The document discusses how data analytics and financial technology (fintech) companies are revolutionizing the banking industry. It provides definitions of key terms like fintech and describes how fintech companies exploit inherent risks in banking through data-driven lending. Several use cases of data analytics in banking are outlined, along with some of the risks to traditional banks from these new competitors. Techniques of data science that can be applied in banking are listed. The document aims to outline how data analytics is transforming financial services.

1. Financial Services
February 2018
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. Introduction
• Place - changes in geography, time, physical
resources and budget
• Platform – enriching how information is produced
and consumed
• People – modifying the frame of reference
• Practice - impacting the reality of management
Schlenker (2015)

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