1This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

Carlos Albo (WEA)

Configurable and PersonalizedInsurance Products

2This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

Cluster#5 Pilots[P#13] Alternative & automated insurance risk selection for SMEs [WEA]

Capture from open sources nontraditional data from SMEs to automate risk rating and insurance product recommendation

Exploit AI technologies to create SMEs risk profiles and automate underwriting processes S M E ’ s

A G R I C U L T U R E

Open Data

Weather Data

Risk Selecction

Automate Processes

[P#14] Big Data & IoT for Agricultural Insurance Industry [AGRO]

Enable insurance companies to exploit the untapped market potential ofAgricultural Insurance (AgI), taking advantage of innovations in EarthObservation (EO), weather intelligence & ICT technology

Big Data / ML&AI / IoT

3This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

P#13 – Alternative & automated insurance risk selection for SMEs

• Business: Automate insurance risk and underwriting processes and personalized product recommendation

• Technical: Flexible data acquisition, use of AI /ML and performing Big Data in low latency management and high flexible connectivity

P#14 – Big Data & IoT for the Agricultural Insurance Industry

• Business: Reduce weather prediction uncertainty, reduce number of presence visits reducing cost and obtain more accurate contracts

• Technical: using HPC infrastructure to better weather prediction, modular architecture to deploy faster solution and blockchain to deploy smart contracs

Highlights - Innovation

4This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

Pilot #13Alternative and automated insurance risk selection and

insurance product recommendation for SME’s

Carlos Albo, carlos@Wenalyze.com WENALYZE

5This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

Problem

UP to 70 percent of the critical data insurers collect from

enterprises customers is wrong. This problem adds manual tasks whileignoring customers real risks.

Financial corporations use outdated internal information that has been gathered by agents and brokers or by using specific data sources.

Insurers often have manual checking tasks on their underwriting and renewal processes to minimize this problem, using lower quality of data and slowing their decisions.

6This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

How it works Pilot #13

Enriched Data +

Build Risk Indicators

Financial Entity / Bank

API

Web Application

Public Registers

Customer Reviews

Business Websites

Other Sources

PILOT #13 - Alternative and automated insurance risk selection and insurance product recommendation for SME’s

7This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

Pilot #13 - Workflow 4 Steps

Data is captured 50 data points per secondare matched from hundreds of data sources

1

2

3

4

Insurer sends data including customer business Name and Postal Code

Data is automatically analyzed, compared, updated, verified and enriched

Data is delivered back to the insurer with seamless integration

10This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

Pilot#13– Business Innovation

Fast Quoting

Risk Mapping

Data Quality Increase

Adding Risk Indicators to Increase Pricing Models Accuracy

Updating, Completing and Verifying Current Data

Automating Underwriting and Renewals Processes

11This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

Pilot#13– KPI’s

Fast Quoting60-100%

Risk Mapping12 indicators

Data Quality Increase80-90%

Adding Risk Indicators to Increase Pricing Models Accuracy

Updating, Completing and Verifying Current Data

Automating Underwriting and Renewals Processes

12This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

Pilot #14Big Data & IoT for the Agricultural Insurance Industry

Gregory Mygdakos , mygdakos@agroapps.gr AgroApps PC.

13This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

The problem

Estimating Risk-premiums based on historical-past calamities data.

• historic records are sometimes absent or not sufficiently accurate• high variability of extreme weather events* causes uncertainty of future Risk

Estimation of the damage at the insured parcel by on spot visits

• increases operational costs (22-24% of premiums value**)• insurance agents are less objective and less “independent” leading to biased

assessments & higher costs

On spot visits to check compliance • increased operational costs & are not efficient!

*Top risk in likelihood and impact in The World Economic Forum Global Risk Report for 2018, 2017, 2016.**U.S. Department of Agriculture, Risk Management Agency. Crop Indemnities Maps, 2016

14This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

Big Data & IoT for the Agricultural Insurance Industry

Delivers INFINITECH AgI module, a commercial service module that will enable insurance companies to exploit theuntapped market potential of Agricultural Insurance (AgI), taking advantage of innovations in Earth Observation (EO),weather intelligence & ICT technology

Infinitech AgI module supports:

Intelligent risk assessment and

risk mappingInsurance Product development

Weather and climate

information services

Remote damage

assessment

Business Intelligence for

portfolio management

15This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

Pilot #14 - WorkflowData integrator

● AOI & Metadata● WIE Products● Octopush Products

Insurance companyServicesData sources

● User interface● Visualized data● Thematic maps

Crop Monitoring

Pest & Disease Services

Damage Assessment Services

Octopush EOService

Weather Forecast Services

Climate Services

Weather Intel.Engine (WIE)

17This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

Pilot 14 – Business Innovation

Intelligent risk assessment& risk mapping

Business Intelligence for portfolio management

Weather and climate information services

Remote damageassessment

Crop Monitoring

Pest & Disease Services

Damage Assessment Services

Octopush EOService

Weather Forecast Services

Climate Services

Weather Intel.Engine (WIE)

Insurance Productdevelopment

Infinitech AgI module will:

1. Allow AgI companies to

alleviate the effect of weather

uncertainty when estimating

risk of AgIproducts,

2. Reduce the number of on-site

visits for claim verification,

reduce operational and

administrative costs for

monitoring of insured indexes

and contract handling, and

3. Design more accurate and

personalized contracts.

18This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

Pilot #14 – KPI’s

Key Performance Indicators (KPIs) & Target values

% increase in accuracy of risk assessment (average): 35%

% decrease in operational costs (average): 25%

AgI Process cycle-time decrease (%): 60%

% of automation workflow achieved on AgI company level: 90%

% of automation workflow achieved on AgI supply chain level: 80%

19This project has received funding from the European Union’s horizon 2020 research and innovation programme under grant agreement no 856632

Flagship initiative for Big Data in Finance and Insurance

Carlos Albo (WEA)

Thanks for your attention

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