first national bank presentation at the chief analytics officer, africa 2016

Attaching Value to Data through Experimental Work

including Looking at the Big Data & Analytics

Phenomenon to Build Use Cases

Dr. Mark Nasila

Head of Advanced

Analytics

First National Bank

Presenter Background

PHD (Mathematical Statistics) 2014, NMMU

MSc. Cum Laude (Mathematical Statistics) 2008, NMMU

BSc. Hons (Mathematical Statistics) 2007, NMMU

Chair - First National Bank Advanced Analytics Committee.

FirstRand Group Assets and Liabilities analytics committee

First National Bank Data Executive committee

Fraud analytics committee (Chair)

Financial Crime Risk Model Validation Committee

First National Bank Non Credit Risk Model Validation Committee

Design and facilitate the data science lab

Head of Advanced Analytics, First National Bank : 2015 –Present

Head of Advanced Analytics, Financial Risk Management, First National Bank : 2014 –2015

Quantitative Analyst, Premium and Core Banking, First National Bank : 2012 –2014

Quantitative Analyst, Core Banking Solutions, First National Bank : 2010 –2012

Associate Lecturer, Statistics Department, Nelson Mandela Metropolitan University: 2008 –2010

• Nasila, M.W. and Sharp, G.D. (2011) The Power of Statistical Methods in Detecting and Preventing Card Fraud,

Proceedings of the International Banking Conference (IBC) held in Durban and On board On the MSC Sinfonia

Ocean Liner, South Africa.

• Chair of a session, “Statistics and the web” at the ISI (International Statistics Institute) Conference, 2009.

• Nasila, M.W. and Litvine, I.N. (2009) Statistical models for pricing weather derivatives for major South African

Coastal areas, Proceedings of the European simulation and modelling conference held in Leicester, London.

• Co-Author of Conference paper: The Determination of an Environmental Service for a Contingent Valuation Study –

Using R to Compute Estimates. Gary Sharp, David Friskin, Stephen Hosking, Catherine Logie, Mark Nasila, Henri

van der Westhuizen.

Outline

Analytics for decision making: Business Value through Use Cases

Deriving value while in the analytics journey: from infancy to maturity

Creating a balance between Descriptive, Predictive and Prescriptive Analytics

Analytics skills are scarce: how do you manage them?

The role of a Chief Analytics Officer/Head of Advanced Analytics in business

Analytics for decision making: Business Value

through Use Cases

Deriving Value Using Big Data and

Analytics

Data Storage Systems

Structured and

Unstructured Data

Data Engineers

and Scientists

Analytical Platforms


through Use Cases

What do you really want to achieve?

Increased customer loyalty? Better customer engagement?

A greater share of wallet via cross-sell? New customers?

Lower attrition?

In other words, what is the use case? What exactly are the types of

business problems big data analytics likely to solve?

“For this you need a mini-MBA in Big Data Use Cases”

(worldpress.com).


through Use Cases

Industry specific vs. Process specific Use Cases

Use cases vs. Use case Enablers

Use case Enablers

Data Mining Techniques (descriptive statistics)

Analytical platforms e.g. SAS, R, Python

Predictive and Machine learning models

(Inferential Statistics)

Specialised datasets e.g. geo location, contact

ability data, call Centre agent data

Big Data Use Cases in Telecommunications

Insurance fraud detection

Campaign and sales program optimisation

Brand management

Care Management

Patient care quality and program analysis

Medical Device and Pharma Supply-chain management

Drug discovery and development analysis

Clinical trail optimisation

Patient Engagement

Health Behavior Modification

Prescription Adherence

Prescription Fulfillment

Big Data Use Cases in the Health Care Industry

Financial crime management

Card Fraud, application fraud, money laundering analytics, investigations

Internal fraud, false claims etc.….

Marketing

Campaign and sales program optimisation

Risk Management and Profiling

Credit risk management e.g. Intelligent Credit default risk models

Compliance and regulatory reporting

Regulatory compliance e.g. Market Conduct Risk etc.

