1. Generali Roadmap to a Data Driven Enterprise
Federico Crecchi – Business Analytics Solutions Manager
Pisa – May 10th, 2017

2. Generali Group at a glance

3. Analytics Solution Center fosters analytics driven business approaches
Our mission is to transform Generali into a analytics-driven organization, to solve business issues and to identify potential for growth
WHY
WHERE
Product &
Services
development
Channel
Optimization
Claims
Management
Operation Optimization
Pricing & Underwriting
Sales & Marketing
Identify business needs and potential opportunities
Leverage internal information asset
enriched with external data
Exploited through state of the art analytical techniques
Applied to business processes
HOW
Business Opportunity
Data
Insights
Business Outcomes
If you are here in the room you have most probably already interacted with us, and thus you should be familiar with our mission, some of our expertise and resources and with how we operate.
None the less is worth remembering the Group Analytics Solution Center is aimed to act as a transformation agent spreading an analytic driven approach across the group, and this is the reason why:
We work across the entire insurance value chain
Applying a data driven approach that moves from fact driven identification of opportunity
Leveraging internal data assets, that once collected, cleaned can be augmented with external data
Exploited with state of the art analytical technique
To produce actionable insights
That ultimately bring business outcomes
Technology
ENABLERS & CAPABILITIES
People
Process

4. ASC is currently working on more than 20 projects divided in streams
Fraud analytics
Claims streamlining
Portfolio management
Forecasting
Health analytics
Text Analytics
If you are here in the room you have most probably already interacted with us, and thus you should be familiar with our mission, some of our expertise and resources and with how we operate.
None the less is worth remembering the Group Analytics Solution Center is aimed to act as a transformation agent spreading an analytic driven approach across the group, and this is the reason why:
We work across the entire insurance value chain
Applying a data driven approach that moves from fact driven identification of opportunity
Leveraging internal data assets, that once collected, cleaned can be augmented with external data
Exploited with state of the art analytical technique
To produce actionable insights
That ultimately bring business outcomes
Customer Analytics
Augmented pricing

5. To be impactful, lots of skills are needed, hard to find in a single team..
A vast and diverse Analytics process requires a lot of different skills
…da cui il fatto di avere una mappatura di tanti tipi di capacità diverse per essere performanti in tutti I punti del processo stesso. Descrizione rapida delle capacità e quindi comprensione della data science come gioco di squadra

6. …thus the need to create an analytic ecosystem
The goal of Generali is to build an ecosystem of analytic experts
Project Theme
What is the best methodology to model short-term market movement? Desire to test deep-learning
Project Idea
Short-term movement determined by trading, which very often relies on “graphical” techniques (technical/cyclical analysis) 
maybe deep learning, which is very effective in image recognition, can help.
Also, one can include correlated exogenous factors.
…da cui il fatto di avere una mappatura di tanti tipi di capacità diverse per essere performanti in tutti I punti del processo stesso. Descrizione rapida delle capacità e quindi comprensione della data science come gioco di squadra

7. Thank you federico.crecchi@generali.com

8. APPENDIX

9. Fraud Analytics: combining business sense with advanced analytics
Social Network Analysis
Outlier detection
Full blown analytical capabilities
Text Analytics
Machine Learning & Predictive Modeling
Intermediate analytic solution
Heuristic Approach
Location Analytics
Business Rules
Data Augmentation

10. Example of Fraud Analytics
GOALS
LoB
Improve automatic detection
Leverage on advanced analytics to increase conversion rate
Expand data to new data source to gain new insight on claims
Accidents
Property
Motor
METHODOLOGY
Risk Matrix
Predictive Model
Scores +
Events
Andrea
Subjects
Behaviours
Business Rules
The final score is a combination of business rules and adavnced analytics rules
The claim is reported one after its occurence
If gap is higher than 50 days,
the the risk weight is 0.6

11. Communities identification
Customer Link Analytics: from individuals to communities to improve effectiveness
Data Identification
Link construction
Communities identification
Business application

12. What is text Analytics: from text to actionable knowledge2
Mining knowledge about language
e.g. part of speech tagging
Mining content from text data
e.g. finding topics and their distribution
Mining knowledge about the observer
e.g. sentiment analysis
Infer other real-world variables (predictive analytics)
e.g. predictions (big data applications) 3
REAL WORLD
OBSERVED WORLD
TEXT DATA
Perceive
Express
Perception
Language 1 4
Text data can be seen as the output of an observation, where the human is the sensor that perceives the real world and expresses the perception through a natural language, creating an unstructed textual data, which can be easily interpreted by other humans (we hope most of them). What text analytics does is to invert this process, starting from text as input and extract actionable knowledge from it. This can be done at different level of depth, starting from mining knowledge about the language itself, for example to discover which language is used in the text, or dividing the text into words, which is a process called tokenization, or tagging the words as the different parts of the speech (identifying nouns, verbs), this is called part of speech tagging. This is a shallow but more robust analysis. A deeper analysis might be mining content from text data, finding for example fotpics of discussion and their distribution or mining knowledge about the observer, its sentiment or emotions. Finally, a even deeper analysis is to infer other real world variables from text and do predictions. This can be done with large amounts of data. For example, we might want to try to predict the next president of the united states looking at the text regarding the elections, coming from news, tweets and other web data. This is a more difficult task.

