1. HOW DATA ANALYTICS IS
DRIVING THE FUTURE OF
BANKING
EMEKA OKOYE.
SEMANTIC ARCHITECT.

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ABOUT THE SPEAKER
 Solves Real-World problems with Semantic Web
Technologies at Cymantiks Limited, Enugu as a Semantic
Architect
 Has 25 years of Software Engineering experience with 15
years in Data and 10 years in Semantic Web
 Worked with the best Data & Semantic Tech company in
the world, OpenLink Software, as Country Manager Africa
 Co-founded Nigeria’s earliest startup in 1997, ngex.com

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AGENDA
 What is Data Analytics
 Situation Analysis
 Why Banks need
Analytics
 Benefits & Examples
 Strategies

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WHAT IS DATA ANALYTICS?
 Is the pursuit of
extracting meaning
from raw data by
algorithms or software
 They (algorithms or
software) transform,
organize and model
the data to draw

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WHAT IS DATA ANALYTICS?
 Is a process of inspecting, cleansing,
transforming and modeling data with the goal of
discovering useful information, informing
conclusions and supporting decision-making

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SITUATION ANALYSIS
“If you don’t know where you are, you won’t know
where to go” ~ Anon.

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TYPICAL BANK
 Banking products are getting commoditized,
and the features of all banks’ products (current
account, savings account, fixed deposits,
personal loans, or credit cards) are very similar.
 How, then, can banks differentiate and grow
their business? No matter how much
strategizing happens at the top level, it is these
sales officers at the bottom of the pyramid who

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TYPICAL BANK
 Brute force and threats to bring in business may
bear results in the short run, but not in the long
run.
 It could also lead to malpractices by the account
officers due to the immense pressure being put
on them by their managers, and can also lead
to talent attrition.

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MOTIVATION
 Technology has changed how customers
interact across every industry so that all lifestyle
activities now leave a digital footprint.

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MESSY ENTERPRISE
 The modern enterprise exist within a world of information
abundance. Applications and business processes generate
data at an ever-accelerating rate and in increasingly
diverse formats.
 The driver for all organizations is the desire to overcome
inefficiency in dealing with information – especially when
legacy systems have been in place for years. Systems
upon systems are integrated, modified, added on to, and
adapted in order to develop new capabilities. Because
these systems have different developers, codebases, and
architectures, concepts are represented in different ways.

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MESSY ENTERPRISE
 Every organizations have their own silos of information:
operations, sales, marketing, personnel, legal, finance,
research, maintenance, CRM, document vaults etc.
 Over the years there have been various attempts to break
down these silos including the creation of the mother-of-all
databases that houses (or replicates) all information or the
replacement of disparate applications with their own
database with a mother-of-all application that eliminates
the separate databases.

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MESSY ENTERPRISE

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MESSY ENTERPRISE
 Organizations always struggled with where their
knowledge lies:
 Stays ‘resident’ with the worker, scientist or locked into
more traditional enterprise applications
 There for one job but not recognized as connected to
other jobs
 The querying of data still has limitations without
significant rewriting of new code

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MESSY ENTERPRISE
 Enterprises spend far too much time and resources in
integrating and on boarding the data before they can
realize any value out of it
 The result is a complex, brittle environment that requires
immense resources just to maintain – with no ability to
transform how the business interacts with customers.
Enterprises are reaching the tipping point in needing to
start fresh with new technology.

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DATA SILOS
 Silos of data need to be minimised wherever
possible because they cost money, reduce
quality of decisions and can ultimately slow the
organisation down.

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THREATS
 Doug Laney from Gartner summed it up
recently when he pointed out that through 2017,
90% of Big Data projects will not be
leverageable because they will continue to be in
silos of technology or location

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APPLICATION-CENTRICITY
 Typically embodies a narrow view of its purpose
 It stores the data it needs for a specialised
purpose in its own dedicated system without
regard to how other processes or functions may
need that data.

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APPLICATION-CENTRICITY
 Application centric approaches limit an
organisation’s ability to flexibly ask questions
across the larger enterprise.
 Organisations struggle to ask questions to go
across organisational boundaries.

