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.

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

The AI Revolution in Banking: Forcing Banks to Upgrade their Tech Infrastructure for a New Era The adoption of AI in banking is experiencing hyper adoption. According to a report by Business Insider Intelligence, about 80% of banks are highly aware of the potential benefits of AI. The global AI in banking market size is expected to grow from $3.88 billion in 2020 to $64.03 billion by 2030 As banks rush to integrate #AI into their operations, they face significant challenges with their existing infrastructure. #Legacy systems, which form the #backbone of many banking operations, were not designed to handle the data volumes, processing speeds, and algorithmic complexities required by modern AI applications. This mismatch is forcing #banks to reevaluate and upgrade their infrastructure: 🔌 Advanced Data Analytics Platforms: Banks need robust platforms capable of handling structured and unstructured data from various sources. 🔌 High-Performance Computing (HPC) Infrastructure: To train and run complex AI models, banks require significant computational power. This may involve investing in GPU clusters or leveraging #cloud based HPC solutions. 🔌 Cloud-Native Architecture: Adopting a cloud-native approach allows banks to scale their AI capabilities efficiently, enhance agility, and reduce #infrastructure costs. According to a leading SI firm, 82% of bank executives agree that the cloud will become the dominant #digital delivery platform for banking within the next two years. 🔌 AI Model Management Systems: As banks deploy more AI models, they need sophisticated #systems to manage model lifecycles, ensure model accuracy, and maintain #regulatory compliance. 🔌 Robust #API Management: A well-designed API strategy is crucial for #integrating AI capabilities across different banking systems and partnering with #fintech companies to enhance service offerings. 🔌 Enhanced #Cybersecurity Measures: Banks must implement advanced security measures, including AI-powered threat detection systems, to protect their AI #infrastructure and maintain customer trust. 🔌 Explainable AI (XAI) Tools: To address regulatory requirements and build customer trust, banks need tools that can explain AI decision-making processes, especially in areas like #creditscoring and #risk assessment. While the potential benefits of AI for banking are immense, realizing them requires a significant overhaul of existing banking infrastructure. Banks that successfully navigate this #transformation by investing in the right #technologies and reimagining their infrastructure will be well-positioned to thrive in the AI-driven future of finance. Those that fail to adapt risk falling behind in an increasingly competitive and #technology driven industry. As we look to the future, it's clear that the integration of AI in banking is not just about adopting new #technologies—it's about reimagining the very foundation of banking #operations for the digital age. #cloudcomputing #generativeai

AI Can Fuel Improvements in the Retail Banking Onboarding Process for Cards 💡 Customer onboarding in banking is the first touchpoint crucial for transforming a prospect into a customer. One of the most considerable challenges they face during customer onboarding is dealing with incomplete information from customers: indeed, 75% of them report that customers often submit incomplete documentation causing significant delays. The overwhelming amount of paperwork required also often leads to mistakes or missing details, forcing bank staff into a frustrating cycle of back-and-forth communication with customers. The potential for AI is to alleviate these challenges is enormous; it can automate reminders via email, SMS, and phone calls to prompt customers about missing or incomplete documents, ensuring timely submissions and reducing delays. AI systems can also collect and analyze structured or unstructured data from varied sources and improve decision-making by eradicating biases and providing consistent outcomes 🚀 AI-powered fraud detection systems significantly enhance the speed and accuracy of identity verification and fraud detection. They flag applications with unusual behavior, such as mismatched personal details or suspicious IP addresses. Advanced facial recognition and biometric verification prevent impersonation and fraud, ensuring compliance with regulatory requirements. This boosts security and reduces the time and effort required for manual verification processes. AI algorithms match customer photographs with government-issued identification documents, verifying identities with high accuracy and minimizing fraud risk 🛡 AI can also analyze regulatory data, identify compliance requirements, and monitor banking operations. This proactive approach mitigates the risk of non-compliance by flagging potential compliance violations. In addition, AI-powered reporting tools generate comprehensive compliance reports, streamlining auditing processes. New-age banks use AI to enhance compliance and streamline operations 🤖 AI-powered intelligent documentation processes can manage and accelerate the processing of large volumes of documents. Optical character recognition (OCR) technology can automatically extract from images and convert it into machine-encoded text. This eliminates the need for manual data entry, speeds up the processes, and reduces errors. AI-powered risk-scoring models process vast datasets, identify patterns, and make accurate predictions. By analyzing historical data, AI uncovers insights that human analysts might miss, enabling personalized, fair credit assessments. It can also extend credit to underserved populations by incorporating alternative data like spending habits, income and employment. Source: Capgemini - https://shorturl.at/V90l4 #Innovation #Fintech #Banking #FinancialServices #Cards #Payments #KYC #Onboarding #Compliance #AI #Data #OCR #GenAI #Biometrics

