© Created by Diagon Consulting Ltd. – 2019 October

LEVERAGING DATA: MARKET

INTELLIGENCE AND THE CHALLENGE

OF CYBER THREAT

2PRIVATE & CONFIDENTIAL

PRESENTATION

AGENDA

The Great Hack

Data defined and the current view of

market intelligence

Where we are as a region?

Future view of data with two way

communication and immediate market

intel

Importance of security

01

02

04

05

03

06 It is all interconnected.

3PRIVATE & CONFIDENTIAL 3PRIVATE & CONFIDENTIAL

THE GREAT HACK

4PRIVATE & CONFIDENTIALPRIVATE & CONFIDENTIAL

►Data - Meaningless facts that when applied to a situation and context produces information

► Information – When correctly grouped, harmonized, synthesized and assimilated produces intelligence

WHAT IS DATA

GOLD < DATA > OIL

5PRIVATE & CONFIDENTIALPRIVATE & CONFIDENTIAL

Predictive analysis

analyst

Business Intelligence

prescrip

tive analysis

exploratory analysis

Machine learning

Descrip

tive

analytics

Data management

Data quality

Advanced analytics

Extraction Transformation Load

dashboard

Data lakes

datamarts

Data warehouses

visualisation

information

governance

aggregation

Multi-

dimentional

dat

abas

e

Real-time

Big d

ata

insight

KPI

regression

Decision trees

Neural network

Test data

Business performance

management

Master data management

Data store

Scenario building

Decision making

Data security

Adhoc reporitng

Power users

What will happen?

Why did it happen?

What can we do?Enterprise wide

THE PLETHORA OF TERMS

6PRIVATE & CONFIDENTIAL

VISUALISATIONS

ADVANCED ANALYTICS

TRANSACTION SYSTEMS

DATA CAPTURE TRANSFORM & STORE DELIVER

SIMPLISTIC DATA CYCLE – CURRENT STATE

Segments Channels

Mass(Natural)

Mass Affluent

Affluent

High End

MassBusiness

Small Business

Enterprises

Corporations

Contact Center

Internet Banking

Branches

Face to FaceSpecialized

IVR

ATM

Moble Banking

RELATIONAL DB AND DATA STORES

Market insight is limited to traditional data capture via channels

7PRIVATE & CONFIDENTIAL

Cross sell ratio

Customer retention rate

Customer segmentation/ profiling

Customer churn prediction

Customer insights

Product mix determination

Customer profitability measure

Marketing cost and marketing campaign effectiveness analysis

Sales channel effectiveness

Credit analysis

Visualisation

Exploratory analyticsAdvanced Analytics incl

machine learning

Data warehuses/ Data marts

DELIVER: MARKET INTELLIGENCE THROUGH ANALYTICS

Prescriptive

Descriptive/Exploratory

Predictive

KEY

DescriptiveDiagnostic PredictivePrescriptive

8PRIVATE & CONFIDENTIAL

Forward looking/ structured data/ enterprise wide

Backwards looking/structured data/

inter-divisionBackwards looking/ structured data/ divisional focus

Val

ue

Forward looking/ structured and non-

structured data/ enterprise wide

Complexity

10 %

Very few organizations have the infrastructure to support and use analytics effectively

WHERE WE ARE AS A REGION

9PRIVATE & CONFIDENTIAL

WHY ARE WE HERE?

ABSENCE OF THE RIGHT AND QTY OF DATA

INSUFFICIENT DATA ARCHITECTURES

Big Data?The "single versionof the truth"

10PRIVATE & CONFIDENTIAL

ADMIN

COMPANY A.com

TeleMar

MMB

Data SourceFunction

Email

Cust Prof

Wel Ltr

Cust Hier

Up/XSell

Self Svc Performance

Management

Including KPI

Calculation

Customer

Segmentation

Management

Reporting

Product Mix Decisions and

Product Profitability Index

Calculation

Dashboard

UNTIDY DRAWER ARCHITECTURE

11PRIVATE & CONFIDENTIAL

MDM

ADMIN

COMPANY A.com

TeleMar

MMB

Data SourceFunction

Email

Cust Prof

Welc Ltr

Cust Hier

Up/XSell

Self Svc

TIDY DRAWER ARCHITECTURE

Datamart

Datamart

12PRIVATE & CONFIDENTIAL

PREDICTING THE PROBABILITY OF MY SON PASSING

AGE X + STUDY TIME Y + IQ SCORE Z + PAST SCORES R

HOWEVER – WE DON’T HAVE DATA ON THE FOLLOWING:- Exam difficulty- Sleep before an exam- Productive study time%- Who he sat next to this term

13PRIVATE & CONFIDENTIAL

PUTTING IT TOGETHER

14PRIVATE & CONFIDENTIAL

VISUALISATIONS

ADVANCED ANALYTICS

TRANSACTION SYSTEMS

DATA CAPTURE TRANSFORM & STORE DELIVER

Segments Channels

Mass(Natural)

