-
Analytics For The Specialty Classes
Data Standards For Credit Risks
Suki Basi
Russell Group Limited
2016
FILE 2016008
-
Specialty Classes Within The Risk Landscape
Capital Modelling
Policy Management
Multi-Class
Natural Perils
Specialty
Third Party / Client Data
Solvency Risk
Management
Underwriting Risk
Management
Underwriting Risk Distributions
FILE 2016008
-
Proposed Solution For The Specialty Classes
Multi-Class
Portfolio
Multi Class Retro
RI
Direct
Aerospace
Aviation &
Space
Non-Marine
Casualty Energy Marine
Non-Marine
Trade Credit Others
Types of Analyses
Portfolio
Multi-Class
Aggregation
Simulated
Pricing
Aggregation
Single Class
Method of Placement
Line of Business
FILE 2016008
-
Connected Risks – Multi-Class Eco System
Credit
Political
Violence
Supply Chain
Cyber
Aviation Space Energy Marine Cargo Casualty
FILE 2016008
-
Data Needs For The Credit Risks Market
• No naming convention for credit risks
• No consistent format of exchange between the various parties
in the value chain
• Aggregation becomes largely a manual process
• Variability of pricing approach
• Limited modelling of credit risk scenarios
• Overall data handling and resultant analytical processes are
inefficient
FILE 2016008
-
Insurance Data Value Chain – Data Conveyor belt
Insured Insurer Reinsurer Retro-Insurer
Broker RI Broker RI Broker
Capital Market
Captive Market
Underlying
Risk
FILE 2016008
-
Aviation Data Conveyor belt
Insured Insurer Reinsurer Retro-Insurer
Broker RI Broker RI Broker
Capital Market
Captive Market
Underlying
Risk
Airline Group
Airline
Domicile
Aircraft
Cycles
Routes
Airports
Losses
Consistent Airline name, Underlying Exposure, Market Loss
information
Consistent approach to aggregation, pricing, scenario
modelling
New product opportunities - Index triggers, warranties, ILS
FILE 2016008
-
Benefits for Aviation
• Better understanding of exposure and capital utilisation.
• Better understanding of underlying risk prior to capital
commitment.
• Improved risk selection and optimization of capital
• More accurate, consistent and timely exposure analysis
• Improved quality and efficiency of risk management across the
class
FILE 2016008
-
Credit Risks Data Conveyor belt
Insured Insurer Reinsurer Retro-Insurer
Broker RI Broker RI Broker
Capital Market
Captive Market
Underlying
Risk
Counterparty
Subsidiary
Domicile
Sector
Rating
PD
LGD
Losses
Consistent Counterparty Name, Underlying Exposure, Market
Loss
Consistent approach to aggregation, pricing, scenario
modelling
New product opportunities
FILE 2016008
-
Steps to Data Standardisation
• Implement naming convention for counterparties and
subsidiaries
• Reference counterparties by name and range of identifiers (eg,
ISIN, CUSIP)
• Link to data sources to extract underlying rating
information
• Link to economic data sources to predict change in rating
• Share loss information to implement market loss source
• Create risk profiles to exchange exposure information between
insurers and reinsurers
FILE 2016008
-
Benefits for the Credit Risks Market
• Better understanding of exposure and capital utilisation.
• Better understanding of underlying risk prior to capital
commitment.
• Improved risk selection and optimization of capital
• More accurate, consistent and timely exposure analysis
• Improved quality and efficiency of risk management across the
class
FILE 2016008
-
• Flair in delivery
• Flexibility of risk architecture
• Fluency in risk management
Why Russell
FILE 2016008
-
Thank you
FILE 2016008