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

What CTOs in Banking Should Do with AI for Customer Experience A few months ago, I sat with the CTO of a major bank who shared a familiar frustration: “We’ve invested millions in AI, but our customer experience hasn’t improved the way we expected.” I asked a simple question: “Are you using AI to solve real customer pain points, or are you using it because it’s expected?” That conversation led us down a path that many banking leaders are navigating today—leveraging AI not just for efficiency, but to truly enhance customer relationships. AI and the Future of Banking Customer Experience The global AI in banking market is expected to reach $130 billion by 2030, growing at a CAGR of 32% (Allied Market Research). This isn’t just about chatbots or fraud detection anymore; AI is redefining how banks engage with customers at every touchpoint. McKinsey reports that banks effectively using AI can increase customer satisfaction by 35% while reducing operational costs by up to 25%. The challenge, however, is execution—CTOs must ensure AI is seamlessly integrated into both digital and human interactions. How Leading CTOs Use AI for Customer Experience 1- Hyper-Personalization Example: JPMorgan Chase uses AI to analyze customer behavior and provide real-time loan and investment suggestions, increasing engagement by 40%. 2- AI-Powered Virtual Assistants Example: Bank of America’s Erica, an AI-powered assistant, has handled over 1.5 billion interactions, offering personalized financial insights. 3- Predictive Analytics for Proactive Engagement Example: A European bank using AI-driven insights reduced customer churn by 22% by proactively addressing financial concerns. 4- AI-Enhanced Fraud Detection Example: Mastercard’s AI-based fraud prevention has reduced false declines by 50%, improving trust and security. A Real-World Impact: AI in Action One of our banking clients struggled with high customer complaints about slow loan approvals. By integrating AI-driven document verification and risk assessment, approval times dropped from 5 days to 5 minutes. The result? A 30% increase in loan applications and a significant boost in customer satisfaction. The Human-AI Balance in Banking Despite AI’s capabilities, customers still value human interaction. 88% of banking customers want a mix of AI-powered convenience and human support when dealing with financial decisions (PwC). The key for CTOs is to balance automation with empathy—ensuring AI enhances, rather than replaces, the personal touch. The Road Ahead AI is no longer a futuristic concept in banking—it’s a strategic necessity. CTOs who embrace AI for customer experience, not just efficiency, will lead the industry forward. At Devsinc, we believe the future of banking isn’t just digital—it’s intelligent, personalized, and deeply customer-centric. The question is, are we using AI to replace transactions, or to build trust? Because in banking, trust isn’t just a feature—it’s the foundation.

AI in Banking: The Smartest Bets for Real Impact The window for experimenting with AI is closing fast banking leaders are now expected to deliver tangible outcomes. The good news? A clear pattern is emerging around where AI consistently drives value. High-impact, high-feasibility use cases are leading the charge: • Internal AI copilots for contact centers: Not just speeding up service, but enhancing empathy and resolution through real-time insights. • Enterprise knowledge assistants: Turning data noise into curated intelligence for RMs, compliance, and marketing teams alike. • Smart compliance (AML, transaction monitoring): Replacing reactive checks with proactive, precision-led oversight. • Retail credit underwriting: Moving beyond FICO to more inclusive, risk-aware lending powered by alternative data. • GenAI for code generation: Compressing development cycles and unlocking new digital experiences at pace. Strategic value comes in four flavors: • Efficiency: Strip out manual steps. Accelerate processes across onboarding, fraud detection, and document handling. • Customer experience: Contextual, timely, humanized—even when it’s machine-powered. • Risk mitigation: Smarter signals. Fewer false positives. Stronger regulatory posture. • Revenue growth: Hyper-personalization, intelligent segmentation, and sharper product fit. For forward-thinking banks, the opportunity isn’t just in deploying AI—but in orchestrating it across silos to create compounding value. The winners won’t be those who try to do everything—but those who know exactly where to start. #AI #BankingStrategy #GenAI #CustomerExperience #RiskManagement #FinancialInnovation #DigitalExecution