AI systems are not a magic bullet.

The world is changing at a rapid pace, and AI is set to play a defining role. When thinking about AI for your business, don’t start with the technology. Start with the most pressing point, the challenge or opportunity that will make the biggest difference to your company. Do you want to: ✅ Work smarter or faster ✅ Deliver more personalised experiences for clients and employees ✅ Reduce costs ✅ Or achieve all of the above! Once you’re clear on the outcome to begin with, it helps identify the right use cases, select the best AI solutions, and partner effectively to deliver real impact. https://cnb.cx/48eIuC5

Learn how Anthropic's Sonnet 4.5 provides a specialized solution for the finance industry. This hybrid reasoning model leverages a deep knowledge of finance and prioritizes AI Safety. With capabilities like 64k output tokens and a verified low dishonesty rate of 6.90% in extended thinking mode, it is the current top performer on the Finance Agent evaluation, enabling secure and reliable autonomous workflows. #Sonnet45 #Anthropic #AIinFinance #FinTech #AISafety #LLM #FinanceAI #MachineLearning #DeepLearning #ClaudeAI #AImodel https://lnkd.in/dPjKyvPD

🚀 Ever wondered why your ML model struggles with minority classes? Let’s talk about one of the most powerful techniques to fix that — SMOTE (Synthetic Minority Oversampling Technique) 💡 In my latest experiment using the UCI Credit Card Default dataset, I trained a Random Forest model to predict who might default on their credit payments. Here’s what happened: 🔹 Before SMOTE Data was imbalanced (only 22% defaults) Accuracy looked good (81%), but the model missed many defaulters. 🔹 After SMOTE Both classes were balanced (equal “default” and “no default” cases). Recall for the minority class improved significantly. Model became more fair and sensitive to defaults, though accuracy dropped slightly — a fair trade-off for better generalization. 📊 Key takeaway: Balancing data isn’t just about numbers — it’s about ethical AI and real-world reliability. A model that predicts defaults correctly helps banks and customers avoid financial risk responsibly. 💬 Have you tried SMOTE or any other imbalance handling methods in your projects? What results did you get? #MachineLearning #DataScience #SMOTE #AI #CreditRisk #RandomForest #DataImbalance #ModelEvaluation

Integration = it will take years for the company to realize AI’s potential by stitching the cognitive power of AI models together with the bank’s proprietary data and software programs, said Waldron.

𝙈𝙤𝙣𝙞𝙩𝙤𝙧𝙞𝙣𝙜 𝙩𝙚𝙡𝙡𝙨 𝙮𝙤𝙪 𝙞𝙛 𝙮𝙤𝙪𝙧 𝘼𝙄 𝙞𝙨 𝙖𝙡𝙞𝙫𝙚. 𝙊𝙗𝙨𝙚𝙧𝙫𝙖𝙗𝙞𝙡𝙞𝙩𝙮 𝙩𝙚𝙡𝙡𝙨 𝙮𝙤𝙪 𝙞𝙛 𝙞𝙩’𝙨 𝙗𝙚𝙝𝙖𝙫𝙞𝙣𝙜 𝙡𝙞𝙠𝙚 𝙞𝙩 𝙨𝙝𝙤𝙪𝙡𝙙 𝙗𝙚! While I was reading the article “AI-Driven Observability in Financial Platforms” published in EA Journals (2025), one line really made me pause. “𝗔𝗜 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝘀 𝗺𝗼𝗱𝗲𝗹 𝗼𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁 𝗳𝗿𝗼𝗺 𝗮 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝗶𝗻𝘁𝗼 𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝘀𝘀𝗲𝘁.” That line hit differently because anyone who has worked with AI systems knows they can be a little unpredictable. They’re like that one overenthusiastic intern who starts off great, learns fast and then suddenly begins doing things no one asked for. In finance, that unpredictability isn’t just funny, it’s in fact quite expensive. Trust no longer comes from a banker’s signature or a firm handshake. It now lives inside the algorithms that decide who gets a loan, what looks like fraud and which transaction suddenly seems suspicious. These models run quietly, make millions of decisions and occasionally go off-script. That is where AI observability comes in. It is not just about knowing if your model is running, it is about understanding if it still remembers what you trained it to do. It lets teams trace decisions, spot early warning signs and make sure the model does not develop an identity crisis halfway through the quarter. The EA Journal's study found that financial institutions using observability frameworks reduced operational costs by 𝗻𝗲𝗮𝗿𝗹𝘆 𝟯𝟬% 𝗮𝗻𝗱 𝗮𝗿𝗼𝘂𝗻𝗱 𝗮 𝟰𝟮 𝗽𝗲𝗿𝗰𝗲𝗻𝘁 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗶𝗻 𝘀𝘆𝘀𝘁𝗲𝗺 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆. But beyond the numbers, it means something simpler. People were actually watching their models before the models embarrassed them! Imagine a credit scoring model that suddenly starts rejecting small business applications from one region. Everything looks fine, the accuracy scores are stable and nobody raises a flag. But after a festival season, customer spending patterns change and the model quietly drifts. Without observability, this would go unnoticed until complaints start pouring in. 𝗪𝗶𝘁𝗵 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗶𝘁 𝘄𝗼𝘂𝗹𝗱 𝗯𝗲 𝗰𝗮𝘂𝗴𝗵𝘁 𝗲𝗮𝗿𝗹𝘆, 𝗳𝗶𝘅𝗲𝗱 𝗾𝘂𝗶𝗰𝗸𝗹𝘆 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗴𝗼𝗲𝘀 𝗼𝗻 𝗮𝘀 𝘂𝘀𝘂𝗮𝗹. Traditional monitoring tells you if your AI is working. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗲𝗹𝗹𝘀 𝘆𝗼𝘂 𝗶𝗳 𝗶𝘁 𝗶𝘀 𝗯𝗲𝗵𝗮𝘃𝗶𝗻𝗴 𝗹𝗶𝗸𝗲 𝗮 𝗿𝗲𝗮𝘀𝗼𝗻𝗮𝗯𝗹𝗲 𝗮𝗱𝘂𝗹𝘁. And maybe that is the next step for fintech. Not just building smarter AI, but AI that can explain itself when things get weird. So here is what I have been thinking. 𝗔𝘀 𝘄𝗲 𝗴𝗶𝘃𝗲 𝗺𝗼𝗿𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝗽𝗼𝘄𝗲𝗿 𝘁𝗼 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀, 𝘄𝗵𝗼 𝗶𝘀 𝗿𝗲𝗮𝗹𝗹𝘆 𝗸𝗲𝗲𝗽𝗶𝗻𝗴 𝗮𝗻 𝗲𝘆𝗲 𝗼𝗻 𝘁𝗵𝗲 𝗼𝗻𝗲𝘀 𝗺𝗮𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗰𝗮𝗹𝗹𝘀? #ResponsibleAI #AIObservability #Governance

