INFORMATION DRIVEN INSURER Transform Data into Insight
Transcript of INFORMATION DRIVEN INSURER Transform Data into Insight
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Insurers IT Investments
% IT Investments in 2016
“100% del
campione dichiara
di voler aumentare
il budget nel
triennio 17-20”
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Competing in today’s marketplace
“The risk of investing too late in smart
machines is likely greater than the risk
of investing too soon”
- Gartner
“Insights-Driven Businesses Will
Steal $1.2 Trillion Annually By 2020”
- Forrester
Advanced Analytics is the autonomous or semi-
autonomous examination of data or content using
sophisticated techniques and tools, typically beyond
those of traditional business intelligence (BI)
Advanced Analytics Machine Learning Data-insights Driven Business
& =
“Early evidence suggests that AI can deliver real
value to serious adopters and can be a powerful
force for disruption. Early adopters are already
creating competitive advantages, and the gap with
the laggards looks set to grow”
- McKinsey Global Institute
Machine learning is a field of computer
science that gives computers the ability
to learn without being explicitly
programmed
A data-driven company is an
organization where every person who
can use data to make better decisions,
has access to the data they need when
they need it.
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Enterprises Data Driven Best Practices
Leveraging all types of data Applying Machine Learning across the
enterprise
Automating decision making
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What Makes this Difficult?
Belief that it is Too Hard
AnalyticalSkill Gap
Transactions
Conversations
Machines
Massive Amount of Data
1.5 Million managers + analysts who know how to use big data to make effective decisions will be needed
Shortage of 140K to 190K deep analytical skills
Source: McKinsey Global Institute
• We don’t have enough Data Scientists
• We don’t know where to begin
• We believe that it is too complex and challenging to even get started
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Need for Automation
http://www.gartner.com/newsroom/id/3570917
“More than 40 percent of data science
tasks will be automated by 2020, resulting
in increased productivity and broader
usage of data and analytics by citizen data
scientists”
Gartner defines a citizen data scientist as a person who creates or generates models
that use advanced diagnostic analytics or predictive and prescriptive capabilities, but
whose primary job function is outside the field of statistics and analytics.
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Traditional Approaches to Advanced Analytics Require You To
Rely on a few highly skilled, scarce, and expensive resources
Constantly maintain models manually preventing scalability
Spend excessive time searching and preparing data for use
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Automation of Machine Learning process is the fastest way to become a data driven business
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SAP Leonardo Machine Learning : High Level Portfolio of Capabilities
Data Science Platformgiving data scientists and business analysts tools to build machine learning models
In-Database Machine Learninggiving developers, data scientists, and IT departments the platform needed to buildintelligence into their IT landscapes.
Machine Learning Servicesenabling developers to quickly build intelligence into their applications and business processes.
Intelligent Applications & Machine Learning Extensionsaddressing specific business problems by lines of business
De
velo
pe
r
Data Scientist
End
-Use
r
Application Services (ML + Deep Learning) Technical Machine Learning Libraries Machine Learning Modelling
Business Applications
2
13
4
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- Strategic Data Platform for all SAP Applications
- Machine Learning Libraries allowing in-memory and co-located
transactional and analytics processing
- Predictive Analytics Library Over 90+ algorithms, covering Classification, Regression, Clustering, Association analysis, Time
series forecasting, Link analysis, Recommender systems, Outlier detection, statistical and data
pre-processing functions
- Integration / Extensibility SAP HANA R integration and Google Tensorflow integration
Streaming Analytics embedded machine learning
Application function library (AFL) SDK embedding custom C++ functions
SAP Application-specific function libraries for optimization and demand forecasting
in SAP Supply Chain- and SAP Retail Applications
SAP HANA In-Database ML and Application Development Platform
Algorithms
And Data
Push Machine
Learning close to
Data
Algorithms
designed to run in-
memory
Parallel processing
for fastest
predictions,
forecasts, …
Intelligent Apps & ML ExtensionsMachine Learning ServicesIn-Database Machine Learning Data Science Platform1
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SAP Predictive Analytics
- Enable Business Analysts & Citizen Data Scientists to
create predictive models through simplified approach
- Automate the end-to-end process - data preparation, model
training and model deployment are fully automated
- Ensure the output is ready to consume by business users.