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APPLICATION-CENTRICITY
 Limited to application views, whereby its data
models are based on rigid processes defined by
the application, and the context for analytics
originates from the applications.
 Decisions are coloured by the nature of the
application and the limited scope of the specific
data sources that enable specific processes

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SITUATION REALITIES OF BANKS
 Banks are almost singularly focused on
products and sales.
 Customer service and experience took a
backseat.

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WEAKNESSES
 Few banks have embraced the holistic
approach of BigTechs to leverage data for
keener customer insights.
 Instead, banks address customer pain points at
various touchpoints, which can create a
disjointed experience and a less-than-seamless
journey

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BANKING THREATS
 Digital, online and mobile technologies has
exposed banking customers to exceptional
services and seamless customer experiences
from providers in other industries.
 Big Techs (Google Amazon Facebook Alibaba)
are affecting the expectations of banking
customers as well.

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BANKING THREATS
 There are other ‘data players’ in the Open
Banking future that, like banks, are old
economy companies in many ways. These
include retailers, energy companies, telcos,
wealth managers, pension providers, insurance
companies.
 The key advantage of these organizations is
huge amounts of customer data that could be

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BANKING THREATS
 These Big tech firms have invested heavily in
engagement technology, having the ability to
handle data at scale and use it to generate new
services
 They’re not scared of deploying new services
and they see financial services as another way
to facilitate their business model.

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BANKING THREAT
 Have they recognized the opportunity?
 Do they have the ambition to do anything about
it?
 Do they have the ability to execute at pace?

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Big Data Analytics can become the main driver of
innovation in the banking industry

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WHY BANKS NEED ANALYTICS
 To customize services for:
 improving customer experience
 drive up-sales
 make customers feel valued

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WHY BANKS NEED ANALYTICS
 Increase the ability to address and monitor
regulatory compliance
 Increase transparency and understanding of
risk exposures to manage the business more
effectively
 Develop a risk-adjusted view of performance
 Manage fraud effectively

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WHY BANKS NEED ANALYTICS
 Measure customer and product profitability
 Identify “high-potential” prospects and
customers
 Improve the ability to target products and
services to prospects or customers
 Enhance specific elements of the offer—
product, pricing, channel

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BENEFITS
 A data driven business achieves sustainable
competitive advantage by leveraging insights
from data to deliver greater value to their
customers. This approach promotes fact-based
decision making over intuition and gut instincts.

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BENEFITS
 Peer deeper into the customer experience.
When you enable deep analytics in banking,
you can gain a multi-layered look at the
customer experience. You can drill down to
things like individual transaction histories,
providing eye-opening insights. You’ll be able to
see, from a data-enabled viewpoint, what the
customers see—good and bad—about your

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SOLUTION
 Today’s banks struggle with their data. They’ll
throw Excel sheet after Excel sheet at the
problem, attempting to report as much as they
can. But that approach is misguided.
 In order to ensure that you derive the greatest
possible benefit from analytics in banking, it’s
best to follow these steps

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SOLUTION
 Perform a metadata analysis of your existing
banking data, to make sure it’s tagged in a
useful way. Semantic representation.
 Incorporate automation into your data capture
as much as possible. This removes
spreadsheets, reporting layers, and pushes
newfound metatags into the underlying data
systems.

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SOLUTION
 A single source of truth for all the data across
an enterprise is the Holy Grail.
 This can catapult an organization to become
one that is truly data-driven.

