personalization, and convenience in managing their financial lives. As this #ecosystem continues to evolve, we can expect to see even greater integration of financial services into our daily lives, blurring the lines between banking and other industries, and ultimately reshaping the very nature of finance itself. Arthur D. Little #fintech #payments #insurance #investments #loyalty #savings

What does responsible AI implementation at a bank looks like? The new report from Commonwealth Bank is a good read for those who may be curious. 🔍 Use AI to help reduce scams and fraud, and protect against phishing. The AI-driven Card Not Present Detection project resulted in $29 million reduction in potential financial losses. 💬 Gen AI assistant Compass AI delivers inquiries on their business bank knowledge base over 3x faster than traditional methods. ✍ Six AI principles grounded in the bank's Code of Conduct, Australia's AI Ethics Principles, and the OECD AI Principles, with the Board, Executive Leadership Team, and different management-level committees (including an AI Risk Committee) to maintain governance and accountability. 💻 Reskilling as a strategy, including an AI learning series for employees, a leadership learning program for senior leaders, an AI risk learning pathway (for AI-related risks and mitigations), and a tech hub. 🌐 One of my favorite use cases is their deployment of a special AI model to help identify digital payment transactions that include harassing, threatening, or offensive messages, which enables the bank to better protect and support victim-survivors of domestic and family violence and financial abuse. The pre-trained model is available to other FIs globally, and I've covered it in my book, Banking on (Artificial) Intelligence as well. #AI #BankingOnAI #FinancialServices #BankingIndustry

Overdue Open Banking post (#EmFi has been using all my 🧠 power recently!) What’s happening in the US right now deserves a spotlight. JPMorganChase has introduced a steep rate card for accessing customer data via API. It’s a direct attempt to squeeze fintechs and their customers. 🔷️ Context 🔷️ The Trump administration is working to roll back the Open Banking rule implemented by the CFPB. Not because they’re ideologically opposed to Open Banking, but because they want to reduce the size of the agency. While banks are suing it they can't reduce the headcount. They've indicated they’ll “do it better” eventually. 🔴 Impact 🔴 Fintechs and Open Banking platforms will face significantly higher costs to access consumer-permissioned data. This isn't theoetical. The cost of a new loan from an alternative lenders will go up. Pay-by-Bank payments will cost merchants more meaning less money to invest. Will budgeting apps survive? 🟤 JPM's Logic 🟤 Data is costly to maintain, secure, and serve. If others are going to monetise it, they should pay. But this justification is thin cover for a broader goal: restrict the portability of banking data so that it’s harder for competitors to serve customers or reduce payment costs. ⚠️Why is this problematic? ⚠️ Three reasons: 1️⃣ Data belongs to the customer. Your transaction history is yours. If you want to share it with a third party, you should be able to. The institution is merely a steward of your information. 2️⃣ Access to data drives competition. Fintechs rely on real-time data to underwrite loans, manage risk, and provide tailored services. Making that harder raises barriers to entry and reduces market efficiency and ultimately growth. 3️⃣ Raising API prices makes screen scraping more attractive. That’s bad. It’s insecure, unregulated, and less transparent. Ironically, the very thing Open Banking was meant to improve is being undermined by misaligned incentives. 🔭 The Big Picture 🔭 Large banks are protected by implicit government guarantees. They're essential to the system and so regulated accordingly. But that safety net also reduces pressure to innovate. Instead of building better products, they raise prices and resist change. That’s why Open Banking matters. It levels the playing field. It introduces real competition. And it ensures that innovation in financial services doesn’t come solely from protected incumbents. If the US rolls back #OpenBanking, the result is clear: consumers and businesses lose out, #GDP is lower than it could be, and innovation slows down. That’s why this isn’t just a technical debate. It’s an economic one. (Views my own, not those of any current or former employer.) #payments #openfinance #innovation #OpenEconomyConsulting