- Wide range of in-database & in application scoring options to
enable deployment everywhere
- In application deployment through Predictive Analytics integrator
(PAI)
Train
model
Prepare
data
Apply
model
Capture
feedback
Intelligent Apps & ML ExtensionsMachine Learning ServicesIn-Database Machine Learning Data Science Platform
Data Manager
Automated Modeler Expert Analytics (VCF)
Predictive Factory
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Enable business users to create predictive models
Automated Data Preparation & Encoding
Model building with SRM Easy to understand & review results
• Operates on your data where it resides – no ETL to/from analysis data marts
• Automated data prep for missing values, outliers, non-linear distributions
• Machine learning applied to encoding ordinal, nominal, string, and date variables
• Simplified 2 quality indicators• KI – measuring Model ability to
explain the target• Percentile – 0 to 100%
• KR – measuring Model ability to generalized on new data
• Percentile – 0 to 100%
• Apply Vladamir Vapnik’s SRM methodology, an application of statistical learning theory.
• Automatic search through a family of robust ridge regression models, arriving at an optimum solution
• Optimizes predictive power and statistical robustness simultaneously (in-process model validation)
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Magnitude of productivity with automation – 70% reduction
Problem
Analysis – 5
to 10%
Data Analysis, Preparation and Encoding – 45 to 65% Build Model – 20 to 30%Deployment –
5 to 10%
Review Results – 20 to
30%
Traditional
Manual Repetitive Prone to error
Automated
Problem
Analysis
Data
Analysis,
Preparation
and Encoding
Build
ModelDeployment
Review
Results
SAP Predictive Analytics automates full Lifecyle to reduce 70% of traditional
Source: Gartner
Models
per
month
Number of analysts and Data Scientists
Traditional
Automation
Weeks and Months
Hours and Days End to End Insights Everywhere
Operationalize
with end user
application
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Predictive Services
Scoring Equation
Key Influencers
Outliers Detection
What-If Simulation
Time Series Forecast
ClusteringRecommendation
Brand Sentiment Predictive Maintenance Network Optimization Insider Threats
Asset Tracking Personalized Care Product Recommendation Risk Mitigation in Real Time
Propensity to Churn
Real-Time Demand/Supply Forecast
360-degree Customer View
Fraud Detection
SCP Predictive Services
Intelligent Apps & ML ExtensionsMachine Learning ServicesIn-Database Machine Learning Data Science Platform
• Machine learning
• Deep Learning
3
Possible
Use cases
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SAP Machine Learning Foundation
Intelligent Apps & ML ExtensionsMachine Learning ServicesIn-Database Machine Learning Data Science Platform
• Time series change point
detection
• Similarity scoring
Tabular Image
• Image feature extraction
• Image classification
• Customizable image
classification
Text
• Topic detection
• Text classification
• Text feature extraction
• Deploy, Manage and Customize in SAP Cloud
• Deploy, Manage and Monitor run your own TensorFlow Model on ML foundation
• Leverage and benefit from the platform capabilities of ML foundation like authentication and scalability
• Use your existing data assets to retrain ML foundation’s image or text classifier
• Simply access ML foundation’s API for retraining – no extensive machine learning knowledge required
• Benefit from having a classifier that is tailored to your own business
• Leverage ML foundation’s capabilities to serve your training jobs
SAP TechEd 2017 Announcements - Deep Learning Services – TensorFlow
• Machine learning
• Deep Learning
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Intelligent Apps & ML Extensions
SAP Cash Application Next-generation intelligent invoice matching
powered by machine learning
• Improves days of sales outstanding
• Integrated with SAP S/4HANA for reduced TCO and
time to value
• Allows shared services to scale as the business grows
• Empowers finance to focus on strategic tasks and
service quality
SAP Brand ImpactReimagine marketing and sponsorship
engagements
• Fast: Near real-time
• Transparent Interactive Interface
• Accurate and scalable to millions of hours
• Time-annotated impact indicator API for combining
