Unisys Data Analytics capabilities - Unlock business value from ClearPath Forward applications

Transcription

1 Unisys Data Analytics capabilities - Unlock business value from ClearPath Forward applications Authors: Alessandro Macchiarola, EMEA Principal Data Scientist David Howard, Global Director Technology September 2018

2 Agenda Introduction to Machine Learning Unisys Proposition - Unlocking business value/insights from ClearPath Forward applications Case Studies Moving data from ClearPath Forward to an Analytics Platform 2

3 Introduction to Machine Learning 3

4 Situation Data Growth Customer experience and centricity Industry evolution and innovation 4

5 Complications Availability of information Data fragmentation (internal, external) Economic Factors Competition, Weak Global Growth, Cost Pressures, Competitive advantage through innovation Technology Factors Cybersecurity, Blockchain, AI, FinTechs, Digital Wallets, new Product and Services Business Environment Regulations (GDPR, Basel), Risk Management, Customer Experience, Reputation Management 5

6 Outstanding Questions How do I maximise ROI on Data Assets while meeting business objectives and solving complications? How do I transform complications into advantage to my firm? How do I deal with the changing nature of my customer base and still be relevant and successful? How do I reduce cost structure while increasing volume of analytical, smart outputs? 6

7 Why is Machine Learning so important? 7

8 Beyond BI The Need for ML Imagine Now: Hundreds of columns defining the features for the model Millions and millions of rows due to our massive data ingestion When a new customer gets analyzed, could you say for certain whether he/she will stay as a customer? When a customer calls in (Inbound case), how can you recalibrate in real time the individual customer churn score, as well as best next offering, based on interactions with agent (e.g. call center)? Are you adopting self-service channels? How do these channels leverage cognitive information coming from this and other datasets? 8

9 The Big 3 Big Data Extremely large data sets analyzed to reveal trends, relating to human behavior and interactions. Data Analytics Examining raw data with the purpose of finding patterns and drawing conclusions by typically applying visualizations. Data Science Combines statistics, mathematics, programming, problem-solving and other scientific methods to extract knowledge and insights from data in various forms. 9

10 Unisys Proposition - Unlocking business value/insights from ClearPath Forward applications 10

11 Data Exchange Unisys Proposition - Solution blocks Business Outcome Third party feeds Social Media data Event-driven information system Listening Traffic data Customer lifecycle analytics and ML Segmentation targeting and positioning for each customer at each stage of its lifecycle Customer experience Understand and act upon what impacts customer NPS Social Media analytics Include social media data to inform daily customer behaviours and needs Customer Internet Banking, transactional behaviour, Retail Banking, CRM, MDM Website Browsing data Transactions Recommendation engine Provide next best offer on key offers and 3rd party products and services, and optimise offer landscape Risk analytics Quantification off risk at customer level and for various scenarios: from financial risk (exposure, loss) to personalised risk scores ClearPath Forward applications/ data bases Customer data Risk performance Sales & Marketing Sales & Marketing operational efficiency reporting Customer flow reporting / detect purchase pathway 11

12 Unisys Advanced Analytics Offerings Financial Services Public Sector Commercial Churn Fraud Risk Regulatory Compliance Cross-sell / Upsell Credit Modelling Integration with Elevate Weather Patterns Fraud, Waste, and Abuse Law Enforcement Regulatory Compliance Social Services Health Services Integrate with LineSight Customer 360 Predictive Maintenance Propensity to Spend Passenger / Airport Cargo Farming / Agriculture Integration with ActiveInsights 12

13 Data Science Tools and Technologies Currently Used 13

14 Case Studies 14

15 VALIUE APPROACH SOLUTION CHALLENGE Automate risk assessment processes Financial Services Evaluations of new bank submissions are currently manual and time consuming. It relies on individual agents reading through explanations, rules and regulations to decide to either accept or reject a submission. Unisys automates the process through Machine Learning. The algorithm learns the underlying criteria which determine agent s decision and then predicts an outcome for new submissions. Apply Naïve Bayes algorithm to learn accept/reject criteria from previously labeled data to predict acceptance/rejection for new submissions. Reduces resource cost Increases accuracy Leverage as an assistive tool, that recommends submission outcomes 15

