2. Agenda
• Factors enabling data monetization
• Traditional stumbling blocks for data monetization
• Ethical aspects of data monetization
• Technology –Transformation in the value chain
• Conceptual framework for data monetization
• Pathways to data monetization
• Examples – Internal
• Examples- External
3. Factors enabling data
monetization
• Technology
• Availability of data
• Data storage capabilities
• Customer personalization
• IoT
• Profitability pressures
• Commoditization of analytics
• Fraud and AML
4. Technology – what has changed
Computing technology1 Data explosion4 Mobility7
Natural language processing2 Image analytics5 Social media8
Artificial Intelligence3 Cloud6 Open source9
5. Traditional
stumbling
blocks for data
monetization
• Business sponsorship
• Legacy infrastructure
• Availability of talent
• SME dependency of
data
• Robust sandbox
• Scale up analytics
• Business - IT
disconnect on data @Copyright Accenture
6. Ethical aspects of
data monetization
• Privacy issues
• Anonymization
• Customer agreement
• Marketing
• Transparency
• Gaining trust
• Unfair biases
• Impact to brand
7. Technology – Transformation in the
insurance value chain
• Channel digitization
• Insight driven customer experience
• The changing role of agent
• The future of aggregators
• The role of ecosystems
• The internet of things
8. 4
12
3
TRANSFORMEDTRADITIONAL
Future Ready
IndustrializedSilos and Spaghetti
Integrated Experience
Operational Efficiency
TRANSFORMEDTRADITIONAL
CustomerExperienceThe Four Pathways to Future ReadyResearch by Barbara H Wixcom @ Jeanne W. Ross mitsmr.com
Increasing automation, standardizing, reuse, and productivity
Increasingcustomerfocus
9. Conceptual Framework for monetization
Focus on highest value opportunities that are aligned to the overall company business strategy
Production
Sandbox
Discover Insights Embed insights in
Business process
IT
Business
Data Science
Actuarial analysis
Reports/Trending
Production models
Actuarial consumption
10. Pathways to data
monetization
•Internal
•Top Line growth
•Profitability
•Cost reduction
•Customer personalization
•External
•Data as a service
•Insight as a service
•Analytics as a service
11. Examples - Internal
Top line growth
• Smart targeting, Innovative product design
•Expand risk appetite using location analytics and sensors
•Next best action
Profitability
•Price optimization and retention, Risk assessment, Telematics, Preventive analytics
•Fraud detection, Producer segmentation and commission optimization
•Actuarial analysis
Cost reduction
•Call center analytics, reduced cost of data preparation
•AI in claims and underwriting, Image analytics in claims
•Automation
Customer personalization
•Customer service, Personalized offers
•Customer life time value, segmentation and
•Early engagement and cross sell
12. Examples - External
Data as a service
Aggregated or Anonymized data is sold to intermediate companies or
end customers
Insight as a service
Actionable insights are offered using a combination of internal and
external data available
Analytics as a service
Analytics is available on demand.
Example: Predictive model is available as an API
13. Profitability
Customer
Profitability
Customer Life
Time Value
Data and analytics framework for BFSI
Channel
Profitability
Location
Profitability
Product
Profitability
Risk
Credit Risk
Fraud, AML
Liquidity Risk
Collections and
Credit Exposure
Default
Management
Asset and Liability Management
Production
Capital
Allocation
Analysis
Liquidity
Analysis
Credit Loss
Provision
Net Interest
Margin Variance
Fund Maturity
Analysis
Structured
Finance Analysis
Campaign
Analysis
Call Center
Analytics
Cross Sell/Up
Sell- Analysis
Customer
Acquisition
Customer
propensity
Customer
Loyalty
Copy right @ https://www.anblicks.com/industries/bfsi/
14. citations
1. Demystifying data – Article in MIT Sloan Management Review
https://sloanreview.mit.edu/article/demystifying-data-monetization/
2. Data and analytics in the insurance industry – Deloitte
https://www2.deloitte.com/content/dam/Deloitte/nl/Documents/financial-services/deloitte-nl-fsi-
insurance-data-analytics-within-the-insurance-industry.pdf
3. Data rich profit poor – Accenture
https://www.accenture.com/us-en/insight-data-rich-profit-poor
4. Harnessing the potential of data in insurance – McKinsey
https://www.mckinsey.com/industries/financial-services/our-insights/harnessing-the-potential-of-data-in-
insurance
