Data Science in Finance

If I Had to Start Over in Data Science, I’d Do This First…. People ask me all the time:  "Patrick, if you had to start over in data science, what would you do differently?" My answer always surprises them. I wouldn’t start with Python. I wouldn’t start with SQL. I wouldn’t even start with machine learning. I’d start with Financial Intelligence. Why??? Because if you don’t understand how businesses make money, you won’t know how to create real impact. Early in my career, I focused on models, data cleaning, and accuracy… everything they tell you to prioritize.  But when I sat in meetings with executives, they didn’t care about my model’s accuracy. They cared about revenue, costs, and profitability. Once I learned to read financial statements and connect data to business impact… everything changed. So here’s my advice: Learn to read income statements, balance sheets, and cash flow. Understand ROI, profitability, and cost optimization. THEN master Python, SQL, and ML… because you’ll know how to apply them where it matters. The best data scientists don’t just analyze numbers… they drive business decisions. Are you focusing on what actually sets you apart? Drop ‘CONNECT’ in the comments if you’re ready to think beyond the code.

3 Data Science Concepts for FP&A Leaders (+FREE: My top 10 infographics) Financial models powered by artificial intelligence, machine learning, and sophisticated algorithms become more popular each day. As FP&A professionals, we don’t need to be able to build them from scratch. But we need to know their use cases, advantages, and shortcomings. And we need to know basic Data Science vocabulary, so we can effectively lead AI implementation projects. --- 💡 Get my top 10 most popular FP&A infographics for FREE at https://lnkd.in/eihTAhTW --- Here are three basic concepts you need to know: #1  𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻 𝘃𝗲𝗿𝘀𝘂𝘀 𝗖𝗮𝘂𝘀𝗮𝘁𝗶𝗼𝗻 It’s considered one of the golden rules of statistics: Just because the movements of two variables track each other closely over time doesn't mean that one causes the other. To differentiate factual causation from mere correlation, we need to consider two things: a) What’s the causal link or mechanism, and b) is there enough scientific rigor that shows a statistically significant correlation (for instance, you may not be considering enough data points.) 📌 Your Actions: Ask your Data Scientist about the statistical significance look for the mechanism. #2  𝗕𝗮𝗰𝗸𝘁𝗲𝘀𝘁𝗶𝗻𝗴 AI and ML models allow us to do what’s almost impossible with traditional modeling. We can act as if the past hasn’t happened yet. We can start the model using data from a year ago and see how well it would have predicted that period. As a result, we can have multiple models compete with each other and select the one with the lowest variance to reported actuals. 📌 Your Action: Don’t agree to use an AI/ML model until you have seen the backtesting results. #3  Feature Selection You may have heard the term “garbage in, garbage out” before. Computer Scientists use it to remind us that the best model won’t make any valuable predictions if we feed it with the wrong inputs. 📌 Your Action: When the Data Science team presents a model to you, always ask which “Features” (a term for model inputs) they chose, why they selected them, and which other features they have tried. If the model isn't accurate enough, consider increasing the number of features. -Christian About me: 🏫 I teach FP&A skills to finance teams and business leaders. 🖥️ I spent 15+ years in FP&A leadership roles at P&G, Unilever, Squarespace. 🎓 Now, I'm a full-time corporate trainer, online course creator, and the Director of the Wharton School's FP&A Certificate program. 🗣️ To learn more, visit FPAprep[dot]com or email me at hello[at]FPAprep[dot]com.

🚀 Just Released: ML-Enhanced Stock Prediction using Ito's Lemma! 📈 I'm excited to share my latest project that bridges the gap between traditional financial mathematics and cutting-edge machine learning! 🔬 What makes this special? ✅ Combines Ito's Lemma (stochastic calculus) with LSTM neural networks ✅ Multi-head attention mechanism for complex temporal pattern recognition ✅ Predicts volatility and drift parameters dynamically using ML ✅ 20+ technical indicators including RSI, MACD, Bollinger Bands ✅ Monte Carlo simulations with confidence intervals ✅ Real-time 6-month forecasting with uncertainty quantification 📊 Key Results: 68.3% profit probability (ML) vs 61.2% (traditional) 2.15% reduction in prediction uncertainty Dynamic parameter estimation that adapts to market conditions 🛠 Tech Stack: PyTorch for deep learning architecture LSTM + Attention for sequence modeling Geometric Brownian Motion enhanced with ML predictions yfinance for real-time market data This hybrid approach demonstrates how mathematical finance and AI can work together to create more robust prediction models. The model doesn't just predict prices—it learns market dynamics and adjusts volatility/drift parameters in real-time. 🔗 Open Source: Available on GitHub with comprehensive documentation and examples! Disclaimer: This is for educational/research purposes only. Always consult financial professionals for investment decisions. #MachineLearning #QuantitativeFinance #StochasticCalculus #DeepLearning #PyTorch #FinTech #DataScience #LSTM #StockPrediction #OpenSource What do you think about combining traditional financial mathematics with modern ML techniques? Would love to hear your thoughts! 💭

