My Professional Journey in digitalization of seven industries as Educational intelligence. Data intelligence into Fintech insights, actions, solutions. Practical Reference: 7 Machine Learning Algorithms. Examples for the build-out of the data and analytic architecture from my personal experience in seven industries

1. Digital Intelligence for
Fintech.
7 by 7 practical view.
Professional guide
‘How to use 7 ML methods
in 7 fintech industries.’
Ph. D. in AI Irina Muhina

2. Agenda
My Professional Journey in digitalization of seven
industries as Educational intelligence
Data intelligence into Fintech insights, actions, solutions
Practical Reference: 7 Machine Learning Algorithms
Examples for the build-out of the data and analytic
architecture from my personal experience in seven industries
Questions and answers
2

4. My Professional Journey into data intelligence.
• Royal & Sun Alliance Insurance. Statistician, Actuarial Department. I conducted detailed econometric
analysis and comprehensive stochastic and statistical modeling and interpreted data to identify clear
patterns and trends that could be useful to management.
• Bank of Montreal. Senior Manager Marketing Analytics and Campaigns. I guided a team of 18 Data
Analysts and Statisticians in conducting comprehensive bank enterprise analysis of customer insights,
performing customer segmentation, and creating models based on data mined from internal and external
sources.
• RBC Royal Bank. Manager Predictive Modeling; Strategic Marketing Research and Analytics.
I provided management with insights that were crucial for informed strategic and tactical decision-
making across all lines of business.
• Exchange Solutions, Consulting. Value Management Architect. I spearheaded the development of
economic models, with focus on exchange value between customer and financial institution, and relied on
them to create and deploy high-ROI Customer Loyalty Value Management solutions.
• Manulife Financial, Global Investment Management. Assistant VP, Strategic Asset Allocation. I have
conceived and developed BIF (Business Intelligent Framework), a solution that combines the power of
data feeds, analytical tools, expert knowledge, proprietary models and best practices, turning unrelated
information into meaningful insights that facilitate accelerated trade-off decision-making
• Manulife Asset Management. Managing Director. R&D I have lead end-to-end analysis of strategic asset
allocations, and carefully evaluate and coordinate Fund Managers’ performance – all to maximize client
portfolio returns by implementing better strategic and tactical methodology.
• iECARUS. Innovative Educational Center for Art and Science. Founder.

5. Managing my Professional Journey for digital success
1990 2000 2010 2020
g
smart
Statistics
Data Mining
Text Mining
Big Data
Smart Data
AI IOT
insutech iCustomer iBanking iLoyalty iPensionsiRiskAsset allocation
Programming , mining data, analyzing, visualizing results. Managing people, projects, data, asset, money.
Leading real, virtual, diversifying team. Developing analytical frameworks. Coaching and mentoring students.

6. #DataFest2019

7. Data intelligence into insights, and into Fintech solutions.
What
happened?
Why Did It
Happen?
What Will
Happen?
VALUE
Data Lake
Descriptive
Analytics
Diagnostic
Analytics
Predictive
Analytics
Prescriptive
Analytics
Facts
Hindsight
Insight
Foresight
How can I make (prevent)
it (from) Happen(ing)
Imperative Agile
Decision Management
DIFFICULTY
Turning data
into info
.. And insights into
recommendations
Turning info
into insights
Feedback from
test and learn

8. Practical Reference: 7 Machine Learning. Algorithms
ML Algorithms Practical usage
• Creating a data-dependent number of arbitrarily shaped, disjoint, or overlapping clusters of different sizes
• Creating a known a priori number of nonspherical, disjoint, or overlapping clusters of different sizes
• Creating a known a priori number of spherical, disjoint, equally sized clusters
• k-modes method can be used for categorical data
• k-prototypes method can be used for mixed data
• Modeling nonlinear and nonlinearly separable phenomena in large, dirty data
• Interactions considered automatically, but implicitly
• Missing values and outliers in input variables handled automatically in many implementations
• Decision tree ensembles, e.g., random forests and gradient boosting, can increase prediction
• accuracy and decrease overfitting, but also decrease scalability and interpretability
• Modeling linearly separable phenomena in large data sets
• Well-suited for extremely large data sets where complex methods are intractable
Bayesian
Decision tree
Clustering
Regression
• Modeling linear or linearly separable phenomena
• Manually specifying nonlinear and explicit interaction terms
• Well suited for N << p
• Building sets of complex rules by using the co-occurrence of items or events in transactional
data sets
Association rule
Dimension reduction
Artificial Neural
Network
• Extracting a data-dependent number of linear, orthogonal features, where N >> p
• Extracted features can be rotated to increase interpretability, but orthogonality is usually lost
• Singular value decomposition (SVD) is often used instead of PCA on wide or sparse data
• Sparse PCA can be used to create more interpretable features, but orthogonality is lost
• Kernel PCA can be used to extract nonlinear features
• Modeling linear or linearly separable phenomena by using linear kernels
• Modeling nonlinear or nonlinearly separable phenomena by using nonlinear kernels
• Anomaly detection with one-class SVM (OSVM)
Moderate
Moderate
Moderate
Moderate
Generally low
Low
High
Interpretability

9. Insutech is lags other sectors in digital maturity.
Source: McKinsey&Company
iNSUTECH

10. The Path of Customer Analytics Value
Based upon State Data Architecture
IncreasingIntegrationandQuerySophistication
Timeline: (Increasing Data Detail, Volume, Integration, and Schema Sophistication)
Selected
Information
Correlations
and Causality
Price Time Series &
sophisticated
Client/Firm Analytics
Real-time
analytics /
consistency across
channels
Near Real time
monitoring with
Automated Action
Analyzing
Reporting
Modeling Insights
Work-flow integration
Adoption
Which agents were
the months top
performers?
What are the
characteristics of
plan participants
most likely to
upgrade?
What agents and
clients are most
likely to take up
new product?
Create high
propensity client
offer based on
multiple factors
ABC to call a plan
participant based
on current
browsing of the
website with high
propensity offer
Business Value
BMO Current Position
10
Customer
Analytics Value

11. Loyalty as a Fintech Solution
Understanding Marketing Effectiveness
ONLINE/OFFLINE
TRANSACTIONS
Website
Visits
Mobile/Table
t App Visits
Email
Activity
Website
Referrals
Call Centre
Interaction
Social Site
Referrals
Display Re-
targetting
Search
(Organic/PPC)
RESULT (Retailer): 10% Saving in
Annual Digital Marketing Budget
Advanced Attribution helps
understand the value in
marketing spend across
multiple channels to identify
which combination of
marketing events are most
effective in converting the
customer.
PERSONALISED WEIGHTED MODEL
0.03
0.17
0.13
0.14
0.5
iLOYALTY

12. ASSET
ALLOCATION

13. iPENSION

14. Factor Decomposition Across Different Time Intervals & Regimes iRISK

15. Realized Risk-Return Trade-off, Different Optimization Methods, Resampling
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 18.0%
Risk
Return
Min Variance
Risk-Weighted
Maximum Diversification
Equal Weight
Minimum Correlation
Minimum Average Correlation
MVO
iOptimization

16. iECARUS
EDUCATIONAL INTELLEGENCE

17. Questions ?