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

Building Blocks For A Digital Bank 💡 The first key element is to find out how you wish to be regulated. Most greenfield banks built by existing banks will rely on the umbrella of their existing parent's banking license. However, new entrants in the sector will need to be regulated themselves to play and compete. A multitude of options are available: 1️⃣ The Banking License If you want to become a fully licensed bank, you need a banking license. The banking license is the almighty of all licenses, basically allowing you to provide all the services you typically expect from a bank (i.e. current accounts, overdraft) and extend to more advanced activities like granting loans or mortgages. 2️⃣ The E-money License Regulators have made it easier to launch new banking-like services by creating a new kind of license – The E-money license. The EMI allow you to store money on an electronic device or remotely on a server, effectively creating a 'digital account' or an 'e-wallet'. These e-wallets can support all types of payment services-like features - which looks and feels almost exactly like a traditional account offered by your bank. Now Let's Build Your Platform... 🚀 The next step is to assemble your platform, which will constitute the backbone of your architecture. A platform is essentially made of 3 layers: 👨💻 Starting with The Core The first phase and most important building block to a digital bank is the the Core Banking. The Core Banking is the heart of a bank, basically the engine behind the creation of accounts, balances, transactions, loans, interest rates, journal entries along with the storage of client data, receipts and other reporting tools. New cloud-based next providers have recently emerged, with stronger focus on serving new digital services at a much more affordable installation price. Next generation Core Banking specialists (like Mambu, Thought Machine, and Skaleet) are usually based on a monthly subscription fee model based on utilization instead of heavy project costs. 👨🔧 Complemented by a Middle & Front-End Another crucial part of the infrastructure is the middleware, which resides on top of your back-end or Core Banking infrastructure as an add-on, and acting as a system orchestrator connecting all layers together, eventually reducing the need to involve your Core Banking in every single customer interaction. Additionally, such platforms allow you to either build you very own user experience (or front-end) or use their white-label front-end solutions. Once again, considering to leverage an existing front-end applications from an existing vendor may lowest your risk and come down to a most cost-effective solution, at least for your initial launch. Sources: Nickolas B. x FinTech Insights - https://bit.ly/46y9tVt Germain Bahri - https://bit.ly/401NvI2 #Fintech #Banking #DigitalBanking #Neobanks #OpenBanking #OpenAPIs #BaaS #FinancialServices #CoreBanking #CreditCards #Payments #Loans #KYC #AML #Wallets

🚀Traditional Bank to Digital Bank With traditional customer journey includes traveling to branches to talk with a customer service representative, assembling pages of paper documents to meet KYC requirements, & then waiting for days to receive decisions I am blessed with actual implementation in and out of banking while making Digital Bank from Its Traditional behavior ;  Definitely, The transformation from traditional banking to digital banking involves numerous changes across various aspects of the banking industry Let's understand ? 💡 Technological Advancements: Rapid advancements in technology, such as the internet, mobile devices, cloud computing, and data analytics, have paved the way for digital banking 💡 Changing Customer Expectations:Customers now expect convenient and on-demand banking services 24/7 availability, user-friendly interfaces, and personalized experiences 💡Platform banking - Evolution of Platforms with-in Bank and Outside of banking e.g. adoption of Banking as Service , Public Digital Infrastructure is Boost 💡Integration of Financial Services: Digital banking has facilitated the integration of various financial services into a single platform including instant a/c opening 💡 Payments & Transactions: Digital banking enables fast and secure electronic payments and transactions. 💡Personalized Services: Digital banking leverages customer data and analytics to offer personalized financial services 💡Security & Fraud Prevention: Digital banking implements robust security measures to protect customer data and transactions 💡FinTech Integration: The digital banking transformation involves collaboration with financial technology (FinTech) companies 💡Customer Support: Digital banking provides multiple channels for customer support, including online chatbots, virtual assistants, and video conferencing. 💡Data Analytics: The transition to digital banking generates vast amounts of customer data. 💡 Regulatory Compliance: Digital banking requires adherence to evolving regulatory frameworks related to cybersecurity, data privacy, anti-money laundering, and consumer protection. 💡 Infra Transformation: Movement of Infra from Data Center to So called Cloud-centric Data center with better mechanism to manage and monitor Infra Banks are Changing and It's important to note that the extent and pace of digital banking transformation may vary across different regions and institutions, as customer preferences, technological infrastructure, and regulatory environments can influence the speed of adoption Transformation is , the change is with leadership, Employees , Process Automation, Technology Adoption, Architecture Change, regulatory compliance, Support Systems, Partners, and ultimately for everchanging Customer Experience! Do you have suggestions, Please comment