16 VALIUE APPROACH SOLUTION CHALLENGE Automating Managed Service Workplace Services (EUS) Problem / Resolution details in ticket data are not fully utilized for strategic data-driven decisions, mainly because the details are documented in free text fields, limiting the ability to identify patterns manually. Use multi-variate information from ticket and prior resolution histories to find the best resolver group to route a ticket. Automate Queueing of Tickets for quicker and more efficient resolutions that eliminate human error. Create a Triage Bot to predict ticket routing for effective resolution Use NLP (LDA) to do Topic Modeling on both Problem and Resolution unstructured text to aid in ticket correlation Using Time-to-Event Modeling internally to aid in meeting SLA s. Improves customer experience Reduce TTR and reactivation rates Minimize Ticket hops Optimize resources 16

17 VALIUE APPROACH SOLUTION CHALLENGE Threat Detection Public Sector Improve Cargo checking efficiency, and boost success rate in detecting threats at borders Unisys LineSight is a near-real time, all-source intelligence analysis solution. It assesses the risk of incoming and departing people, cargo, conveyances and parcels via air, land and sea borders. Use Supervised learning for known threats to create high performing models. Use Unsupervised learning and anomaly detection to identify new threats based on emerging patterns. Streamline border clearance process Identify emerging threats Enable cost effective early intervention 17

18 VALIUE APPROACH SOLUTION CHALLENGE High Frequency Threat detection Cyber Security Private information is more vulnerable than ever before. News stories about ID theft and data breaches occur on a regular basis, with the effects being felt by millions of consumers. The Unisys SIEM Solution is designed to help our customers gain faster time-to-value and respond more effectively to threats. Unisys leverages LogRhythm to provide 24x7 security event monitoring, platform management, regulatory compliance, advanced analytics, and incident response services. Add ML algorithms on top of the rules-based approach to capture new threats which are arising daily. Keeps PII safe Protects from law suits Reduces operational costs 18

19 Mortgage Analytics - Dashboard with customer data and ML scoring logic, allowing quick and precise decision making 19

20 The importance of ClearPath Forward Move data out of ClearPath Understand what makes customer happy Data Growth Leverage Big Data and Machine Learning to automate decisions Customer experience and centricity Industry evolution and innovation 20

21 Moving data from ClearPath Forward to an Analytics Platform

22 What are requirements Batch e.g. Overnight Near Real time Just Changes / All Data Details of the change (operations) Just the last values (state) Daily Changes ClearPath Database Target Database 22

23 Data Exchange Data Exchange Extract, Transform and Load Other Databases ClearPath Databases DEX 5.0 RDMS DMSII DMSII 23

24 New Opportunities with Kafka Changes To Database ClearPath Databases Read / Re-Read Stream of Updates Can add / remove consumers at any time DEX will generate XSD to define message Examples Casandra utility Use changes to trigger an action 24

25 Data Exchange Capabilities Bulk Data Transfer (BDT) Moving all the data Elapsed Time sensitive Performed periodically Targets SQL Server Oracle KAFKA DMSII* Changed Data Transfer (CDT) Data transformations A B C B C a Move only changed data Saves multiple BDTs Near real-time Restructure data Transform values Perform validations Sources SQL Server* DMSII RDMS *Moving data into a ClearPath Forward Database is available with SQL Server to DMSII 25

26 How is Data Exchange being used Operational Efficiency Faster, better decision making (AI) New products & services The core operational data on ClearPath Forward Systems ( System of Record ) is key to enabling Big Data, Digital Business, and Operational Efficiency Data Exchange is key in enabling ClearPath Forward to participate in these industry trends 26

27 The Data Exchange Journey Year Release Main Feature DMSII to Microsoft SQL Server OS 2200 RDMS to Microsoft SQL Server SQL Server to DMSII SQL Server to Oracle (12c) OS 2200 RDMS to Oracle (12c) July DMSII to Kafka Queues OS 2200 RDMS to Kafka Queues 2H OS 2200 DMS to Kafka Future roadmap, subject to change 27

28 Questions