1. © 2014 Cognizant BBVAOpen4U Innova Challenge SpendWise Genie Application Dec 1, 2014 SpendWise. Be wise © Cognizant Technology Solutions 2014. All rights reserved. Cognizant owns all rights in all its trademarks, trade names or logos, Patents, Copyrights and any other intellectual property rights used in the presentation. Cognizant acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in the presentation. Except as expressly permitted, neither this presentation nor any part of it may be reproduced, stored in a retrieval system, transmitted or modified in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without prior written permission of Cognizant Technology Solutions. Cognizant disclaims and makes no warranties or representations as to the accuracy, quality, reliability, suitability, completeness, usefulness of the presentation.

2. © 2014 Cognizant 1 What’s in store … Background Introduction The Experience User Journey Key Insights “People Like Me” & “Offers for Me” Key Business Benefits Delivered How it Works Solution Architecture – Business & Data Flows Technical Architecture – Application Design Components Cloud Based Predictive Model Design Recommended Road Map Solution Evaluation Parameters Appendix

3. © 2014 Cognizant Background  Banks face the challenge of leveraging large volumes of disparate data for increasing customer engagement (consumers & Bank affiliated merchants)  Merchants find it challenging to provide new and existing customers with target offers at the right place & time and increase sales  Customers need to manage personal finances, monitor spend and save money on their purchases through relevant offers 2 SpendWise. Be wise Cognizant believes that Banks can address these questions by leveraging advanced Cloud Based Data Analytics and the Digital Banking–driven ecosystem ( including Social Media Feeds ) This submission is an attempt to solve these business problems by leveraging the power of Cognizant’s Pioneering SMAC (Social - Mobile - Analytics - Cloud) Framework Our Solution Challenges

4. © 2014 Cognizant 3 Introduction “SpendWise Genie ”, is a multipurpose Mobile App that gives consumers rich insights into benchmarking their spend behavior with people of similar demographic profile( by location and merchant category). The App also uses the power of real time predictive algorithms for relevant offer presentment and empowered decision making BBVA App For Customers Value Proposition SpendWise Genie Spend Comparison with Peers across segments Responsible Spending (for Consumers*) + Increased Sales (for Banks & Merchants) Best Offers Prediction supported with real-time data ( Maps/ratings) SpendWise. Be wise People Like Me Offers For Me Leverages the power of BBVA API’s Non BBVA customers can also benchmark their spend behaviour through this App

5. © 2014 Cognizant 4 User Journey SpendWise Genie is a Smart “Spend Benchmarking & Real Time Offer Presentment” App that leverages BBVA APIs & advanced predictive algorithms View Minimal input details (Age group, Gender etc.) for Non BBVA customers Interactive charts & visualizations to highlight spend patterns across category- location- time continuum 1 Plan Empowered spend planning across categories and peer groups 3 Compare 2 Compare own spend pattern with peer segment and other BBVA Customers to understand deviations or identify high spend categories Explore 4 Receive relevant offers and reduce spend (Prediction based on consumer preferences, merchant location, discount offered etc. )

6. © 2014 Cognizant 5 Key Insights “SpendWise Genie” is designed to answer the following:- What is your spend pattern across different spend categories & time- periods? How does your spend compare to your peer segment? How can you plan & optimize your spend across categories ? What are the most relevant offers which you can utilize ?

7. © 2014 Cognizant 6 “People like me” App With “People Like Me” learn how consumers spend in your same category People Like Me Rich interactive charts represent the spend pattern across time cohorts The consumer also picks spend category and subcategory The user provides his age, gender & location information and also the time period for which he wants to view spend patterns For App demo refer to pps file For App demo refer to pps file

8. © 2014 Cognizant 7 The “Offers for me” App With “Offers For Me”, review relevant and timely offers with supporting information ( maps, reviews and Ratings) Offers for me The user provides his age, gender & location information The consumer specifies anticipated spend range or can be presented offers for the selected spend category The selected merchant location is displayed on an interactive map where consumer has the option to get driving directions from current location (GPS) The consumer can also view ratings and reviews of the selected merchant, available with ‘Google Places’ The top merchant offers pertinent to the spend category and anticipated spend range are presented to the consumer as a listing For App demo refer to pps file

9. © 2014 Cognizant 8 Key Business Benefits delivered Promotes Responsible Spending - Know whether you are over spending / or can afford to spend more in certain categories People Like Me Offers For Me Save on spend by redeeming offers Drive / Reach new customers (by motivating those who underspent in specific categories)  Drivel Sales through increased Footfall and attract new customers  Design Future Offers/ Campaigns based on the performance / take- up of offers Deliver the power of data to customers and increase loyalty by being a source of personal finance planning information  Drive Spend – thus increasing benefits for the bank and its merchants  Increased Customer Loyalty through an effective Offer Presentment Program Consumer Merchant Bank