Banking infrastructure is entering a new phase where software no longer waits for instructions. The architecture described here around “Agentic Banking” points toward financial systems built for autonomous execution, where AI agents can reason, access tools, trigger workflows, monitor infrastructure, and operate across banking environments in real time. That changes the role of banking infrastructure itself. Core banking systems were originally designed around human-operated workflows and rigid procedural logic. But autonomous systems require something very different: API discoverability, real-time contextual data, continuous orchestration, and operational resilience that can react without waiting for manual intervention. One of the more important points in this piece is that agentic banking is not a standalone AI deployment problem. It becomes a full-stack engineering problem involving: • real-time data pipelines • cloud-native core modernization • AI-optimized compute infrastructure • autonomous AIOps and resilience layers • domain-specific model fine-tuning • token cost optimization • hybrid on-prem and cloud architectures Huawei also outlines some of the infrastructure requirements behind this transition, including AI-powered modernization with over 90% mainframe code transpilation adoption rates, architectures designed to support 10-fold traffic surges, and autonomous resilience systems targeting 99.999% availability. The operational implications are massive. An AI agent handling onboarding, treasury operations, fraud monitoring, customer servicing, or payment orchestration cannot operate on stale batch data or fragmented infrastructure. The system architecture itself starts becoming part of the intelligence layer. This is also where many banks may underestimate the scale of transformation required. Adding AI copilots on top of legacy systems is very different from building production-grade autonomous financial operations capable of executing actions safely across multiple systems in real time. Another interesting part of the article is Huawei’s focus on computing engineering, model engineering, and agentic engineering as separate operational disciplines, alongside its RONGHAI Global Partner Ecosystem strategy that connects infrastructure providers, ISVs, and system integrators to accelerate deployment across banking environments. The next generation of banking infrastructure will likely be shaped by institutions that can combine real-time orchestration, unified data foundations, resilient infrastructure, and specialized financial AI models into one operational stack. That may become one of the biggest competitive advantages in financial services over the next decade. Read the full article in the comments

AI is becoming a structural cost lever in banking, not an innovation experiment. Banks are no longer experimenting with AI, they’re looking to industrialise it to take structural cost out , lower cost-to-income, and speed up decisions. Using #genAI and #agenticAI to automate document-heavy credit and risk workflows, cut cycle times by up to 50%, and redeploy talent to higher‑value client work. The real competitive gap is emerging between leaders who can scale AI safely and those still stuck in pilots. The recent multi‑year strategic partnership between HSBC and French startup Mistral AI to use its large language models in a self‑hosted, bank‑controlled environment, is an example of what we are starting to see across our clients, a focus on achieving results with and scaling agentic AI use cases. The partnership is aimed at speeding up analysis of complex, document‑heavy financing and lending cases, with an ambition to cut review times roughly in half for #credit and financing teams. It will also power #multilingualreasoning and #translation, tailored client communications, #hyperpersonalisedmarketing, and broader #productivity tools used by HSBC staff globally.