13. Natural Language Processing Text Clustering and Categorization
How to do Text Analytics: areas of analysis
Natural Language Processing
Topic Mining
Text Clustering and Categorization
Sentiment Analysis
Text Retrieval
These are some areas of analysis. Natural Language Processing (NLP) is the statistical base for any type of analysis. Just to give a general idea, basic principles of statistics are used applying them to words. Something called a word distribution is created looking at word frequency and words co-occurence in a document. Grouping together words allows for different analyses, lexical looking at single words, syntactic looking at small groups of words and semantic looking at the meaning.
Topic mining is about extracting topics from text, a topic is a list of words that occur together frequently with a probability associated to each word. A topic could be for example insurance and the most probable words could be policy, agent, customer, life, P&C.
Text clustering and categorization is applied when there is a collection of documents and we might want to find clusters of similar documents based on a similarity metric (for example grouping documents written in the same language) or we might want to classify documents based on a predefined list of categories (for example assigning the main topic to each document). Sentiment analysis is about understanding the feelings, the mood or the opinions in the text and it is done for example looking for adjectives that express positive or negative connotations, these might depend on the context, for example high has a positive connotation when referred to quality but it is negative is referred to price. Finally Text retrieval is what a google search does with websites, so retrieving documents that contain certain keywords. The work with europassistance we are going to present has used text retrieval.

14. Possible business applications in the insurance domain
1 CORE PROCESS IMPROVEMENT
Survey feedbacks (i.e. NPS)
Identifying roots of customer dissatisfaction to improve existing business processes
5 COMPLAINT MANAGEMENT
Complaints sent to the company
Assigning complaints automatically to appropriate departments with appropriate prioritization
Real time reporting about the complaints (i.e. topics, affected BUs)
2 CORE PROCESS AUTOMATION
Claims request and bills, service tickets
Health claims management automation
Field care ticket management automation
6 MARKETING
Newspaper articles, Social networks, Forums
Perception of the brand and the competitors (sentiment analysis)
Perception of new products and services
Investors’ perception of the company
Early warning for reputation risk
3 FRAUD DETECTION
Providers bills, Medical reports, Claims requests, Claims reports
or even social web comments
Finding common patterns to discover fraudulent activities
Le applicazioni del text analytics nel mondo assicurativo sono innumerevoli. La più ovvia è quella del miglioramento dei processi.
Per esempio usare I questionari con I feedback per migliorare la customer satisfaction.
Nel call centre, per fare quality control, capire le dinamiche dell’interazione con I clienti o come nel caso di europassistance, usare le note degli operatori per estrarre informazioni.
Il text analytics può aiutare nelle frodi, per esempio identificando pattern nel testo comuni ai sinistri fraudolenti, il classico colpo di frusta.
Nella gestione dei reclami per automatizzare il processo e per esempio direzionare il reclamo al dipartimento corretto.
Nel marketing usando per esempio dati testuali provenienti da internet ed in generale, in operations, usando i documenti storici che le compagnie assicurative hanno in gran quantità.
7 OPERATIONS
Procurement contracts, Employment contracts, Historical Documents
Back office document archiving and analyses
Risk detection: Identifying unusual conditions
4 CALL CENTER QUALITY CONTROL
Call center calls (speech to text), Transcripts from operators
Quality assurance of operators
Deeper understanding of customer interactions

15. Merging structured and unstructured data+
DISEASES
TIME STAMP
SYMPTOMS
COSTS
ACCIDENTS
LOCATION
Nel caso del progetto di text analytics con i dati del call centre di EuropAssistance Italy, la compagnia ci ha fornito 3 anni di dati, riguardanti il prodotto travel health, con circa dossier, sinistri, e con un database di dati strutturati, come la durata di gestione del sinistro, i costi, i massimali e un dato non strutturato che è la transcrizione della conversazione degli operatori del call centre con i clienti. Da queste noi abbiamo estratto una serie di dati non strutturati contenuti nel testo con cui abbiamo arricchito i dati strutturati come la nazione nel quale il sinistro avviene e il tipo di problema di salute che ha scatenato il sinistro, includendo i sintomi citati, le malattie, gli infortuni, le operazioni eccetera. A partire da questo database arricchito abbiamo prodotto delle infografiche interattive che forniscono nuove insights sul business, utili alla compagnia per capire il business e che possono essere usate anche per fornire informazioni ai clienti, per esempio sui rischi di salute nelle destinazioni di viaggio.
SURGERY
TOTAL COVERAGE
PREGNANCY