SparkBeyond Connecting AI to Business and S ocial Impact

1. SparkBeyond Connecting AI to Business and Social Impact

2. Reducing prison violence by >40% in the US Reducing inmate violence by over 40%

3. Increasing palm plantation yield in Indonesia Smart plantation yield with AI

4. Increasing non-medically invasive underwriting decisions by 10x (from 7 to 70%), allowing for 31% of the decisions to be made automatically Existing process Automated Underwriting powered by SparkBeyond AGE AGE FACE VALUE < 40 41-50 51-60 61-69 70+ < 40 41-50 51-60 61-69 70+ Non-invasive: 7% cases < $100,000 Non-invasive: 70% of cases (no manual intervention - 31% of cases) $100,000 -$249,999 Invasive: 30% of cases (Model assisted decision making in 6% of cases) Invasive (e.g., lab tests): 63% cases $250,000 -$499,999 Invasive + Rx + Physician statements: 30% cases $500,000 -$999,999 $1,000,000 > $1,000,000

5. Microsoft|SparkBeyond: Connecting AI to Business and Social Impact HARNESSING HUMANITY’S COLLECTIVE INTELLIGENCE TO SOLVE THE WORLD’S MOST IMPACTFUL PROBLEMS. Generating >$1b in impact across 20+ industries, all the while: • Fighting human trafficking • Accelerating lifesciences research • Empowering cities to become more resilient Click here for a video on SparkBeyond, made by Microsoft SparkBeyond proprietary & confidential

6. Accelerating life-sciences research - replicating 2yrs of work in 34-mins COLORECTAL CANCER OSTEOPOROTIC HIP FRACTURES HOSPITAL READMISSION predicting patient at risk 1 year in to the future - generating a lift of 13x for the top 1 percent of the population beating the current model within 30 days predicting 5 years in to the future - beat the 3 state of the art models

7. SparkBeyond was born out of three challenges that organizations face togenerating impact at-scale ... SparkBeyond asks millions of questions per minute to discover drivers about the business Cognitive bias & bottleneck 1 1 1 1 1 1 0 0 0 0 1 SparkBeyond enriches client data with hundreds of external data sources, connecting the dots to the world around us 2 Internal data provides a partial view Dynamic systems continuously discover drivers; never sleeping, never slowing down Ongoing need to adapt to changing conditions 3 SparkBeyond proprietary & confidential

8. SparkBeyond has generated>$1b in impact across 20+ industries... RETAIL E-COMMERCE MEDIA / TELECOM Generated $100m in impactby identifying over 20 new churn drivers in a week covering 80% of churners for a major US TelCo. Discoveredprice drivers for 15,000 categories and millions of product. For example, bright colourful gadgets tend to sell at a lower price. A leading retailer optimized locations of the next 1,000 branches. External data help identify unexpected drivers, such as the proximity to laundromats. OIL & GAS, MINING & MINERALS FINANCIAL SERVICES LIFE SCIENCES Discovered anovel technique to detect colon cancervia blood tests; designed a unique three- way partnership between HMOs and pharma's to scale drug testing Increased a leading European bank’s total net income by 2.3% - identified drivers for Branch plan optimization External & Internal SME credit risk scoring Enabled an increase in oil well productivity by 40%,using data from over 10,000 unconventional oil wells. 20 Most Promising Cognitive Solution Providers 2017 2016 Machine Intelligence 3.0 highlight Entrepreneurial Company of the Year 2016 Award in ML Analytics in Oil Gas SparkBeyond proprietary & confidential

9. A top-5 Polish bank improved net income by 2.3% by scaling SparkBeyond across 4+ use cases - in Marketing & Sales, Risk, and Operations Use case Poland • Integrated SparkBeyond into core workflows to increase client total net income by 2.3%, mainly through • Branch plan optimization • External & internal SME credit risk scoring • Electronic fraud prevention (e.g. phishing) • Next best offer • Accelerate impact through: • Leveraging additional external data • Identifying stronger indicators of future customer behavior and embedding continuous discovery of drivers • Better integrating models with business process A A B C Branch Performance Credit risk scoring Fraud Analytics Next-Product-To-Buy(NPTB) Banking