Customer Segmentation

Customer analytics, Call center interaction analytics (including Demand

Prediction)

Customer sentiment analytics

Customer Churn prediction and management

Attrition prediction, Designing loyalty and rewarding programs

Big Data Use Cases in Banking

Deriving value while on the analytics journey:

from infancy to maturity

The Old Way of Analytics

Business Idea Business case Executive Approval –

(Budget Approval)

1.Identify People

2.Select Resources

3. Infrastructure

Explore Data Develop Analytics

Implement Analytics

Derive NPV

The New Way of Analytics

Business Idea Business case Executive Approval –

(Budget Approval)

1.Identify People

2.Select Resources

3. Infrastructure

Explore Data Experimental Environment

Develop Analytics

(POC)

Implement Analytics

(POC)

Derive NPV





Experimental Environment/Innovation Lab

BIG DATA

Structured

Unstructured

INNOVATION LAB

SAS

R

Python

Claudera

IBM

STATISTICA

DATA- DRIVEN USE

CASES



So what really Goes on in the Data science

Lab?

New Mindset

Visibility & Awareness

Encourage to Fail

Recognition &

Incentives

Challenge the

business-as-usual

Collaboration

Enablers

Big Data Use Cases for Telcos

Creating a balance between Descriptive,

Predictive and Prescriptive Analytics

Descriptive Analytics Describe, show or summarise data in a meaningful

way such that, for example, patterns might emerge from the data.

Does not allow to make conclusions regarding any hypotheses we might have made.

Measures of central tendency Mode, Median, and Mean

Measures of spread Range, Quartiles, Absolute Deviation, Variance and

Standard Deviation.

Normally presented using Graphical and tabulation techniques or even plain commentary.



Predictive Analytics ( a subset of inferential

analytics)

Inferential analytics ~ are techniques that allow

to make generalisations about the populations.

Estimation inferential techniques Forecasting (demand)

Predictive modelling of events e.g. Product take-up, fraud

Hypothèses testing e.g. Effect of interest rate increase on credit defult risk and profit

margins

Effect of personalised marketing on customer loyalty

Prescriptive Analytics Analytics dedicated to finding the best course of action for a

given descriptive and inferential analytics insights.

Explores all possible scenarios (outcomes)

Incorporates real life operational factors in decision making e.g. real time capability, available analytics deployment platforms.

May play a role of recommending the best strategy to follow given the available insights

E.g. Given the number of transactions processed daily, the accuracy of the fraud model and the complexity of the fraud model, we should have real time fraud declines



Descriptive Analytics Understand at an aggregate level what is going on.

Summarize and describe different business aspects.

Predictive Analytics Need to know something about the future

Fill in the information that you do not have.

Prescriptive Analytics Need to provide users with advice on what action to

take.



Analytics skills are scarce: how do you

manage them?

Analytics skills are scarce: how do you

manage them?

Tips for Retaining

Analysts with Data Science

Skills

Make a business

connection

Set clear roles and

expectations

Feed their love of tools and technologies

Pay them Well

Demonstrate a Culture of Data

Science Understanding

Treat them as distinct

Provide managerial

support

The role of a Chief Analytics Officer in the Data

and Analytics Strategy

“It's Time To Welcome The Chief Analytics Officer To The C-suite”, Frank Bien - CEO of Looker.

• Chief Analytics Officer (CAO) – Deriving business value using big data and analytics

within an organisation

– New C-Level position Driven by organisations desire to turn big data into a strategic asset

– Few Organisations have the role of CAO defined within their existing c-suite, but have the “Advanced analytics head” at a lower level

– Owns the resulting ROI/NPV on both top line and bottom line numbers

Trends Driving the Chief Analytics Officer Role

Organisations are increasingly focused on business

ability to innovate quickly using big data and

analytics

Business user self-service is a key requirement for

all organisations

Implement best practices will accelerate decision-

making

Tailored specific solution for data and analytics

related needs

Enable more users inside and outside of an

organisation to access enterprise analytics

The role of a Chief Analytics Officer in the Data

and Analytics Strategy

Attributes of a chief analytics

officer

bridge builder

analytics, technology

and business disciplines

Encourages Fail fast --

within reason

Be a change leader,

operationally and culturally

Connected and Keeps abreast of analytical

developments

Identify opportunities

to gain advantage

Data Science and Statistical

Expertise

Embraces Outcome

based analytics

Thank You