Mass Affluent

Affluent

High End

MassBusiness

Small Business

Enterprises

Corporations

Contact Center

Internet Banking

Branches

Face to Face Ppecialized

IVR

ATM

Moble Banking RELATIONAL DB AND DATA STORES

New channels, two way information exchange

FUTUREPROOFED DATA CAPTURE

BLOCKCHAIN

Other Bank’scustomers

API

Chatbots

PISp/ AISp

`

15PRIVATE & CONFIDENTIAL

INTERNET OF

THINGS

TWO WAY COMMUNICATION - RICHER INFO

APIS

CHATBOTS

ROBOADVISORS

16PRIVATE & CONFIDENTIAL

Bank Bank

Users Users

Internal API Public API

Third PartyApps

Proprietary Apps

Proprietary Apps

01GREATER

CUSTOMER

BASE

023RD PARTY

ACCESS TO

CUSTOMERS

OPEN API – ACCESS TO BANK’S DATA

17PRIVATE & CONFIDENTIAL

REGULATORS MOBILE ADOPTION

CUSTOMERS

DRIVERS FOR CHANGE

73% Mobile subscribers : total

population

in the Caribbean

MILLENNIALSBorn 1981-1998

Target group for most banks

GEN ZBorn 1998 to present

Greater than the Gen x but less than

Millennials

Truly mobile generation

18PRIVATE & CONFIDENTIAL

“TRADITIONAL BANKS KNOW THAT TO COMPETE, THEY MUST DEVELOP DIGITAL CAPABILITIES TO AVOID BEING DIS-INTERMEDIATED COMPLETELY BY NEW ENTRANTS WITH SUPERIOR, MORE AGILE OFFERINGS.”

“OPEN BANKING IS ABOUT MAKING EVERYTHING FOR SALE. IT PROVIDES A NEW WAY TO INCREASE DIGITAL REVENUE FOR THE BANKS THAT ARE WILLING TO THINK DIFFERENTLY...OPEN BANKS AND FINTECHS WILL CONTINUE TO ERODE MARGINS AND CUSTOMER RELATIONSHIPS FOR THOSE BANKS THAT DON’T.”

“IMAGINE A SIMPLE APPLICATION THAT WILL FIND THE CLOSEST ATM TO YOU. THE APPLICATION WILL SEND A REQUEST TO ALL FINANCIAL INSTITUTIONS USING THEIR OPEN API “

INTERESTING QUOTES

19PRIVATE & CONFIDENTIAL

2 MOST VALUABLE FORMS OF DATA

FINANCIAL DATA MEDICAL RECORDS DATA

HIPPAPCI

20PRIVATE & CONFIDENTIAL

BAD SECURITY PRACTICES RESULT IN….

• Litigation

• Brand and reputation damage

• Loss of customer and confidence

• Loss of market value

• Regulatory action

• Financial loss

• Lack of user awareness: Overuse of company credentials

• Poor application security: Easily accessible client data

• Defense in depth vs Detection and response strategies

Results in:

21PRIVATE & CONFIDENTIAL

DATA LEAKAGE – GOES UNDETECTED

22PRIVATE & CONFIDENTIAL 22PRIVATE & CONFIDENTIAL

WHERE IT ALL BEGINS – USERS ARE THE WEAK LINK MOST TIMES

23PRIVATE & CONFIDENTIAL

1. Your Asset and Network Terrain

2. Threats Targeting Your Environment

3. Valuable Data and Where it Resides

4. Adversaries’ Actions and their TTPs

5. How to Alter the Terrain to Your Advantage

Know Your Environment

Determine most likely paths of:• Exfiltration• C&C• Surveillance• Etc.

IN ORDER TO PROTECT DATA– YOU MUST UNDERSTAND……

24PRIVATE & CONFIDENTIAL

Dwell Time = The time that a threat actor goes undetected on your network before they are found

and contained.

The 2018 Ponemon Breach Study indicated the following:

“The faster a data breach can be identified and contained, the lower the costs. The Average Time to identify a threat is 197 days andto contain it is 97 days.

On average companies that identify a breach in less than 100 days can save more than amillion dollars.”

REDUCING DWELL TIME

25PRIVATE & CONFIDENTIAL 25PRIVATE & CONFIDENTIAL

SIMULATED ATTACK UNFOLDING

26PRIVATE & CONFIDENTIAL

WHAT’S NEXT FOR CARIBBEAN BANKS

REDEFINE

ARCHITECTURE

New channels, new data

structures, two-way and

immediate

START WITH SIMPLE

VISUALISATON

02

03

01

04

REMAIN RELEVANT

Strategic decisions need to be

made re digital strategy

listening to the market and the

direction of the banking world

ROBUST SUPPORT

INFRASTRUCTUREI

Security etc.