Artificial Intelligence high ROI case studies from around the world: approach, algorithms and operationalization. Pranay Dave - Teradata
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This document provides examples of artificial intelligence case studies with high returns on investment from around the world. It summarizes several case studies including a 30% improvement in a popularity model in Japan, $34 million in identified fraudulent activity in Denmark, and a 75% increase in customer viewings from personalized recommendations for a telco company in Central Asia. The document advocates for an analytic operations framework to help operationalize artificial intelligence models and maximize business value.
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Artificial Intelligence high ROI case studies from around the world: approach, algorithms and operationalization. Pranay Dave - Teradata
- 1. Artificial Intelligence high ROI case studies from around the world Pranay Dave Director Data Science, Artificial Intelligence at Teradata Approach, algorithms and operationalization
- 2. About Us Business Outcome Led, Technology Driven • ~1,400 + Customers in 77 Countries • ~15,000 Employees including ~5,000 Consultants • Market Cap: US $4 Billion+ • World’s Most Ethical Companies – Ethisphere Institute Fortune: Top 10 US Software Company Forbes 12/2017 : Teradata « 1 Customer focus » Top in Gartner and Forrester Quadrant
- 3. 30% improvement in popularity model $34M identified in fraudulent activity 75% of viewings via personalized recommendations 99% on-time arrival rate for trains 20% increase in customer retention $3.5M net profit increase from IVR flow redesign $6M revenue increase via next best offers $10M cost reduction optimizing patient stay $1M saved via identifying high risk churners 2X leads via behavior based triggers 5-Day reduction in close cycle time 200% increase in customer spend 50% time savings for users working with raw data $80M in revenue identified 40M customer accounts supported $3M saved by closing gaps in member care 10% reduction in RFQ cycle time 360º real-time view of customers 28% uplift in incremental sales Business Outcomes And many more…
- 4. Use-case selection Human Intensive Intellectual Activity ROI Potential SAMPLE CLIENT EXAMPLE Size of bubble is ROI Potential Few Examples of intellectual activity Creating Breakthrough Products Improving Customer Experience Operational Excellence
- 5. High ROI use-case from around the world AI-enabled GPS Japan Creating break-through products
- 6. AI-enabled GPS • Currently only 7% of cars globally have a dedicated system to detect stopped vehicles • We have helped developed AI based GPS systems which alert of stopped vehicles Context and Business Problem Result • This would enhance the navigation system they sell and demonstrate command of advanced technical capability to the public and competitors • Detecting Stopped Cars has to be done in Real time • We used YOLO Framework which provides the fastest object recognition Solution Highlight YOLO : You Only Look Once Improving driver safety
- 7. AI-enabled GPS Creating training data from Video Training Integrate in GPS (with Camera) OPENCV YOLO MULTINET SEGMENTATION
- 8. High ROI use-case from around the world Fraud Detection Denmark Operational Excellence
- 9. Fraud Detection Staying Ahead of Fraudsters • Danske Bank is one of top banks in Nordics area Context and Business Problem Result • Decrease in false positive by 60% • Converting banking transactions into 2D Solution Highlight Tens of Millions € lost each month High Fraud Loss Fast evolving fraud sophistication FRAUD NON- FRAUD
- 10. Fraud Detection Staying Ahead of Fraudsters Customer Initiated Fraudster Initiated
- 11. Fraud Detection Staying Ahead of Fraudsters Current models can only catch ~70% of all fraud cases Traditional ML models view transactions atomically Often missed fraud transactions are part of a series Capturing correlation across many features DEEP Learning Opportunity
- 12. Fraud Detection Staying Ahead of Fraudsters Converting Banking Transaction as 2D image Non-fraud Transaction Image Non-fraud Fraud Transaction Image X-axis: features, Y-axis: time Fraud Non-fraud
- 13. Fraud Detection Staying Ahead of Fraudsters • Decrease in False Positive by 60%
- 14. High ROI use-case from around the world Chatbot Central-Asia Improving Customer Experience
- 15. Telco AI powered Chatbot Improving Customer experience • Customer is largest Telco group in Asia • One of the main problems was response time to customer queries , which was in many minutes Context and Business Problem Result of AI based Chatbot solution • 90% of customer requests are now replied in seconds compared to many minutes earlier • Use Machine learning to identify subject such as Internet, 4G, Billing etc… Solution Highlight • Use AI to understand the question and generate reply. Use of « standard-box » responses • Use of Fast Text , which is open-source Deep Learning Library from Facebook based on messenger chats • Able to identify different word with same meaning: Package: pakage, package, pack
- 16. Telco AI powered Chatbot Improving Customer experience Roman/ English Language Detection •Internet •Billing •4G Subject area Classification User Query 1 or more reponses Classifier Matches Queries to Queries InformationRetreival Match Query to Responses using IR approaches FastText (AI-based) Deep Learning based Sequence to Sequence Models
- 18. 18 1. Getting into Production fast 2. Staying Relevant over Time Operationalization is about getting value Sustained competitive advantage requires analytic models to be deployed easily and continuously improved.