AI is not transforming banking. It’s reinventing it. I’ve seen this shift unfold across multiple banking programs over the years. And the momentum today is unlike anything we have experienced before. For a long time, banks focused on going digital. But the next frontier is different. Banks are becoming intelligent systems. They are learning, adapting & predicting in real time. This is the architecture of the AI Bank of the Future. 1. Engagement: The Human Touch, Amplified : AI is not replacing conversations. It is elevating them. ↳ Every customer gets a personal journey shaped by real insights ↳ AI listens, learns & responds instantly ↳ Chat, voice & video that feel natural & helpful ↳ Employees get smart tools, not smaller roles Imagine a relationship manager preparing for a client meeting. An AI assistant summarizes past interactions, flags opportunities, & suggests the next best action. That is the new standard. Customer experience is no longer reactive. It’s predictive. 2. AI-Powered Decision Making: The Brain of the Bank : This is where intelligence becomes business impact. ↳ AI agents scan transactions, risks, & behaviors ↳ Fraud patterns are detected before damage occurs ↳ Predictive analytics identify needs customers have not expressed yet ↳ Decisions become faster, sharper & consistently accurate Think of a credit officer who gets a real-time explanation of why a loan looks risky, along with safer alternatives. That’s intelligence at scale. The bank begins to think continuously & proactively. 3. Core Technology & Data: The Beating Heart : No AI succeeds without the right foundation. ↳ Always-on machine learning & LLM pipelines ↳ Real-time enterprise data ↳ Vector databases & retrieval engines ↳ Clean, connected & unified data across the bank This is where many legacy systems struggle. If the core is not modernized, nothing above it can reach true potential. Silos collapse. Intelligence becomes the default. 4. Operating Model: The Cultural Shift That Decides Everything : This is the layer that separates fast-moving banks from slow-moving giants. ↳ Agile, cross-functional, AI-first teams ↳ AI control towers overseeing end-to-end processes ↳ Modern talent including data scientists, AI trainers & digital leaders ↳ An organization built to change, learn & adapt continuously This is the shift that turns AI from a project into the operating system of the bank. Here is the real truth - This is not a future vision. - This is already happening. Banks that embrace this model will: ✔ Understand customers deeply ✔ Identify risks early ✔ Move faster than legacy competitors ✔ Create new intelligence-driven revenue streams The winning formula isn’t 𝐀𝐈 𝐯𝐬 𝐡𝐮𝐦𝐚𝐧𝐬. It’s 𝐀𝐈 + 𝐡𝐮𝐦𝐚𝐧𝐬. That combination is the strongest force in financial services today. The AI Bank of the Future is already open for business. What do you think? Which layer creates the biggest competitive advantage? Follow Ashish Joshi for more insights

2026 is quietly being called the “year of Agentic AI” in banking and finance. Until now, AI in banks mostly meant chatbots, dashboards, or automation projects stuck in pilot mode. Now the conversation is changing.  Large banks and consulting firms are actively working on 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐭𝐡𝐚𝐭 𝐦𝐨𝐧𝐢𝐭𝐨𝐫 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬, 𝐭𝐫𝐢𝐠𝐠𝐞𝐫 𝐚𝐜𝐭𝐢𝐨𝐧𝐬, 𝐚𝐧𝐝 𝐚𝐬𝐬𝐢𝐬𝐭 𝐭𝐞𝐚𝐦𝐬 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬𝐥𝐲, rather than just generating responses. Oracle launched new agentic AI capabilities for banking platforms in early February. It enables banks to automate workflows across onboarding, service operations, compliance checks, and process monitoring. At the same time, global consulting firms like 𝐀𝐜𝐜𝐞𝐧𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐂𝐨𝐠𝐧𝐢𝐳𝐚𝐧𝐭 are pushing enterprise-wide adoption programs where AI agents support operations, customer servicing, and internal workflows. The interesting shift? Finance and FP&A are next in line. Because, changes are visible across: 1. Variance analysis that runs automatically every day instead of after month close. 2. Forecasts which adjust continuously as sales or cost signals change. 3. Scenario models which run automatically when assumptions move. 4. Liquidity or working-capital risks that get flagged early, not after reporting. Finance teams spend huge time assembling numbers.  Agentic AI changes the timing by changing outcomes. However, finance leaders need discipline here. They need to ask 3 important questions: 𝟏. 𝐃𝐨𝐞𝐬 𝐀𝐈 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 𝐨𝐫 𝐣𝐮𝐬𝐭 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐭𝐚𝐬𝐤𝐬? Headcount savings alone won’t justify investment. Better capital allocation and faster decisions will. 𝟐. 𝐖𝐡𝐨 𝐨𝐰𝐧𝐬 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 𝐰𝐡𝐞𝐧 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐜𝐭? Governance, auditability, and human oversight must be built in from day one. 𝟑. 𝐂𝐚𝐧 𝐀𝐈 𝐜𝐨𝐬𝐭𝐬 𝐬𝐩𝐢𝐫𝐚𝐥 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐬? Cloud AI usage without discipline can quietly inflate tech budgets. Agentic AI won’t replace finance teams. But finance teams using agentic AI may replace those who don’t. #AgenticAI #AIinBanking #FinancialServices #DigitalTransformation