Applications of Machine Learning in Finance

Your AI Copilot Isn’t Replacing You — It’s Promoting You 🚀 Remember when Excel first landed in offices? The people who mastered it didn’t get replaced. They got promoted. We’re living through that moment again—only now, it’s with AI. Your AI copilot—whether it’s ChatGPT, Claude, or a custom tool—isn’t here to take your job. It’s here to multiply your impact. Take my week, for example: 🧠 Summarized a 20-page whitepaper in 90 seconds ✍️ Drafted 3 client emails—in my voice, not some generic template 💡 Reframed an investor pitch deck using insights from a different industry None of that replaced me. It amplified me. And what I’m seeing personally? It’s happening at scale in fintech. AI in Fintech: Quiet Revolution, Massive Impact The same AI that’s helping me move faster is now transforming how fintech operates — not someday, but right now. 1. Smarter Risk Management ↳ AI flags fraud in real time, predicts loan defaults before they happen. ↳ JPMorgan cut false positives in fraud detection by 40%. 2. Personalization That Actually Works ↳ Hyper-relevant offers, proactive chatbots, AI-driven wealth advisors. ↳ Result? 5–10% uplift in revenue through more engaged customers. 3. Less Ops, More Innovation ↳ KYC checks, compliance reviews, documentation—automated. ↳ Your team spends less time chasing files, more time chasing growth. PwC predicts over $1 trillion in AI-driven value for financial services by 2030. Deloitte shows major gains in both cost reduction and revenue growth. This isn’t just an upgrade. It’s a shift in how fintech runs. At Netevia, we are already making this a reality. We are currently integrating AI into two core fintech processes: risk assessment and underwriting. These processes are being enhanced with AI to improve accuracy, speed, and decision-making. This integration enables our teams to focus on higher-level insights while AI handles complexity at scale. 💬 If you treat AI as competition, you’ll get left behind. 💡 If you treat it as a collaborator, you’ll move ahead. So let’s make this real: How are you using AI as your copilot? Drop your favorite use case in the comments—let’s crowdsource the next fintech playbook. #AI #Fintech #FutureOfWork #ArtificialIntelligence #ChatGPT #Productivity #CareerGrowth #BankingInnovation

5 Ways AI Is Reshaping Finance Right Now (Banks and financial firms are using AI to cut risks, boost profits, and make smarter decisions.) 1. Fraud Detection ↳ AI scans millions of transactions in real-time, flagging suspicious activity instantly. Banks using AI for fraud prevention have cut losses by 50%. 2. Algorithmic Trading ↳ AI-driven systems execute 60%+ of stock trades, reacting to market shifts in milliseconds. This improves accuracy, reduces human error, and maximizes returns. 3. Credit Risk Assessment ↳ AI-powered credit scoring analyzes thousands of data points, helping banks approve loans 30% faster while reducing default risk. 4. Personalized Banking ↳ AI chatbots and virtual assistants handle 80% of routine banking questions, cutting wait times and improving customer satisfaction. 5. Wealth Management ↳ AI-driven robo-advisors manage over $1 trillion in assets, offering smart investment strategies with lower fees. AI is transforming finance - are you using it to stay ahead? ______________________ AI Consultant, Course Creator & Keynote Speaker Follow Ashley Gross for more about AI

AI for Finance Leaders: TL;DR Edition. AI for Smarter Forecasting: The Power of Sentiment Analysis We all wish we had more time to dive into the research on AI and think about how it will impact us, so today we’re doing just that.  Xiaowei Zhao, Yong Zhou, Xiujuan Xu, Yu Liu recently published a paper called “Extensible Multi-Granularity Fusion Network for Aspect-based Sentiment Analysis” or more simply “Super Smart Tech That Knows What People Really Think”. Let’s break it down to understand what the technology means and how I imagine it could impact the role of finance in the organization. What’s the paper about?   Imagine a super smart tool that reads through thousands of online reviews to find out exactly what people love or don't about a movie, down to the smallest details like the storyline or the graphics. This magic tool is powered by the latest tech called the EMGF (Extensible Multi-Granularity Fusion) network, which is like a detective that can understand feelings and opinions in writing. It's not just about knowing if people liked the movie, but understanding every single part they talked about. Why does it matter for finance? It could be easy to dismiss this technology in finance, but this gives us the ability to analyze written subjective content in mass to create more consistent and accurate forecasts. Here are a few ways that I could see finance tools using this tech to improve your workflow. Practical applications: CRM & Call recording Insights  Imagine using this technology to read through every recorded call with a prospect or customer. Often in sales forecasting, reps use their gut to determine whether they think a deal will move to the next stage. Once you have more than one rep, the consistency starts to plummet. Some are sandbagging, some are looking at the deal with rose-colored glasses, and the results can be all over the place. If AI were able to read through these conversations, it could consistently use sentiment analysis to apply scoring to sales conversations, potentially leading to more accurate and consistent forecasting. Practical applications: Decoding Market Trends We’re also often trying to pull market signals into our forecasts. The problem is there’s so much external data that’s difficult to distill into any kind of useful signal. With the power of EMGF network analysis, we may start to see AI dive into a sea of data from social media, news, etc. to spot the small and large trends that are relevant to our specific markets. It would be like having a map that shows where consumer interests are heading, allowing you to navigate your business strategy with precision and foresight. This insight could further increase our forecast accuracy. How do you see AI-driven sentiment analysis changing your approach to financial analysis and forecasting? #finance #ai #cfo