INFORMATION DRIVEN INSURER Transform Data into Insight

INFORMATION DRIVEN INSURERTransform Data into Insight

Milan, 9th November, 2017

2CUSTOMER© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ

Insurers IT Investments

% IT Investments in 2016

“100% del

campione dichiara

di voler aumentare

il budget nel

triennio 17-20”


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.


Enterprises Data Driven Best Practices

Leveraging all types of data Applying Machine Learning across the

enterprise

Automating decision making


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


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.

http://www.gartner.com/newsroom/id/3570917


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


Automation of Machine Learning process is the fastest way to become a data driven business


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


- 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


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

2


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)


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


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


• Machine learning

• Deep Learning

3

Possible

Use cases


SAP Machine Learning Foundation


• 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

3


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


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

4


Drink your own Champagne: SAP uses SAP Leonardo ML Service Ticketing


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

19CUSTOMER © 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ

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


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



Analyse the needsof your customers with focus on the

essentials.



Receive productrecommendationsthrough machine

learning algorithms.


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


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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