data with CRM, ERP, Web site stats
SAP Customer Retention Build customer loyalty through proactive
retention
• Automatically classifies and finds patterns
• Detects at-risk customers
• Provides understanding of root causes and timely
predictions to act
Intelligent Apps & ML ExtensionsMachine Learning ServicesIn-Database Machine Learning Data Science Platform
SAP Business Integrity Screening-Identify fraud behavior
• Ability to focus on cases with highest likelihood of
fraud and ROI
• Integrated with SAP HANA for reduced TCO and
time to value
• Models update as patterns of fraud evolve
• Custom and 3rd-party algorithms to optimize for
customer’s business
Cloud for Customer
Opportunity Scoring
• Opportunity scoring to find the most likely
opportunities to close
• Key Factor Analysis to explain reasons
SAP Service Ticketing-Accelerate customer service in an
omnichannel front office
• Improve service response times with automated
processing
• Integrated with SAP Hybris Service Cloud for
reduced time to value
• Allows customer service to scale with increased
digital interactions
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Drink your own Champagne: SAP uses SAP Leonardo ML Service Ticketing
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How SAP Customers are solving business problems
FRAUD + RISK FINANCE + HR
• Fraud and Abuse Detection
• Claim Analysis• Collection and
Delinquency• Credit Scoring• Operational Risk
Modeling• Crime Threat• Revenue and Loss
Analysis
• Cash Flow and Forecasting• Budgeting Simulation• Profitability and Margin
Analysis• Financial Risk Modeling• Employee Retention
Modeling• Succession Planning
SALES + MARKETING OPERATIONS
• Churn Reduction• Customer Acquisition• Lead Scoring• Product
Recommendation• Campaign
Optimization• Customer
Segmentation• Next Best
Offer/Action
• Predictive Maintenance• Load Forecasting• Inventory/Demand
Optimization• Product
Recommendation• Manufacturing Process
Opt.• Quality Management• Yield Management
21+ Industries
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Product recommendation supports agents to find the best personalized
recommendation of insurance products for a customer based on known
customer features. Customers life situation, sales experience and
historical sale success is taken into consideration.
Causes:
▪ Sales experience is often not shared across sales agents
Proposed Solution:
▪ Use ML technique to create a customer profile from collected customer data
▪ Give customer the option to adjust the proposed customer profile (fine-tuning)
▪ Derive product recommendations from adjusted customer profile
Rough Solution Sketch Benefits
▪ Be more competitive
▪ Better personalized
recommendations based on sales
experience for similar customers
Challenges / Pain Points for Insurers
▪ Highly competitive market to sell insurance products
▪ Customers request personalized recommendations tailored to their needs
Product recommendation
Find the best products for your customer
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SAP Financial Solution Advisor APP
• Quickly gain an overview of Sales KPIs, Upcoming Appointments, Quotes, Tasks and recent customer interactions
• Get a 360° customer view with insurance flavor
• Manage tasks & appointments and respond to customer enquiries
• Search for Customers, Quotes, Policies, Tasks and Appointments
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SAP Financial Solution Advisor APP
Analyse the needsof your customers with focus on the
essentials.
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SAP Financial Solution Advisor APP
Receive productrecommendationsthrough machine
learning algorithms.
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Where can SAP take you?
*Forrester Total Economic Impact of SAP Analytics, November 2016 www.sap.com/analytics-tei
60%Reduction in
process costs
$2.4M Annual cost
savings
171% Three year ROI
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Unlike other offerings in the market, SAP provides
SAPINTEGRATION
ALL-IN-ONESOLUTION
INSIGHTS EVERYWHEREEND TO ENDFAST
Minutes to hours vs.weeks to months with
automation
Automated techniquesto embed models
From 10 to 1,000’s ofmodels and data sets
HANA | HCP BW | Universes
Prepare I ModelDeploy I Automate
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