[Cryptica 22] Intellya’s AI CORE bringing value to businesses: the power of Contextual bandits - Aleksandra Vucicevic
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1. Intellya provides an AI-driven ecosystem including multilingual AI, NLP, customer-centric solutions, and business process automation. 2. The ecosystem utilizes various data sources and machine learning models to provide personalized customer experiences, predictive analytics, and robo-advisor capabilities. 3. Key offerings include a powerful chatbot, analytical CRM, and AI products to improve customer segmentation, recommendations, fraud detection, and more.
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[Cryptica 22] Intellya’s AI CORE bringing value to businesses: the power of Contextual bandits - Aleksandra Vucicevic
- 2. INTELLYA’S AI-DRIVEN ECOSYSTEM v v v MULTILINGUAL AI, NLP, NLU OMNICHANNEL VOICE TO VOICE POWERFUL CHATBOT ENGINE CUSTOMER IN THE PALM OF YOUR HAND REAL- TIME SYNCHRONIZATION BUSINESS PROCESS AUTOMATION REPORTING OF ALL BUSINESS DATA TASK HANDLING NEXT-GEN RELATIONSHIP PLATFORM AI CORE PERSONAL AI- ASSISTANT AND ROBO ADVISOR CUSTOMER-CENTRIC BUSINESS BETTER EMPLOYEE PERFORMANCE UNIQUE DIGITAL EXPERIENCE
- 4. ML MODELS DATA SOURCES Core system Chatbot communication Mobile Application Clicks, messages and interactions Branch activities Call Centre interactions Location data Internet banking … v v v ANALYTICAL CRM SOLUTION AI CORE PERSONAL AI-ASSISTANT AND ROBO ADVISOR APIs FOR EXTERNAL APPLICATIONS v PREDICTIVE MODELS SEGMENTATION MODELS NLP MODELS AI PRODUCTS Saving plans Overspending Bill Payment STATISTICAL MODELS Sentiment analysis Text recommenders Text summarization Data-driven campaigns Customer segmentation Transaction categorization Next best offer CLV Churn Recommenders EWS OPTIMAL CONTROL MODELS
- 5. PERSONALIZATION OF WHOLE CUSTOMER JOURNEY IMPROVED DAILY DECISION- MAKING PROCESS EQUIP THEM WITH VALUABLE BUSINESS INSIGHTS ADDING VALUE WITH NEW SERVICES AND DIGITAL EXPERIENCE HAVING BETTER CUSTOMER JOURNEY AND OFFERING BETTER UNDERSTANDING OF CUSTOMER SEGMENTS IN YOUR AUDIENCE CUSTOMER-CENTRIC BUSINESS 1. BETTER EMPLOYEE PERFORMANCE 2. UNIQUE DIGITAL EXPERIENCE 3.
- 6. PLATFORMS v v v NEXT-GEN RELATIONSHIP PLATFORM COMPANY’S OTHER SYSTEM PERSONAL AI-ASSISTANT AND ROBO ADVISOR ML DATABASE STAGINING DATABASE ML API DATA SOURCES Core system On premise activities Web & Social media activities Mobile application CC & Chatbot communication Batch Streaming API ML DEVELOPMENT Data preparation and feature extraction &model development 1. ML model registry/Tracking Server 2. Inference: choosing Best model 3. AI CORE Predictive models Segmentation models Statistical models NLP models Optimal control ML
- 7. BUSINESS UNDERSTANDING FOCUS ON THE HIGHEST PRIORITY PRODUCTS/ SERVICES DATA UNDERSTANDING INCREMENTAL ADDING OF DATASETS 1. 2. 3. 4. BUSINESS ANALYSIS WITH CLIENTS DATA EXTRACTION DATA ANALYSIS DATA TRANSFORMATION FEATURE SELECTION AND EXTRACTION 1. 2. 3. 4. DATA ENGINEERING PROCESS
- 8. ADVANCED ANALYTICS MLBASED CASES PREDICTIVE MODELS STATISTICAL MODELS NEXT BEST OFFER CUSTOMER LIFETIME VALUE CHURN PREDICTION RECOMMENDERS EARLY WARNING SYSTEM SAVING PLANS OVERSPENDING MODELS BILL PAYMENT MODELS • Ensemble models • Time series models • Computer vision models • Collaborative filtering: a) explicit ratings b) implicite ratings • Graph-based recommenders • Context-based recommenders • Ensemble models • Time series models • Linear programming • Probabilistic linear programming • Non-linear programming • Statistical model
- 9. SEGMENTATION MODELS SENTIMENT ANALYSIS CUSTOMER SEGMENTATION TRANSACTION CATEGORIZATION DATA-DRIVEN CAMPAIGNS • Word2vec models • Time series models • Transformer based models • RFM analysis • Unsupervised clustering based on RFM • Unsupervised clustering based on predefined attributes • Time series clustering • Expert- system model • Contextual bandit model • Optimization- based models NLP MODELS OPTIMAL CONTROL MODELS TEXT RECOMMENDERS *English,Serbian… TEXT SUMMARIZATION *English only
