Real life ai and machine learning (ml) in financial services
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This document discusses artificial intelligence and machine learning in financial services. It provides an overview of key concepts like artificial intelligence, machine learning, and deep learning. It then discusses why now is the right time for financial institutions to adopt these technologies due to factors like increased computing power, data availability, and cloud infrastructure. The document outlines typical use cases for financial services like credit risk management, fraud detection, and personalization. It also provides examples of computer vision and document classification projects. Finally, it discusses challenges of machine learning projects and best practices around data, deployment, and maturity.
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Real life ai and machine learning (ml) in financial services
- 1. © Philippe Choné Artificial Intelligence & Machine Learning in Financial Services Philippe Choné
- 2. © Philippe Choné Philippe Choné Making Change Happen. Fast! 25 years experience Innovation Digital transformations Complex change 200+ distributed teams Complex programmes (budgets>$100m) Technology Data Business & Digital Leadership
- 3. © Philippe Choné AI & ML Overview Hype and Confusion
- 4. © Philippe Choné Disruptive technologies Computer Science Artificial Intelligence Machine Learning NLP Deep Neural Network “Tell the algo what to do” Vs “Tell the algo how to train itself what to do”
- 5. © Philippe Choné What is ML Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. www.expertsystems.com What is a model? Languages used
- 6. © Philippe Choné Why Now? Algo Data Compute Cloud Perfect storm + + + = Land Grab Moore’s law
- 7. © Philippe Choné So What? Automate Processes + Make employees more productive + Increase Speed + Improve customer experience + Personalisation + Improve insight Innovation
- 8. © Philippe Choné Typical FS Use Cases Credit & Risk Mgt Trading Process optimisationPersonalised banking Email classification Demand forecasting OCR Handwriting Document analysis Next Best Action Personalisation Risk Management Chatbot Fraud detection Signature verification ReconciliationSentiment analysis Prediction Investment Analysis Credit scoring KYC Smart automation Customer Churn Prediction Customer insight Segmentation Market surveillance HFT Wealth management
- 9. © Philippe Choné 2 ML examples Computer Vision FinTech Evaluations Conversational AI Predictive Analytics Documents ClassificationImage Recognition Computer Vision
- 10. © Philippe Choné Build, Train & evaluate model Gather Data How not to do ML… Data Scientists
- 11. © Philippe Choné Real ML Journey Predict Deploy Hyper param tuning Build, Train & evaluate model Prepare data Explore data Gather Data Problem Framing Biz PB 5% 95%
- 12. © Philippe Choné From Hidden Technical Debt in Machine Learning Systems, 2014 Google Configuration Data Collection Data Verification Feature Extraction Machine Resource Management Serving Infrastructure Monitoring Analysis Tools Process Management Tools ML Code
- 13. © Philippe Choné Example: Documents classification • Data, data, data • We have a model…. So what? • Deployment • Model transposition • Project vs product
- 14. © Philippe Choné Example: Computer vision • ROI • Data, data, data • Purchasing (more) data • MVP definition • Microservices • Agile + DevOps • End to end process – Human in the loop
- 15. © Philippe Choné Some Challenges & Best Practices
- 16. © Philippe Choné AI is not (always) the silver bullet… • Is AI & ML right for this problem? • Why? • What does good look like? • Human in the loop • Obliterate don’t automate EASY Innovating in a regulated environment
- 17. © Philippe Choné R&D vs Innovation? • Portfolio definition • Business Case? • Success? Metrics ? ROI calculation? • Production? • Think like a start up • Prioritise and execute • Harden and explain • Deployment process • Inference Cost vs
- 18. © Philippe Choné Data, data, data • Where is my training data? Volume ? Labelling? • Governance: Can I use it? Can I trust it? Bias? • Security • Data lineage • More data will always beat better algo • Create more data • Use Exhaust data • Think Pipeline
- 19. © Philippe Choné ML Maturity • Low maturity across the board • Fluid environment • Black art? • Buy, Build, or Assemble? • Don’t forget what you know (Business case, DevOps, Agile) • Technical debts • Production Ready • Governance
- 20. © Philippe Choné In Summary
- 21. © Philippe Choné Start now, Start small, Dream big • Making (digital) strategy happen • Clear Vision • Pick your battle(s) • Execution is key • Clean data in a central place • Cloud base inference platform • Be agile, be fast • Simple is beautiful • Release • Adapt, Experiment & Learn A good plan, violently executed now, is better than a perfect plan next week.
- 22. © Philippe Choné p@chone.co.uk @Philippe_Chone philippechone Thank You philippechone.com