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Analyses and processing of big
data in financial services
Fisnik Doko
Content
• What is Big Data?
• 4 V‘s of Big Data
• Turning Big Data into Value
• Big Data Analytics
• Trends in 2016 for financial services
• Big Data use cases - Finance
• Big data in central banks
• Credit Registry of NBRM
• Questions
2
What is Big Data?
• Big data is data that exceeds the processing capacity of
conventional database systems
• The data is too big, moves too fast, or doesn't fit the
structures of your database architectures
• Aim to solve new problems or old problems in a better way
3
4
4 V‘s of Big Data
More data More relevant data
Real-time streaming dataDifferent types of data
From… …To
Terabyte
≈ 1,000
Gigabyte
≈ 1,000,000,000,000
Gigabyte
Zettabyte Mountain of data Useful insights
Business/structured data Unstructured data
Monthly-based reporting
Fixed frequency data input Continuous flowing data input
…ToFrom…
Real-time reporting
From… …To …ToFrom…
Interpretable Uninterpretable
Relevant Irrelevant
Information
The 4Vs
model
t+30
Live
update
TXT
Twitter Facebook
Insights
(novel info)
Signal
(relevant info)
TXT
5
Turning Big Data into Value
The ‘Datafication’ of
our World;
• Activities
• Conversations
• Words
• Voice
• Social Media
• Browser logs
• Photos
• Videos
• Sensors
• Etc.
Volume
Veracity
Variety
Velocity
Analysing
Big Data:
• Text
analytics
• Sentiment
analysis
• Face
recognition
• Voice
analytics
• Movement
analytics
• Etc.
Value
6
Big Data Analytics
• Examining large amount of data
• Appropriate information
• Identification of hidden patterns, unknown correlations
• Competitive advantage
• Better business decisions: strategic and operational
• Effective marketing, customer satisfaction, increased
revenue
7
Trends in 2016 for financial services
• Banks are continuing to make progress on drafting big data
strategies
• Machine learning will accelerate
• Gaps will become more evident between the leaders and
the laggards
• Data governance and other compliance aspects will become
more deeply integrated with big data platforms
• Financial services organizations are struggling to understand
how to leverage IoT data
8
Big Data use cases - Finance
• Credem Bank - uses data analytics to predict - increased
revenue 22%
• MasterCard - analyze the behavior of customers – find
insights
• Morgon Stanley (financial services firm) - determine the
impact of a particular market event, as well as its original
cause
• Radobank - analysed criminal activities at ATMs
• Zions Bank – froud detection – receiving data from many
sources
• Bundesbank - Expert panels and planning
• Chinese Banking Corporation - marketing strategy
9
Big data in central banks
• Central banks have an active interest in big data
• Develop their own data platforms to handle regulatory data
collection
• Useful for research
• Monetary policy is seen to benefit most from big data
• Central banks actual involvement in the use of big data is
currently limited
• Seminars about big data in central banks in many countries
10
Credit Registry of NBRM
• Information's about credit exposure of banks and clients in
RM
• Improving credit quality and to maintain stability of banking
system
• Biggest database in NBRM
• Will be used for big data analytical purposes
• Applying technological analyzes
• Gain information and value
11
Questions
?
12

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Analyses and processing of big data in financial services

  • 1. Analyses and processing of big data in financial services Fisnik Doko
  • 2. Content • What is Big Data? • 4 V‘s of Big Data • Turning Big Data into Value • Big Data Analytics • Trends in 2016 for financial services • Big Data use cases - Finance • Big data in central banks • Credit Registry of NBRM • Questions 2
  • 3. What is Big Data? • Big data is data that exceeds the processing capacity of conventional database systems • The data is too big, moves too fast, or doesn't fit the structures of your database architectures • Aim to solve new problems or old problems in a better way 3
  • 4. 4
  • 5. 4 V‘s of Big Data More data More relevant data Real-time streaming dataDifferent types of data From… …To Terabyte ≈ 1,000 Gigabyte ≈ 1,000,000,000,000 Gigabyte Zettabyte Mountain of data Useful insights Business/structured data Unstructured data Monthly-based reporting Fixed frequency data input Continuous flowing data input …ToFrom… Real-time reporting From… …To …ToFrom… Interpretable Uninterpretable Relevant Irrelevant Information The 4Vs model t+30 Live update TXT Twitter Facebook Insights (novel info) Signal (relevant info) TXT 5
  • 6. Turning Big Data into Value The ‘Datafication’ of our World; • Activities • Conversations • Words • Voice • Social Media • Browser logs • Photos • Videos • Sensors • Etc. Volume Veracity Variety Velocity Analysing Big Data: • Text analytics • Sentiment analysis • Face recognition • Voice analytics • Movement analytics • Etc. Value 6
  • 7. Big Data Analytics • Examining large amount of data • Appropriate information • Identification of hidden patterns, unknown correlations • Competitive advantage • Better business decisions: strategic and operational • Effective marketing, customer satisfaction, increased revenue 7
  • 8. Trends in 2016 for financial services • Banks are continuing to make progress on drafting big data strategies • Machine learning will accelerate • Gaps will become more evident between the leaders and the laggards • Data governance and other compliance aspects will become more deeply integrated with big data platforms • Financial services organizations are struggling to understand how to leverage IoT data 8
  • 9. Big Data use cases - Finance • Credem Bank - uses data analytics to predict - increased revenue 22% • MasterCard - analyze the behavior of customers – find insights • Morgon Stanley (financial services firm) - determine the impact of a particular market event, as well as its original cause • Radobank - analysed criminal activities at ATMs • Zions Bank – froud detection – receiving data from many sources • Bundesbank - Expert panels and planning • Chinese Banking Corporation - marketing strategy 9
  • 10. Big data in central banks • Central banks have an active interest in big data • Develop their own data platforms to handle regulatory data collection • Useful for research • Monetary policy is seen to benefit most from big data • Central banks actual involvement in the use of big data is currently limited • Seminars about big data in central banks in many countries 10
  • 11. Credit Registry of NBRM • Information's about credit exposure of banks and clients in RM • Improving credit quality and to maintain stability of banking system • Biggest database in NBRM • Will be used for big data analytical purposes • Applying technological analyzes • Gain information and value 11