10. © 2014 Cognizant 9 Solution Architecture Business & Data Flow BBVA Data Analytics BBVA Host BBVA API’s Recommendation rules to push relevant offers based on offer score & customer segment mapping Easy Visualization of Spend Patterns Existing Consumer Transaction Aggregated BBVA Consumer Data | Individual Consumer Data Review Feed Analyzer Regular feed of Social Posts Google Places APIs High prediction accuracy for spend category & range Merchant locations with favorable ratings & reviews SpendWise. Be wise Advanced Predictive Model Cloud Hosted Model

11. © 2014 Cognizant Services 10 Technical Architecture Application Design / Details Mobile Android Application Ionic js Angular JS Cordova / Phone gap Offers Service hosted in Google app engine Prediction Service via Advanced predictive models BBVA Data API Utilities Auditing Logging Caching Exception Handling Connectivity Locator Service via Google Maps API Social Sentiments via Google Places API Solution Building Block Description Mobile Android application UI was developed using ionic and angular js. High charts was used as charting framework. Mobile app was packaged using Cordova. Offers Service This service was built to filter and display the offers specific to user interest. This service is hosted in Google app engine. Prediction Service This service was built using Advanced Predictive Models. Locator Service Details of the merchant providing the offers are located on Maps and direction from user’s location to merchant location is provided. Social Sentiments Reviews and ratings of the merchant are provided using Google Places API Utilities Common components to address non functional related common concerns across layers High charts

12. © 2014 Cognizant Cloud Based Predictive Model Design Modeling Business Understanding Data Understanding Data Preparation Evaluation Deployment Objective of the model is to predict user spend category and spend amount based on: -The historic spend behavior of user segment (age group & gender); -Location (zip code) based spend patterns as well as seasonality trends – based on day-of-the-week and time-of-the-day The base data* used for model training is based on the BBVA transaction data for: -Top 10 spending Zip Codes using BBVA APIs: Cards Cube and Consumption Pattern -Data filtered for Top 5 Merchant Categories with the highest spend in these 10 zips Input data* for predictive model training is prepared by combining data from above 2 BBVA APIs to calculate the most probable spend on merchant category for a user segment at a weekday, at a particular hour: -By calculating merchant spend probability at an hour of a weekday using weighted contribution of spend at that hour and spend by that user segment -For the sake of simplicity, spend behavior on a particular weekday (e.g a Tuesday) across months is assumed to be similar Using Advanced Predictive Models for a given Zip Code, User Segment, Day, Hour of Day: - Model 1: Prediction of Merchant Category with the Highest Probability of next User spend; Model Type: Classification Model; Model Accuracy: 89% - Model 2: Prediction of Spend Range for a user for selected/predicted Merchant Category; Model Type: Regression Model; Refer MSE (Mean Square Error Values in Appendix) Model Output i.e. the predicted category and the predicted spend is used to pull offers from offers database filtered for : -Selected Zip Code, Merchant Category, Predicted Spend (Range) -Pull rating/reviews from Google Places for displayed filtered offers Detailed Evaluation of Models explained in next 2 slides 11 CRISP – DM Methodology Followed * - All local copies of data made to test model accuracy have been purged

13. © 2014 Cognizant Offer Prediction Model 12 Prediction Models Prediction of the Merchant Category with the Highest Probability of next User spend in a given: - Zip Code - User Segment (Age Group, Gender) - Week Day - Hour of Day 1 1 Spend Prediction Category Prediction Prediction of the Spend Range for a user for selected/predicted Merchant Category in a given: - Zip Code - User Segment (Age Group, Gender) - Week Day - Hour of Day 2 2 Model Model Limitations: Since the current model uses the costumer transaction data as input data from BBVA APIs, which is available only at a segment level (age-group, segment) and not at a customer ID level, the models output is only valid for segment/cohort level predictions, assuming all the customers within that cohort behave in a similar spending manner. Model Limitations: Since the current model uses the costumer transaction data as input data from BBVA APIs, which is available only at a segment level (age-group, segment) and not at a customer ID level, the models output is only valid for segment/cohort level predictions, assuming all the customers within that cohort behave in a similar spending manner. Cloud Based Predictive Model Design

14. © 2014 Cognizant How it works… Prediction of Merchant Category with the Highest Probability of subsequent user spend in a given: - Zip Code- User Segment (Age Group, Gender) - Week Day- Hour of the day Assumptions : -Training* and prediction limited to Top 5 Spend Merchant Categories (except mx_others): {(mx_basrsandrestaraunts, mx_food, mx_fashion, mx_auto, mx_hyper (mall)} -High Accuracy prediction for Top 10 spending zips for current model Model Type: Classification Model; Model Accuracy: 89% Test Cases : Zip CodeAge GroupGenderWeek DayHour Actual Category with Maximum Spend Predicted Category "11000""19-25""Female""Fri"5" mx_barsandrestaurants1" "11000""26-35""Unknown""Mon"19" mx_food1"" mx_barsandrestaurants1" "11320"">=66""Male""Thu"1" mx_food1" "11510"">=66""Male""Mon"17" mx_travel1"" mx_food1" "11520""Unknown""Female""Thu"19" mx_fashion1"" mx_barsandrestaurants1" "14300""19-25""Female""Fri"16" mx_hyper1" "11510""Unknown""Female""Mon"10" mx_travel1" "11000""56-65""Female""Tue"6" mx_beauty1"" mx_barsandrestaurants1" "11000""Unknown""Female""Fri"1" mx_beauty1" "11590""<=18""Male""Sat"2" mx_auto1" 1 Model 13 Evaluation of the Data Mining Model for Spend Category Prediction * - All local copies of data made to test model accuracy have been purged