AI-driven, data-led innovation is reshaping financial services at every level. After reading Erica Andersen's excellent article, it's clear how AI is transforming customer experience, fraud detection, risk management, and investment strategies. 📈 Key highlights: 💡 Personalised CX - AI chatbots deliver tailored insights and proactive support. 🛡️ Fraud Prevention - Real-time data analysis detects and stops fraud instantly. 📊 Risk & Trading - AI models assess risk, predict volatility, and power smarter trading. A standout trend: the "clean-as-you-go" approach to data prep—focusing on cleaning data as needed rather than upfront. It's an agile, efficient shift that speeds up AI deployment and innovation. 🚀 #ArtificialIntelligence #DataInnovation #FinancialServices

If you work with AI + finance (or any data-driven domain), the takeaway is simple: don’t trust beautiful output, interrogate it. In my latest Medium piece, “Human-in-the-Loop Quant: An Anti-Overfitting Workflow for AI-Generated Trading Ideas”, I outline a four-gate validation rig that treats every AI idea like a suspect in an interrogation. (Spoiler: most don’t make it past gate two.)  Why this matters: generative models can spit out shiny strategies with perfect backtests, but many of those are overfitted mirages. My process forces you to crash test before you allocate capital. 

Day 01: The Age of AI 💡 Augmented Finance Finance runs on data. Data drives decisions across the financial services value chains and ecosystems informing tactical decisions such as customer segmentation, channel and distribution models support and service design. When we shift into the strategic plane, data (data models) help drive strategic decisions, highlighting shifts in macro trends such as systemic market and industry shifts, shifts in the internal and external risk complexion, capital allocation optimisation decisions and regulatory/prudential requirements (#BAII, #IFRS9, #BCBS239). The growth in volume and velocity data Traditional tools can’t keep up. Here are four areas where AI can make a difference 👇 ✅ Sharper insights: uncover hidden patterns in Interest and FX markets, customer behavior microsegments, emergent value networks and communities. ✅ Smarter risk management: augmented credit scoring, fraud detection, and stress testing and simulations beyond rule-based models. ✅ Operational efficiency: large scale robotics and intelligent automation across reporting processes, reconciliation, compliance and payment orchestration using agents, process discovery and design. ✅ Decision support: Generative AI powered research assistants, speech to text models for real time sentiment analysis, hyper-personalisation (staff and customers) 👉 Augmentation is the goal. An AI augmented industry player can deliver solutions which are cheaper (inclusive), safer, faster and more innovative solutions at scale. An AI augmented industry can foster trust, transparency, inclusion and mitigate systemic risks across the ecosystem. -------- This series will unfold as follows: Over the next 89 days, I’ll share daily insights on how AI is reshaping finance & technology. This series will include a weekly application which brings some of these ideas to life. Data: I have tried to use locally available data to bring the application into our local context, however where local data is missing and or sparse, I have employed proxy data from publicly available and proprietary global datasets. #AI #Finance #RiskManagement #MachineLearning #DataDriven#AgeOfAI#AugmentedFinance#ScalingAI

🚀 AI isn’t just for Wall Street anymore. From automated chart pattern recognition to instant market summaries, retail traders now have access to AI tools that can save time and sharpen decision-making. But what’s hype and what’s actually useful today? 🤔 👉 In our latest blog post, we break down 3 practical ways traders can use AI right now — without coding skills or expensive infrastructure. Read here: https://lnkd.in/dYt2pJPz #AITrading #Forex #CFD #TradingTools #MarketAnalysis 61% of retail CFD accounts lose money.

We're engineering our AI agent, Luca, around a single, non-negotiable principle: Trust requires proof. In finance, that means every automated action must be transparent, traceable, and verifiable. The hardest part of building AI for finance isn't making it smart. It's engineering the system around it to ensure total accountability. Here's what that looks like in practice: First, transparency of logic. An auditor shouldn't need a PhD in computer science to understand how a rule works. Our agent is being built to automatically generate plain-English narratives explaining its own logic, complementing the visual workflow diagrams. You see exactly what it's doing and why. Second, absolute traceability. A final number is useless without its full lineage. We're building the capability to trace any output back through every transformation step to the original source transaction. This isn't just about finding errors - it's about providing an unbreakable chain of evidence. Third, continuous validation. Audits shouldn't be a year end scramble. We're empowering Luca to act as its own QA, automatically testing its own work, sampling transactions, and proactively flagging exceptions in real-time. Compliance becomes a continuous state, not a periodic event. This isn't just about adding features. It's about architecting a system where auditability isn't an afterthought - it's the fundamental operating principle.