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SOLUTION
 Business teams and executives have a holistic
view of all data with full confidence in its
integrity.
 They have the ability to dive down to the lowest
level of detail as needed, with instant response
times
 Data scientists could traverse these systems

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IMPROVING CUSTOMER
EXPERIENCE
 Improving the customer experience requires
truly understanding your customers and relating
to them in ways that they understand. This
includes taking a 360-degree view of your
banking customer and leveraging the gold mine
of data available to you today, including:
 Core customer information (including contact
and location data)

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IMPROVING CUSTOMER
EXPERIENCE
 Transaction information (including checking,
savings and credit card transactions; loan
draws and repayments; investment positions
and balances)
 Customer service data
 Social media information

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PRODUCT CROSS-SELLING
 Why not offer a better return on interest to
cautious investors to stimulate them to spend
more actively? Is it worth providing a short time
loan to an easy spender who already struggles
to repay a debt?
 Precise analysis of the customers’ financial
backgrounds ensures the bank is able to cross-
sell auxiliary products more efficiently and

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CUSTOMER CHURN ANALYSIS
 It help Banks to retain their customers by
analyzing their behavior and identifying patterns
that lead to a customer abandonment.

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SENTIMENT ANALYTICS
 This helps the Bank to analyse social media to
monitor user sentiment towards the brand,
services or products

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FRAUD MGT & PREVENTION
 Knowing the usual spending patterns of an
individual helps raise a red flag if something
outrageous happens.
 If a cautious investor who prefers to pay with his
card attempts to withdraw all the money from
his account via an ATM, this might mean the
card was stolen and used by fraudsters.

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CUSTOMER FEEDBACK
ANALYSIS
 The customer can leave feedback after dealing
with the customer service or through the
feedback form, but they are much more likely to
share their opinion through the social media.
 Big Data tools can sift through this public data
and gather all the mentions of the bank’s brand
to be able to respond rapidly and adequately.

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MEETING CUSTOMER DEMANDS
 Technology companies like Google and
Facebook are setting new customer
expectations. As a consequence, customers
want scores of new features from their banks as
well .
 Big Data is doing this in three ways: by helping
identify the services that customers want; by
helping identify the price points for new services

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CUSTOMER LIFETIME VALUE
 Big Data can surface campaign strategies to
acquire new customers, track customer
response across channels, and adjust channel
investments.
 Insights from Big Data can drive offers that
matter to individual customers rather than
generic approaches with non-optimal returns.
Successful on-boarding can be followed with

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RESTRICTING HIGH COST OF
OPERATION
 Channel ROI can be maximized by using Big
Data to identify locations where new physical
branches need to be established, scaled-down
or shut; data can flag services that can make
branches profitable; and it can establish cost
effective channels for customer outreach,
interaction and service.

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REGULATORY SCRUTINY
 Banks are not sure how regulatory scrutiny will
shape up in the future. But as banks are held
more accountable, their stores of Big Data will
strengthen their positions vis-à-vis compliance
by providing signals that aid early fraud
detection.

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KNOWLEDGE GRAPH
 Is a large network graph of
Entities, Semantic types,
Properties and Relationships
between Entities which are specific
to a Domain or Organization. It is
not limited to Abstract Concepts

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KNOWLEDGE GRAPH
 A Knowledge Graph (KG) is
simply a way of representing
Human-Knowledge to Machines.

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OTHERS
 Frequently used ATM operations
 Predicting when a customer will leave the
institution
 Recommendation engine about how much
money to add to ATMs on weekends and
holidays
 Internal recommendation engine for the

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OTHERS
 New business opportunities for non-customers
 Personalized financial products
 Optimization of the bank's processes and
resources

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We are drowning in information and starving for
knowledge ~ Rutherford D. Roger

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STRATEGY
 The Customer Is The Center of The Universe
 Support From Senior Management Is Critical
 Data Analytics Strategies Must Match The
Growth Strategy
 Functional Integration of Strategy Is Key

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STRATEGY
 Training Is Key
 Drive for Results, But Set Expectations at
Reasonable Levels
 Balance Analytics and The Voice-of-the-
Customer (VoC)
 Start Small

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STRATEGY

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CONCLUSION
 Doing the things the old way is too risky
nowadays. The companies must evolve and
grasp the new technologies if they want to
succeed.
 Adopting the Big Data analytics and imbuing it
into the existing banking sector workflows is
one of the key elements of surviving and
prevailing in the rapidly evolving business

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THANK YOU
TWITTER: @EMEKAOKOYE
EMAIL: EMEKA.OKOYE@GMAIL.COM