- 11. CONTEXTUAL BANDIT MODEL REAL-WORLD DECISION- MAKING CONTENT PERSONALIZATION COMPLEX SITUATIONS ANOMALY DETECTION EFFICIENT INFORMATION RETRIEVAL RECOMMENDER SYSTEM ACTIONS IN A GIVEN CONTEXT DATA-DRIVEN CAMPAIGNS
- 12. ACTION REWARD PERSON SEQUENTIALLY PULLS HANDLES ON SLOT MACHINES (A SERIES OF EXPERIMENTS) ONE-ARMED BANDIT DEVICES IN CASINO EACH MACHINE HAS A DISTINCT CHANCE OF WINNING PERSON’S OBJECTIVE IS TO MAXIMISE THE OVERALL EXPECTED REWARD EACH ACTION HAS PREDEFINED REWARD DISTRIBUTION ACTION HE SEEKS FOR IS ONE IN WHICH EXPECTED REWARD IS THE HIGHEST SLOT MACHINE 1 SLOT MACHINE 2 SLOT MACHINE 3
- 13. ACTION REWARD MULTI-ARMED BANDIT ACTION REWARD CONTEXTUAL BANDIT STATE REWARD DEPENDS ONLY ON ACTION ACTION WILL BE CHOSEN DEPENDING ON THE STATE (CONTEXT), REWARD DOES NOT DEPEND ONLY ON ACTION, IT DEPENDS ON THE STATE AS WELL CONTEXTUAL BANDIT IS GREEDY – ACTION HAS BEEN CHOSEN ON PREMISE TO IMMEDIATELY MAXIMIZE THE REWARD WHICH DEPENDS ON THE STATE AT THE PARTICULAR TIME
- 14. TRADE-OFF LEARNER CHOOSES AN ACTION OBSERVES A LOSS/COST/REWARD FOR CHOSEN ACTION REPEATEDLY OBSERVES A CONTEXT CHOOSING ACTIONS IN DYNAMIC ENVIRONMENTS ALGORITHM CAN TEST OUT DIFFERENT ACTIONS END GOAL: MAXIMIZE THE EXPECTED CUMULATIVE REWARDS IN THE T TRIALS EXPLOITATION Understanding user preferences through new recommendations Recommending based on historic preferences EXPLORATION AUTOMATICALLY LEARN WHICH ACTION HAS THE MOST REWARDING OUTCOME SET OF AVAILABLE ACTIONS IS LIMITED USES ADDITIONAL SIDE INFO/CONTEXT
- 15. Image source: https://nth-iteration-labs.github.io/contextual/articles/website_optimization.html DIFFERENT TAU FACTOR IMPACT ON AVERAGE ACHIEVED REWARD THROUGH TIME: HIGHER TAU: EXPLOITATION IS IN FOCUS v LOWER TAU: EXPLORATION IS IN FOCUS v BY REDUCING TAU FACTOR WE GAIN MORE IN SEARCHING FOR A NEW ONE PERFECT OFFER (EXPLORATION)
- 16. BANK’S CLIENT OR LEAD BANK ACTION: CHANNEL & PRODUCT REWARD: PRODUCT PRICE (VALUE) – COST OF ACQUISITION PER CLIENT (CONTACT CHANNEL AND PROCEDURE) CHANNELS: EMAIL, VIBER, MESSAGE, DESK (BRANCH), ATM STATE: TRANSACTION HISTORY & CLIENT’S PRODUCT PORTFOLIO ACTION REWARD STATE GOAL: TO MAXMIZE REWARD THROUGH DEFINING THE OPTIMAL ACQUISITION CHANNEL AND PERFECT PRODUCT
- 17. OFF-POLICY LEARNING ALGORITHMS OFFSET TREE MODELS DOUBLY ROBUST POLICY OPTIMIZATION ONLINE LEARNING UPPER CONFIDENCE BOUND EPSILON-GREEDY ALGORITHM ADAPTIVE GREEDY THOMPSON SAMPLING OUR DEVELOPED MODEL ONLINE LEARNING MODEL THOMSPON SAMPLING WITH NORMALIZING FLOWS
- 18. IN REAL-TIME ABSOLUTE REWARD INCREASE BY CASE OUR RESULTS BEST PERFORMANCE WITH OUR DEVELOPED ONLINE LEARNING MODEL THOMSPON SAMPLING WITH NORMALIZING FLOWS 30% COMPARED TO CURRENT BANK’S STATE CONTEXTUAL BANDIT MODEL: DATA-DRIVEN SALES AND AI-BOOSTED MARKETING IN BANKING
- 19. CUSTOMER SEGMENTATION CAMPAIGN CHANNEL CUSTOMER RESPONSES ALL CONTENT CREATED BANK’S SALE FUNNEL BANK’S PRODUCT CATALOG DATA WE GATHER OUTPUTS WE PROVIDE BENEFITS WE GUARANTEE WHAT CONTENT TO SEND ON WHICH CHANNEL TO DELIVER WHICH PRODUCT TO OFFER WHEN TO SEND THE CAMPAIGN THE NEXT BEST ACTION FOR A CUSTOMER INCREASED CUSTOMER LIFETIME VALUE DECREASED CHURN RATE EXTENDED LOYALTY BASE MULTIPLIED CONVERSION RATE LONGER TIME SPENT IN A BANK’S SYSTEM
- 22. • XGboost • ADA boost • Random forest • Ensemble deep learning algorithms • ARIMA • ARMAX • MINIROCKET • Self supervised models – TSBERT • Time series • Transformer – TST • ResCNN • Omniscale CNN • Ensemble based contextual bandits • Deep learning based contextual bandits with variational approximations Ensemble methods Time series models Contextual bandit model STATISTIC BASED TIME SERIES MODELS: DEEP LEARNING MODELS:
- 23. • node2vec • Graph Attention networks • Graph Convolutional networks • Graph spectral networks • STFT preprocessing combined with CV models • ResNet • DenseNet CNN • Inception • Vision transformer models • Autoencoder clustering techniques (Deep learning) • Kmeans • DBScan • OPTICS • Mean shift Graph based recommender Computer vision models for NBO and CHURN Clustering