Does AI mean the end of finance jobs? 👀 Just a few years ago, finance relied on human interpretation of data, subjective decision-making, and reliance on outdated technology. Now, AI is streamlining the entire process. ↪ Real-time AI fraud detection keeps your money safer by spotting unusual transactions. ↪ AI enhances risk management, helping businesses avoid financial pitfalls. ↪ AI-based credit scoring opens loan opportunities to more people with accurate assessments. For all those who say, "I'm in finance. Why should I learn AI?" It’s time to upskill or risk getting left behind! Mark my words—AI will change finance like Excel did in the 90s. You can leverage AI to: ↪ Automate Trading Strategies ↪ Forecast Market Trends ↪ Analyze financial news ↪ Enhance risk models ↪ Spot fraud instantly ↪ Build adaptive portfolios. ↪ Assess creditworthiness fairly ↪ Make data-backed decisions But what are the skills you need to learn exactly? Here’s the list! 💡Programming (Python and R): Proficiency in Python and R is essential for developing algorithms, performing complex data analysis, and automating financial tasks. Python’s extensive libraries, like Pandas for data manipulation and Scikit-learn for machine learning, make it indispensable for finance. R is particularly useful for statistical analysis and data visualization. 💡 Machine learning algorithms: Understanding and applying machine learning algorithms is crucial. Finance professionals should learn how to build and train models that can predict market trends, detect fraud, and optimize investment strategies. Techniques like regression analysis, decision trees, and neural networks are foundational. 💡Data Analysis Tools: Mastering tools like Tableau, Microsoft Power BI, and Apache Spark enables finance professionals to handle and visualize large datasets. These tools are critical for turning raw data into actionable insights that drive business decisions. 💡Statistical Analysis: Strong statistical skills are necessary for analyzing financial data, testing hypotheses, and making data-driven decisions. Techniques such as probability theory, variance analysis, and hypothesis testing are fundamental for building accurate financial models. 💡 Big Data Management: Proficiency in managing and analyzing big data is increasingly important. Finance professionals should be familiar with databases like SQL and NoSQL, as well as data processing frameworks such as Hadoop, to efficiently handle large-scale financial data. Answer to the question in the first line: No. Instead, AI will augment current positions, allowing people to do higher-level thinking tasks that robots can’t do. AI and humans working together = better finance and better jobs. AI is not the future. It's now. Embrace it, and you'll lead the next generation of finance. #artificialintelligence #finance #corporate

If I'd need to do an AI for FP&A and Finance Roadmap in 2025 this is what I'd do: 1: Know the Possibilities of AI in Finance AI is not just ChatGPT — and it’s not just about answering questions. It’s about transforming how Finance operates. But it’s hard to know what you don’t know. That’s why the first step is awareness. Get familiar with what’s actually possible. Here are just a few examples of how AI can be used in Finance: ✅ Automated variance analysis – AI detects anomalies, highlights drivers, and explains them in seconds. ✅Forecasting & scenario planning – Build predictive models that adapt in real-time. ✅Expense & invoice classification – Automate tedious reconciliations and improve audit readiness. Before building your roadmap, open the window to what’s possible. 2. Choose an implementation partner and tool I have this resource with 30+ AI tools for Finance below But if you want to keep it simple, My top suggestion is OpenAI and ChatGPT If your company just uses Microsoft products, then explore Copilot If your company just uses Google products, then explore Gemini 3. Get your team trained on that tool No matter what LLM and AI company you choose to partner with, I think this is one of the most important steps. Every tool has its features The more you know about them, the more you can do with AI for Finance Some examples: GPTs from OpenAI: A game changer, you can add your policies, files and data in minutes and you can create a chatbot for your entire company Colab AI Agent from Google: Have an AI finance data scientist at your disposal to explore how to find the main drivers of profitability or do scenario modeling Copilot in Excel with Python from Microsoft: This can unlock data insights in seconds. My point is that every tool has its secrets. And you can spend hours and hours learning them. But AI changes every day. So instead of trying to keep up, choose a learning partner and get your team trained on use cases of AI in Finance. If you need help with that, let me know and I can give you suggestions Some options: AI Finance Club Self Paced Courses LinkedIn Learning Courses 4. Prioritise use cases Use my framework in the pdf Focus on Quick Wins and Major Projects Keep some Fill Ins ready Avoid Thankless Tasks 5. Create a Governance & Compliance Plan AI is powerful—but it needs guardrails. Define what data can and can’t be used Set standards for review and oversight This ensures your AI efforts are safe, ethical, and scalable. 6: Track and Share Wins Start small, but celebrate results. Did an AI tool reduce reporting time by 50%? Did automation save your analysts 10 hours a week? Share it with the team and leadership. Build an Internal prompt Library and start documenting every AI idea or request that comes up—big or small. Momentum builds when people see the value. If you need the full version of this guide (20+ pages), comment and I'll send!

