Course support for Analytics in Action

1. Financial Services
February 2019
Analytics in Action
http://DSign4.education

2. • The authors speak of another revolution in banking.
What is the basis of this claim?
• Why do non-traditional financial companies enjoy
an advantage over traditional banks?
• What are the advantages of data-driven lending?
• Which two inherent risks in banking do Fintech
companies exploit?
• How do the authors believe traditional banks will
respond?
The FinTech Revolution?
Introduction
The Economist (2009) The Fintech Revolution

3. Introduction
©2016 L. SCHLENKER
Agenda
Introduction
Definitions
Infrastructure
Use Scenarios
Limits

4. Inputs
Prediction
Evaluation
Actions
Outcomes

5. • Financial services are a transactional
business made of information rather
than concrete goods
• New applications, processes, products,
or business models in the financial
services industry,
• Originate from independent service
providers and at least one licensed bank
or insurer
• Used to automate insurance, trading,
and risk management.
Data Analytics in Finance
Technology
Business Analytics 3.0

6. Financial Business Models
Technology
• Segment, Analyze, Develop,
Meaure
• Three traditional markets in
banking – retail, wholesale,
capital markets
• Universal banking
• Infrastructure providers
• Open platforms
• Full-fledged Aggregators
4 Banking Business Models for the Digital Age

7. Ian Dowson
• A microeconomic environment characterized
by weak growth, low inflation and low interest
rates
• A radical change in customer behavior
• New competitors with new business models
entering the financial market
• A more demanding and intrusive regulatory
environment
Market Challenges
Technology

8. • Capital One Labs – uses data science
algorithms to develop next generation of
financial products and services.
• Citi Latin America Innovation Lab offers its
commercial customers transactional datato
help clients identify novel trade patterns
• Bank of America runs BankAmeriDeals with
various cashback offers for debit and credit
card holders based on the analytics
• Credit Suisse’s Data scientists find novel
opportunities to create revenue streams; retain
customers and reduce expenses
Whose doing it?
Technology
Dezyre

9. • Spending pattern of customers
• Channel usages
• Customer Segmentation and Profiling
• Product Cross Selling based on the profiling to
increase hit rate
• Sentiment and feedback analysis
• Security and fraud management
Use Scenarios
Technology
The Financial Brand

10. Ian Dowson
 Banks can serve their customers with their
preferred channel by leveraging transactional
behaviour analytics
 Data science can find attributes and patterns
which have increased probability for fraud.
 Data science helps banks optimize the check
float criterion by considerably reducing the
bottom line costs.
 Data science can help ensure customer
satisfaction on quality of service through data-
insights on changing customer requirements
 Data science can help forecast various
profitability components such as charge-off
accounts, delinquency and closure that help
them make effective product and pricing
decisions.
Value Levers
Technology

11. • Customer life event analysis
• Real time allocation based offerings
• Quality of lead analytics
• Micro-segmentation
• Customer Gamification
• DIsclosure reporting
• Anti-money laundering
• IVR analysis
• B2B merchant insights
• Real time capital calculations
• Log analytics
Data Science Techniques
Technology

12. • New offerings from non-traditional players
• Diminishing margins
• Greater operational risks
• Loss of customer focus
• Ethical issues surrounding data privacy
and institutional obligations to act on
analytics findings
What are the risks?
Technology

13. • Adoption of cloud solutions
• KYC complicance to prevent fraud and
financial crimes
• Converged applications will integrate historical
and real-time financial data
• Increasing use of IoT and streaming
• Widespread implementation of Big data and
blockchain technlogies
Future trends
Technology

14. • What is the organization’s business
model?
• Why does the organization focus on
data?
• How is the Data Science team
organized?
• Which data science techniques does
the organization favor ?
• What is the link between data science
and decision making?
• How does the organization use Data
Science to propel growth
Case Study Questions
Technology

15. • Elias, J., (2014), Why Capital One Labs Is Banking On
Experimentation
• MyOnlineCA, (2016), How Banks Earn Money, (video)
• Marous, J., (2014), Customer Analytics is the Key to
Growth in Banking
• Robinson, B., (2016) 4 Banking Business Models for
the Digital Age
• Srivastava, U. , Impact of Big Data Analytics on
Banking Sector: Learning for Indian Banks
• Stringfellow, A., (2018), 20 experts reveal the most
important big data technology trends shaping banking
Bibliography
Next Steps