Presentation at Data Science Festival, Dublin.
Advanced Analytics deals with the automatic discovery and communication of meaningful patterns in structured and unstructured data. Jamie will explain how Advanced Analytics methods are being used within Bank of Ireland to deliver measurable business value through cross functional, agile teams. Jamie will describe the importance of strategic transformation, value prioritisation, the Hub & Spoke engagement model and a scalable Analytics platform in driving a culture of data-driven decision-making. Jamie will show how the Data Analytics Community of Practice and Peer-led training initiatives are supporting the move toward AI, Machine Learning, and Natural Language Processing.
2. Agenda
• Ways of Working: Agile Analytics
• Analytics Community
• Analytics Tools @ BOI
• Machine Learning Use Cases
• Analytics teams
• People Analytics
• Graph Analytics
3. Ways of Working: Agile Analytics
• Hub and Spoke Operating Model
• Collaboration through Agile delivery
• Value-prioritised analytics projects delivered in a Scrum team
• Design thinking (is a non-linear, iterative process that provides a solution-
based approach to solving problems)
• Focus on realising the value in Bank’s data assets
4. Analytics Community @ BOI
• Networking events
• Expert external speakers
• Lunch and learn events
• Data literacy and data democracy
• Capability development initiatives
• Explore Open Data (Property Price Data)
6. Machine Learning Use Cases
• Regression: Forecast the future by estimating the relationship between
variables (Estimate product demand, Predict sales figures, Analyse
marketing returns)
• Anomaly Detection: Identify and predict rare or unusual data points
(Predict credit risk, Detect fraud)
• Clustering: Separate similar data points into intuitive groups (Perform
customer segmentation, Predict customer tastes, Determine market price)
• Classification: Identify what category new information belongs in
(automatically categorise bank transactions into groups like ‘travel’,
‘groceries’, ‘eating out’)
7. Advanced Analytics
• Financial Wellness to enable optimum spend/saving patterns for
Employees & generate savings insights based on peer comparison
• ATM Cash Replenishment Optimisation to understand impact of ATM
removal from busy high streets.
• Graph Analytics to support Business partnerships and identify key business
influencers)
8. Analytics in Fraud
• Understand typical customer spending behaviour and review (anomaly
detection)
• Understand customer types who are likely to have their details
compromised online
• Identify potential points of compromise (ATM skimming)
9. Analytics in Risk
• Credit lending process: Analytic models are used to inform and automate
lending decisions
• Market risk management: Guide, monitor and limit the amount of risk our
traders are taking in the markets
• Enterprise management process: Stress testing where we use scenario
projection analytics to understand the potential implications of external
events
10. People Analytics
• Carry out data-driven evaluations of our various programmes of work
• Investigate linkages, trends and correlations in activity across the
employee life cycle
• Understand and predict employee engagement, cultural embeddedness,
people risk and employee attrition
11. Data & Analytics Self-Assessment Survey
• Categorising Survey Respondents into 6 Analytics Personas
•
• Identify Skill Gaps and develop training approach to address
(Machine Learning / AI, Scrum, Predictive Modelling, Design Thinking)
• Finding Optimal Mentor-Mentee Matches (K-Nearest Neighbours method)
13. Peer-led Training
• Teradata Learning & Certification Group
• Tableau Masterclass Series
• Kaggle Santander Customer Transaction Prediction Challenge
• Machine Learning Algorithm Masterclass Series
• Analytics Institute Certification
• Soft skills development for Graduate Programme & Analytics teams
WHY: “Give an Insight into how the Analytics teams operate @ BOI”
-Methodology (Agile, Value Prioritisation, Design Thinking)
-Tools
-Analytics Teams
-Algorithms & Use Cases
-Analytics Community & Initiatives
-Roles
-Future Projects
Group Data Office, Risk, Decision Science, Marketing, HR, Fraud
The team is built on three pillars: value prioritisation, design thinking and agile delivery.
deliver value-prioritised analytics projects in a scrum team on behalf internal customers
accelerate the adoption of analytics to transform the bank, engage our customers and deliver value.
an agile environment, value prioritisation and design thinking
Scrum uses Value-based Prioritization as one of the core principles
Teradata, Tableau
SAS, R, Python, Spark
Excel
Cloudera
Google Analytics
Financial Wellness embedded ML Dashboard POC to enable optimum spend/saving patterns for Employees & generate savings insights based on peer comparison
ATM Cash Replenishment Optimisation , impact of ATM removal from busy high streets.
Graph Analytics – New SME partnerships , identify key business influencers throughout Retail Network based on transaction spend from our Customer Base.
Predicting HR Employee turnover,(attrition) - significant cost reduction & explanations of main drivers statistically.
we use analytics within fraud:
To understand atypical customer spending behaviour and review them – part of ‘anomaly detection’
To understand customer types who are likely to have their details compromised online
To identify potential points of compromise for things like ATM skimming, etc.
we use analytics in Group Risk to assess risk and inform decisions and analytics are deeply embedded in key processes including
- in our credit lending processes where analytic models are used to inform and automate lending decisions
- in our active market risk management to guide, monitor and limit the amount of risk our traders are taking in the markets
- in our enterprise management processes including stress testing where we use scenario projection analytics to understand the potential implications of external events (like an economic recession) on our business performance.
The 7 Pillars of Successful People Analytics Implementation
https://www.ere.net/the-7-pillars-of-successful-people-analytics-implementation/
In Group Human Resources we utilise analytics to:
Carry out data-driven evaluations of our various programmes of work
Investigate linkages, trends and correlations in activity across the employee life cycle
Carry out data-led workforce planning which drives cost and effort efficiencies in the Group
Understand and predict employee engagement, cultural embeddedness, people risk and employee attrition
Analytics Capability Self-Assessment Survey
Tableau Demo (Overall Dashboard to highlight Gaps, Role Personas with aspiring candidates/gaps, Mentor/Mentee Matching)
Peer-led Training initiatives
Teradata Learning & Certification Group
Tableau Masterclass Series
Kaggle Santander Customer Transaction Prediction Challenge
Machine Learning Algorithm Masterclass Series
Analytics Institute Certification
Soft skills development for Graduate Programme & Analytics teams
Analytics Capability Self-Assessment Survey
Tableau Demo (Overall Dashboard to highlight Gaps, Role Personas with aspiring candidates/gaps, Mentor/Mentee Matching)
Peer-led Training initiatives
Teradata Learning & Certification Group
Tableau Masterclass Series
Kaggle Santander Customer Transaction Prediction Challenge
Machine Learning Algorithm Masterclass Series
Analytics Institute Certification
Soft skills development for Graduate Programme & Analytics teams