10. … Empowering clients to do well while doing good … Sample joint business & social impact use cases Business impact Social impact • Score corporate, SME, and personal loans / credit for high vs. low risk • Allocate part of the impact to providing financial help counseling for high-risk individuals Enabling financial help (Risk Scoring) • Set thought leadership on HR ‘blindfolded’ analytics in banking, discovering insights absent race, age, etc. • Reduce employee churn, increase employee effectiveness Workplace diversity (‘blindfolded’ HR analytics) • Reduce anti-money laundering(as a type of fraud), using explainability to set industry standards • Discover ongoing risk factors for fraudulent transactions or accounts Reducing money laundering (Fraud Analytics) SparkBeyond proprietary & confidential

11. … via the Impact Program which starts with your top 1-2 pain points … SparkBeyond Launching new AI-Powered initiatives: balancing impact with capability building Client RAPID IMPACT ONGOING IMPACT INDEPENDENT IMPACT Set up and operated with SparkBeyond support Set up and operated by clients independently Mobilized by SparkBeyond 1-2 3 5 6 4 1-2 SparkBeyond proprietary & confidential

12. Use case Whats next? SparkBeyond’s global team is set to deliver impact at scale SparkBeyond’s global hubs with PhD-level data scientists and applied AI experts from Cern, Google, Microsoft, IBM and PayPal; strategists from McKinsey, Bain, PwC and Booze&Co LetsTalk@SparkBeyond.com London SparkBeyond Hub • Inspire leadership team on ‘the art of the possible’ • >$1b in impact across 20+ industries • Cross-functional use cases • Identify top business pain points • Mobilize the Impact Program Sample client impact New York Israel Singapore Melbourne SparkBeyond proprietary & confidential

13. A top-5 Polish bank improved net income by 2.3% by scaling SparkBeyond across 4+ use cases - in Marketing & Sales, Risk, and Operations Use case Poland • Integrated SparkBeyond into core workflows to increase client total net income by 2.3%, mainly through • Branch plan optimization • External & internal SME credit risk scoring • Electronic fraud prevention (e.g. phishing) • Next best offer • Accelerate impact through: • Leveraging additional external data • Identifying stronger indicators of future customer behavior and embedding continuous discovery of drivers • Better integrating models with business process A A B C Branch Performance Credit risk scoring Fraud Analytics Next-Product-To-Buy(NPTB) Banking

14. Use case: Branch plan optimization A Branch plan optimization

15. Use case: Branch plan optimization SparkBeyond integrated internal branch and customer data with World Knowledge to cluster branches, identify drivers of performance per cluster and set accurate targets 01 Business integration Data integration Advanced analytics approach 03 02 Cluster A Cluster C Select data used AI approach to identify main dimensions of performance and correlation between drivers Change management and overall budgeting of branches Cluster B Cluster D Assessment of metrics that drive performance of branches GUS data about the Polish market and BIK Wikipedia Open Street Maps Sales representatives performance Clustering of branches with similar characteristics Internal client data ATM density in the area Customer characteristics incl. demo-graphics Branch A Urbanicity Potential new data External data External data SparkBeyond AutomatedResearch Engine Customer product portfolio Median age Customer behavior (transactional data) Positive impact on branch sales Negative impact on branch sales Identified main performance drivers of each branch cluster Branch characteristics (e.g., type of branch) Branch historical performance (e.g., sales, costs) Setting more accurate sales targets for each cluster based on sales potential, PLN million Branches Current Estimated based on potential Internal mgmt. data (e.g., tenure of staff) a 1.05 a Calculated of future targets for branches & made appropriate adjustments Cluster A ~10 Income at stake, EUR million, full 12 months

16. Use case: Branch plan optimization Without using geo-spatial data, the client was overlooking many factors that drive branch performance Branch area based on Google Open Street data Main factors that may drive the branch performance Location Selected branches Branch I Branch located in suburbs area with limited competition (only PKO ATM present) Warsaw, Location 1 Branch located near one of biggest train stations despite competitive area with several branches of competitors Branch II Warsaw, Location 2 Branch located in urban area in city centre next to several restaurants, shops and other banks Branch III Warsaw, Location 3

17. Use case: Branch plan optimization SparkBeyond uncovered several drivers that impacted sales in branches - helpful in setting appropriate sales targets and location optimization Example sanitized drivers that impact sales in branches Sales target Branch performance Location Selected branches Most important Sales representatives performance Branch I ~95% of target X PLN Warsaw, Location 1 Median income Urbanicity Number of FTEs in branches Branch II ~120% of target ~ PLN Warsaw, Location 2 Unemployment rate Median age Branch III ATM density ~1.3X PLN ~60% of target Warsaw, Location 3 Least important Online bank market share