A snapshot of Open Banking across the globe🌍 🏦 Open Banking is gaining impressive traction around the globe. Initially championed by Europe's PSD2 and the UK's Open Banking Standard, conceived three years ago by the Competition and Markets Authority, it's exciting to see that now over 50 nations are embracing the potential of open banking. Various models are emerging: 1️⃣ Regulatory-Driven Approach: Predominantly inspired by Europe and the UK, countries like Australia, Canada, Hong Kong, Brazil, Mexico, Bahrain, and Saudi Arabia are leveraging mandated API standards and data-sharing norms. Highlight: 🇦🇺Australia stands out in this group. Not only did the country’s Prudential Regulatory Authority mandate open banking for its principal banks (akin to the UK model), but it also simultaneously introduced the Consumer Data Right. This innovative initiative empowers citizens to ask not just financial bodies but also enterprises in sectors like energy and telecom to share their data with third-party service providers. 🇨🇦Canada, too, is evolving uniquely. The Banker’s Association in Canada is honing in on digital identity, laying the groundwork for an Open Banking Framework. The nation is now deep into the second leg of its exploration into "consumer-directed finance". 2️⃣ Market-Driven Approach: Here, countries like the U.S., China, and India are trailblazers. This model is primarily spurred by agile fintech startups in the U.S.🇺🇸 and the potent blend of payment disruptors and ecommerce behemoths in Asia. A captivating case study is China🇨🇳. Here, powerhouses such as Alipay and WeChat Pay leveraged their vast social media, gaming ecosystems, and expansive user base to integrate financial services seamlessly. The necessity was a unique one: in a region with low credit card usage, these giants needed to innovate. The outcome? Advanced financial offerings ranging from wealth management to AI-driven lending. The regulatory ambiance was favorable, and the consumers were open to innovation. A testament to their success is Alipay's audacious ambition: to captivate two billion users globally in the forthcoming decade. In essence, the global momentum of Open Banking, whether steered by regulations or the market, signals a transformative era in the financial domain, where data, innovation, and customer-centricity will define the next wave of financial solutions. I highly recommend downloading the complete #fintechreport “Open Banking and the Rise of Banking- as-a-Service” by Temenos for more interesting info on this topic: https://lnkd.in/eJy8QkWF Find this helpful? [ 𝗿𝗲𝗽𝗼𝘀𝘁 ] Anything to add about this subject? [ 𝗶𝗻𝘃𝗶𝘁𝗲𝗱 𝘁𝗼 𝗰𝗼𝗺𝗺𝗲𝗻𝘁 ] Nice story, Marcel. Next! [ 𝗹𝗶𝗸𝗲 ] #fintech #openbanking #openfinance #financialtechnology #digitalbanking #banking #bankingindustry #fintechinnovation #fintechindustry 

