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Machine Learning and Data Access (Data Access (More sources = better model…
Machine Learning and Data Access
Fintech
Opportunities
Cost reduction
Differentiaition
Improved retention of customers
Additional revenues
lanscape is booming
computer programs/technology to support banking and financial services
Data Science
the convergence of three skill sets
Coding/hacking
Build models
Get data/manipulate
Math/Algorithms
Statistics
What you want the computer to do
Domain Expertise
understand the business problem
Critical
Four keys to building a competitive advantage
Have the right data when/where you need it
providers
Make it actionable
harnessing this data comes with significant obstacles
Train everyone to identify opportunities
employees rely more on
everyone needs to be on the look out for opportunities to use machine learning
few data scientists work for you and understand your business
Execute on as many opportunities as possible
Predictive Analytics
Two Stages
Stage 1: Training, Fitting, building
Historical examples to learn from
A modeling blueprint
Stage 2: Predictions, Scoring, Implementation
new data
Model used
Artificial Intelligence vs. Machine Leaning
AI
Make decisions and take action
Assemble data
Use predictions from machine learning to make the "right" decision
Systematically apply business logi
Machine learning
Calculate predictions
Learn from historical examples
developed to answer narrow well-defined questions
Data Access
More data matters?
which data matters?
More sources = better model
make better decisions
Why?
Adding more data reduces the information gap
make more decisions on more people
A growing challenge
90% of the data out there was created in the past 10 years
Repercussions of missing out
there is a cost of missing out