15. © 2014 Cognizant Prediction of Spend Range for a user for selected/predicted Merchant Category for: - Zip Code - User Segment (Age Group, Gender) - Week Day- Hour of Day 2 Model Assumptions : -Training* and prediction limited to Top 5 Spend Merchant Categories (except mx_others): {(mx_basrsandrestaraunts, mx_food, mx_fashion, mx_auto, mx_hyper (mall)} -High Accuracy prediction for Top 10 spending zips for current model Model Count: Separate Model for each of the Top 5 Merchant Categories Model Type: Regression Model; Test Cases: E.g. For merchant Category – “mx_auto” Zip Code Age GroupGenderWeek DayHourActual SpendPredicted Spend "11590""19-25""Female""Fri"7147.33483.6822 "11320""36-45""Male""Mon"171114.31611.875 "11520""46-55""Male""Sat"122723.51415.509 "64000""56-65""Male""Wed"18168.15169.7097 "11590""46-55""Male""Wed"910001294.2 14 How it works…. Evaluation of the Data Mining Model for Spend Range Prediction * - All local copies of data made to test model accuracy have been purged

16. © 2014 Cognizant 15 Recommended Roadmap….  All zips from BBVA consumers and across Mexico can be incorporated  All the merchant categories can be included in analysis  Real time access to offers database  Real time Social media sentiment analysis to push relevant places / offers to specific segments  Customer level transaction data, if made available, more specific & targeted offers can be built using more sophisticated algorithms  Customers can like/dislike offers to generate valuable insights for future offer design  Incorporation of external data factors (e.g. weather data) to suggest suitable offers / merchants  Integration of payments/offer redemptions through Dwolla, helping merchants track & plan offers  iOS version of the app would be launched  A Spanish version of the app in agenda for future release

17. © 2014 Cognizant 16 Solution evaluation parameters Originality The app is uniquely positioned to address customer need of spend optimization through relevant offer presentment based on peer-group past spend behavior, location, day of the week & time of the day Visual Appeal The app enables the customer to view his peer segment spend patterns across spend categories at different drill down levels through rich interactive charts. Also, once a user selects an offer - relevant merchant details, ratings, reviews, social sentiment and its location, directions on an interactive map are presented Usefulness 1.Spend Tracking & Reporting 2.Peer Benchmarking 3.Spend Prediction & Offer Presentment 4.Spend optimization 1.Spend Tracking & Reporting 2.Peer Benchmarking 3.Spend Prediction & Offer Presentment 4.Spend optimization Usability across devices The app uses consumer’s current location along with other segment parameters to report & recommend offers based on cloud hosted Advanced Prediction API rule engine. Mexican mobile market has a prevalence of Android OS (>70% share *), the app can be currently used across Android mobiles & tablets External Data 1.Google Places API (ratings & reviews) 2.Google Maps API 3.Offers database as a proxy for bank offers / Groupon data 1.Google Places API (ratings & reviews) 2.Google Maps API 3.Offers database as a proxy for bank offers / Groupon data * Source - http://www.statista.com/statistics/245193/market-share-of-mobile-operating-systems-for-smartphone-sales-in-mexico/

18. © 2014 Cognizant 17 Appendix

19. © 2014 Cognizant 18 Mocked Up Offers Database The offers database consists of following variables: Actual Merchant Details in Mexico Merchant Name Merchant Address Merchant Zip Code Merchant Latitude & Longitude Merchant Sample Image Link Merchant Category Mocked-up Offers Offer Details  Spend Amount in Mex$  Discount%  Savings in Mex$ Sample offers data

20. © 2014 Cognizant Predictive Model Evaluation (1/2) 19

21. © 2014 Cognizant 1 1 Model High Prediction Accuracy for 4 merchant categories Poor Prediction Accuracy for 1 merchant category 20 Predictive Model Evaluation (2/2) 1 1 Model Based on 50 Out Sample Validation Test Cases

22. © 2014 Cognizant Visit link for app download :- https://bbvaopen4u.cognizant.com/Spen dWiseGenie Visit link for app demo :- https://bbvaopen4u.cognizant.com/Spen dWiseGenie/SpendWiseGenieDemo.ppsx 21 Thank you