𝑩𝒆𝒆𝒏 𝑻𝒉𝒆𝒓𝒆, 𝑫𝒐𝒊𝒏𝒈 𝑻𝒉𝒂𝒕: 𝑯𝒐𝒘 𝑮𝒆𝒏 𝑨𝑰 𝒈𝒆𝒕𝒔 𝒖𝒔𝒆𝒅 𝒊𝒏 𝒃𝒂𝒏𝒌𝒊𝒏𝒈 Wholesale banks, experienced in traditional AI, are capitalizing on generative AI, offering new productivity benefits. In my experience, long-standing users of AI, Corporate and Investment Banks (CIBs) have used machine learning and NLP for trade prediction and data analysis. Now, generative AI is scaling up, potentially adding $𝟐𝟎𝟎-$𝟑𝟒𝟎 𝐛𝐢𝐥𝐥𝐢𝐨𝐧 across banking through enhanced productivity, according to McKinsey Global Institute (MGI). It aids in creating initial drafts, improves natural-language understanding, and boosts productivity in core CIB activities by 30 to 90 percent, possibly increasing CIB operating profits by 9 to 15 percent. CIBs are applying generative AI in new product development, customer operations, and marketing and sales. In compliance, gen AI can synthesize regulatory reports for review. Tools like Broadridge’s BondGPT2 provide bond insights and liquidity updates. Innovators like JPMorgan Chase & Co. and Morgan Stanley are creating gen AI tools for equity selection and real-time customer service. Others, however, remain tentative due to computing costs or intellectual-property issues. The impressive fintech landscape demonstrates the use cases for banking and where big banks can find partners. Effective #genai integration in banks demands a strategic roadmap, cost consideration, talent management, data optimization, and technology upgrades. But risks such as algorithmic bias, privacy issues, security threats, ESG impact, and computing costs must be managed. The whole business model of banking might need a revamp. #financialservices #AI #Innovation #technology #fintech Dive deeper in comments

India's MSME sector is the backbone of the economy, contributing nearly 30% of GDP and 45% of exports. Yet, access to finance remains a key challenge, with many small businesses struggling to secure loans due to traditional risk assessment methods. This is where AI-driven credit scoring and financial analytics are changing the game. Banks and fintech firms are leveraging alternative data sources, including transaction history, GST filings, digital payments, and even social media activity, to assess creditworthiness more accurately. 🔹 AI-powered credit scoring – Moving beyond collateral-based lending, AI evaluates a business’s financial health using real-time data, enabling faster and more inclusive loan approvals. 🔹 Cash flow-based lending – Traditional credit scores often fail to capture the potential of MSMEs. AI helps lenders analyze cash flows, supplier payments, and inventory cycles to assess loan eligibility. 🔹 Fraud detection & risk management – AI models detect anomalies in financial behavior, reducing loan defaults and improving underwriting efficiency. 🔹 Customized financial products – Fintech platforms use predictive analytics to offer tailored loan structures and repayment plans, making credit more accessible. The impact? Faster loan approvals, reduced NPAs, and a thriving MSME sector that can scale efficiently. As India embraces digital transformation, data-driven lending is unlocking new opportunities for small businesses, driving financial inclusion and economic growth. 𝑯𝒐𝒘 𝒅𝒐 𝒚𝒐𝒖 𝒔𝒆𝒆 𝑨𝑰 𝒕𝒓𝒂𝒏𝒔𝒇𝒐𝒓𝒎𝒊𝒏𝒈 𝑴𝑺𝑴𝑬 𝒍𝒆𝒏𝒅𝒊𝒏𝒈 𝒊𝒏 𝒕𝒉𝒆 𝒏𝒆𝒙𝒕 𝒇𝒊𝒗𝒆 𝒚𝒆𝒂𝒓𝒔? #DataAnalytics #DataDrivendecisionmaking #AiinMSME #MSMElending

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At Rich Data Co we saw a gap in the market for banks to better utilise their customers’ transaction data to understand the financial health of their business and commercial customers. AI plays a key role in predicting the cashflow health of businesses. This enables bankers to understand their customer’s past, present and most importantly, their future. With these capabilities, bankers are able to do 3 things:    1️⃣ Seeing warning signs in real time: RDC applies AI to transaction data to identify cashflow deterioration. Cashflow is a leading indicator for early warning, while many other factors are lagging behind business operation problems. Many banks rely on risk rating changes to identify early warnings. This could be triggered by a review of financial statements (often 18 months old), behaviour data changes or banker judgement. While all these factors are important, they are likely to be too late given it is backward looking. This is like comparing driving a car looking out the front window vs. looking at the rear view mirror.  2️⃣ Identify lending opportunities: A businesses cash flow position goes through ups and downs, especially seasonal businesses such as retailers. The prediction of cashflow health allows bankers to look into the future and provide lending to customers when they need it the most. This also allows banks to assess loan suitability to lend responsibly. Banks need to assess how the business can pay the loan back with the cashflow it generates, i.e. the primary source of repayment. Lending to businesses with a strong cashflow will be less risky for banks and provide affordable loans for the business to grow. 3️⃣ Improving efficiency in the customer review: Continued assessment of customer risk enables banks to drive efficiency in their customer review obligation required by the regulators.   This is a paradigm change in how banks manage their business and commercial lending portfolio. We have seen enlightened banks embracing and leveraging AI to realise significant benefits for both the bank and their customers. This paradigm change moves banks from assessing credit risk only a few times, to ongoing. This is like comparing banks taking a static picture of their customers’ financial health vs. making a movie by ongoing observation of their customers. Static picture vs. a movie of a customer's financial health, which one do you think would be more accurate and timely?     The difficulty in applying AI in this domain is how to achieve cashflow prediction accuracy to a banks lending standard. If you'd like to hear more details, RDC is always open to chat.   https://lnkd.in/gjshBwbb   #FutureOfCredit #MachineLearning #ArtificialIntelligence  