- 19. 19 General Situation • Business reviews reports on models • Multiple stakeholders and objectives • IT sent software to re- implement and deploy • Ad-hoc process • Data Scientist sits in Analytic Silo • Custom datasets • Variety of modelling techniques and technologies • Focus on trained model historical performance Performance Reports Trained Models Analytics Business IT MISSING FRAMEWORK
- 20. #TDUNIV Analytic Ops…A smarter way to operationalize Analytics Ops external data iteration data-mining statistics value-driven flexibility exploration discovery modelling blue-sky ideation security governance compliance curation deployment maintenance integration testing engineering process-driven Analytics Operations
- 21. #TDUNIV Analytic Ops Framework Data Scientist making models The business using a trained model Develop • Recipe Templates • DS Lab • Model scripting (untrained models) • Testing, Training, Model Evaluation • Version Control (Gitlab) • Dependency Management Automate • Dockerize • Model Training • Storage of trained models • Model Evaluation • Model Business Approval/Report Creation • Comparison vs current Live model (Champion/Challenger) Consume • Real-time model scoring engines • Automatic deployment of trained model artefacts • Dashboards and forecasts updated using new models • Multi Model management • Model output logging Involving: Analysts, Data Scientists, Engineers, Dev Ops, Business Stakeholders Methodology
- 22. #TDUNIV Analytic Ops Framework - Develop Develop • Recipe Templates • DS Lab • Model scripting (untrained models) • Testing, Training, Model Evaluation • Version Control (Gitlab) • Dependency Management Optimized Code template for operational purposes Github Version controlling
- 23. #TDUNIV Analytic Ops Framework - Automate Automate • Dockerize • Model Training • Storage of trained models • Model Evaluation • Model Business Approval/Report Creation • Comparison vs current Live model (Champion/Challenger) MethodologyAbstraction by Docker Containerization Dockers simplifies operational management Business receive reports to validate in “business” terminology
- 24. #TDUNIV With Analytic Ops Consume • Real-time model scoring engines • Automatic deployment of trained model artefacts • Dashboards and forecasts updated using new models • Multi-model management • Model output loggingChampion Challengers Customer Propensity Engine MULTI-MODEL SCENARIO
- 25. #TDUNIV Teradata Analytic Ops Accelerator Build Model Pipeline Merge Request Model Consump - tion Engine • Build images • Run containers: • Train model • Test model • Generate report • Save metadata • Trained model summary • Review trained models performance • Deploy approved models • Review new model code • Trigger automated modelling pipelines • Develop models Data Lab • Unapproved Models • Metadata • Approved Models • Metadata Recipe Templates
- 26. Key Takeaways / Key Areas for High-ROI use-cases - Creating Breakthrough Products - Improving Customer Experience - Operational Excellence /Operationalization is key for getting business value