I got started in the world of Quant Finance because I love working with data and then I fell in love with finance. In today's high-speed financial environment, possessing knowledge in data analytics is rapidly rivaling the importance of understanding the intricate machinery of finance itself. Today, data doesn't just support decisions—it drives them. It forms the cornerstone of strategies, underpins investment models, and propels predictive analysis. With the digital data deluge, there's a growing need for sharper tools and methods to navigate and interpret this immense sea of info. At Wright Research, we're not just watching this transformation—we're at its forefront. We meld factor models with avant-garde AI techniques and regime modelling, sifting through the data's intricate tales and shaping our investment advisory and portfolio management strategies. But how can upcoming finance aficionados or even seasoned pros gear up for this shift? 📚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Top-tier platforms like Coursera, WorldQuant University, edX, and MIT OpenCourseWare offer specialized courses covering everything from financial data analytics to visualization. 💡 𝐇𝐚𝐧𝐝𝐬-𝐨𝐧 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞: While theory lays the foundation, experience builds the house. Dive into platforms like Kaggle or Quantopian Inc. for a taste of real-world datasets and challenges. 🤝 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐢𝐧𝐠: The quant community is vibrant and ever-growing. Embrace it! YouTube webinars, forums, and offline events are excellent avenues. Sharing, learning, and growing together is the mantra. 🔄 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐄𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧: The learning never stops. From journals like the Journal of Financial Data Science, SSRN to thought leaders' insights, staying in the loop is crucial. 🛠️ 𝐓𝐨𝐨𝐥 𝐌𝐚𝐬𝐭𝐞𝐫𝐲: Step beyond Excel. Explore the depths of Python (with pandas, scikits), R, SQL, and visualization platforms like Tableau or Power BI. In this info-centric era, data analytics isn't just a tool—it's the compass directing finance pros through waves of data, pointing to insights and guiding choices. It's an essential, not a luxury, for those aiming to leave a lasting impact in modern finance. #DataAnalytics #FinanceEvolution #QuantResearch #ContinuousLearning #WrightResearch

Interested in finance careers? Master quantitative & technological skills. Finance is evolving—firms don’t just want people who can think about markets, they want people who can analyse and automate them. Whether you're in investment banking, asset management, or hedge funds, these skills will give you an edge: 📊 Excel & Financial Modeling – The backbone of finance. I. Building financial models – DCF, LBO, merger models. II. Sensitivity & Scenario Analysis – Understanding how key variables impact valuation. III. VBA & Excel Automation – Enhancing portfolio analysis and financial workflows. 📈 Data Science & Quantitative Analysis – Markets are driven by data, and those who can analyse it win. I. Python & R for Data Analysis – Portfolio optimisation, factor modeling, and asset allocation. II. Machine Learning in Asset Management – Predictive analytics, NLP for news sentiment, AI-driven trading. III. Monte Carlo Simulations & Stochastic Models – Risk modeling and stress testing portfolios. 🖥 Bloomberg & Financial Databases – The tools every analyst needs. I. Bloomberg Terminal, FactSet, Refinitiv – Extracting market data and running analysis. II. Interpreting Economic Indicators – Using real-time data to drive investment decisions. ⚡ Algorithmic & Quant Trading – The future of markets is automated. I. High-Frequency Trading (HFT) – Executing thousands of trades per second using ultra-low latency. II. Statistical Arbitrage – Using quantitative models to exploit mispricings. III. Risk Parity Strategies – Balancing risk-weighted exposure across asset classes. The best finance professionals aren’t just good with numbers—they know how to leverage technology to make smarter, faster decisions. Which of these quant skills are you working on? Follow me, Afzal Hussein, to break into the industry faster. #Finance #Banking #Careers