70 million new virtual bank accounts in Indonesia. Over 30% of Hong Kong’s population fully online. A single virtual bank in Brazil valued at $70B. The digital banking revolution is here—and Thailand is the next destination! The Lightnet-WeLab consortium, vying for a virtual banking license in Thailand, aims to bring true financial inclusion to the country’s underserved—particularly the 52.7% labor force in the informal economy with an estimated informal debt of THB16.3 trillion. Why Virtual Banks Matter - In Hong Kong, eight virtual banks licensed in 2019 achieved a 77% CAGR in depositors and 147% CAGR in loans. - Indonesia now boasts over 70 million accounts opened across 15 virtual banks, with loan disbursements rising by 22%. - Brazil’s NuBank, the largest virtual bank worldwide, is valued at around THB2.4 trillion, dwarfing the combined market cap of Thailand’s four biggest banks. What’s Different for Thailand? Purpose-Built Products Example: Bank Saqu in Indonesia introduced “saving pockets” in a single account, achieving a 60% monthly active rate and onboarding nearly 2 million customers - 40% of whom are informal workers. Similar localized offerings could transform Thailand’s retail and MSME segments within 12 months if a license is granted. Responsible Credit WeLab has issued over $15B (THB512B) in loans with AI-driven underwriting, beating Hong Kong’s banking delinquency average by six times. By leveraging multi-dimensional data (even drone imagery for agricultural borrowers), the consortium can extend credit where traditional banks can’t. Ecosystem Synergy Tapping into Lightnet’s 46 million users across agriculture, F&B, and e-commerce plus 150,000 physical touchpoints will close the access gap and spark healthy competition in Thailand’s banking market.

Your AI project will succeed or fail before a single model is deployed. The critical decisions happen during vendor selection — especially in fintech where the consequences of poor implementation extend beyond wasted budgets to regulatory exposure and customer trust. Financial institutions have always excelled at vendor risk management. The difference with AI? The risks are less visible and the consequences more profound. After working on dozens of fintech AI implementations, I've identified four essential filters that determine success when internal AI capabilities are limited: 1️⃣ Integration Readiness For fintech specifically, look beyond the demo. Request documentation on how the vendor handles system integrations. The most advanced AI is worthless if it can't connect to your legacy infrastructure. 2️⃣ Interpretability and Governance Fit In financial services, "black box" AI is potentially non-compliant. Effective vendors should provide tiered explanations for different stakeholders, from technical teams to compliance officers to regulators. Ask for examples of model documentation specifically designed for financial service audits. 3️⃣ Capability Transfer Mechanics With 71% of companies reporting an AI skills gap, knowledge transfer becomes essential. Structure contracts with explicit "shadow-the-vendor" periods where your team works alongside implementation experts. The goal: independence without expertise gaps that create regulatory risks. 4️⃣ Road-Map Transparency and Exit Options Financial services move slower than technology. Ensure your vendor's development roadmap aligns with regulatory timelines and includes established processes for model updates that won't trigger new compliance reviews. Document clear exit rights that include data migration support. In regulated industries like fintech, vendor selection is your primary risk management strategy. The most successful implementations I've witnessed weren't led by AI experts, but by operational leaders who applied these filters systematically, documenting each requirement against specific regulatory and business needs. Successful AI implementation in regulated industries is fundamentally about process rigor before technical rigor. #fintech #ai #governance