18. Use case: Credit risk scoring B Credit risk scoring

19. Use case: Credit risk scoring Internal data augmented with credit bureau, wikipedia and other external sources to discover drivers driving 10’sM EUR via optimized SME credit operations Business integration 01 Advanced analytics approach Data integration 02 03 Selected data presented for internal and external SMEs Discovering new highly predictive variables generated by SparkBeyond from atomic, transactional, BIK, BIG, OSM, GUS, application and network databases, etc. More accurate scoring per SME client Application data, e.g., client characteristics incl. demographic Transaction data of SME and its relations’ network to other SMEs Developing new Machine Learning predictive model for new features’ extraction and knock-out decisions even if logistic regression model suggests granting loan Client’s product portfolio, e.g., loan, deposit, current account information Financial standing of SME from application data Extractthe ranking of risk drivers from Machine Learning model and logistic regression for comparison purposes Only for internal SME clients Internal data SparkBeyond AutomatedResearch Engine Updated credit scorecard based on improved model (and potential adjustment to scorecard criteria methodology) Upgrading current logistic regression model with SparkBeyond and Machine Learning derived features for acceptance decisions GUS, Worldbank, Competitive Industrial Performance macroeconomic data about the Polish market Points of Interests, e.g., competitors within trade area of SME, from OpenStreetMap Wikipedia for text sources Credit bureau data (BIK, BIG) Creating clusters of riskiest SME clients to get understanding and insights from models Adjustments to data collection during application process ~10 Income at stake,EUR million, full 12 months External data Potential new data

20. Use case: Fraud C Electronic fraud prevention

21. MICROSOFT INTERNAL ONLY - SHARED UNDER NDA Use case: Fraud SparkBeyond generated significant impact by discovering 10+ rules for fraudulent activity & deploying a continuous learning system to pick up newly emerging patterns of fraud Selected data presented Advanced analytics approach Data integration 02 03 Business integration 01 Discovering fraud patterns Significantly reduction of undetected attempts of electronic fraud ~15% 10+ 25% 3 Internal Client data Electronic Log Data by SparkBeyond from atomic log data to design 10+ data-driven business rules From To To From Electronic Log Data (Jolt) Click-Stream Back-testing of the rules Client characteristics incl. demographics Undetected frauds Undetected frauds Cost of electronic frauds per year (PLN) Cost of electronic frauds per year (PLN) to avoid high percentage of false-positives Initial Blacklist detection Clients product portfolio Implement new business rules into decision engine to apply auto-detection SparkBeyond AutomatedResearch Engine Additional Business Rules Client behavior (transa-ctional data) Implement continuous learning within fraud detection to ensure new types of fraud are picked up as they develop Potential to scale-up capabilities to further cyber and fraud use cases in the Bank (e.g., application form fraud) Allowed Payments Income at stake PLN million, full 12 months 2-5

22. Use case: Fraud Prior to SparkBeyond, the effectiveness of the bank’s capability to detect fraud varied significantly month to month Detected Frauds Effectivity of the “blacklist” varies significantly from month to month Currently transactions are only blocked based on this “blacklist” Undetected Frauds Volume of fraud,# 12 61 60 Customers have reported previous frauds from this account A wealth of information exists in the clickstream log data as well as other customer data sources which are not being utilized in this process There is significant room for improvement in the current business rules used to characterize fraudulent transactions 64 70 74 Other banks have reported account as fraudulent 1 Business rule Mar Apr May Jun Jul Aug Value of fraud PLN thousand 630 846 970 775 581 98

23. Use case: Fraud SparkBeyond-Powered Electronic Fraud Prevention discovers drivers for fraud as a live system: understand why fraud occurs and scoring new activity as fraudulent or not ~85% 3 mln 275 75% 5 mln 471 2.2 mln Leveraging electronic log data, along with other internal and external data sources to identify when a customer’s account has been accessed by another party Objective: 10+ Cost of electronic frauds per year Number of electronic frauds per year Blocked electronic frauds per year Blocked electronic frauds per year Number of electronic frauds per year Cost of electronic frauds per year (PLN) PLN Impact, saving in one year, rising to 5 million over two years due to expected increase in frauds Data-Driven business rules TO: business rules which identify electronic fraud FROM: Current blacklist process Short Term Long Term Customers have reported previous frauds from this account Bank blacklist Unsupervised methods to monitor new types of electronic fraud Other banks have reported account as fraudulent 1 Business rule Up to PLN 5 million in next 12 months Impact: