ananto whitepaper banking

8/12/2019 Ananto Whitepaper Banking

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Banking accomplishes BIG growth with

BIG DATA Analytics

February 2013


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Background

The McKinsey Global Institute estimates U.S. banks and capital market firms collectively

had more than 1 exabyte (or one quintillion bytes) of stored data in 2009. In the

subsequent years, this figure has exponentially grown 40% year on year. Innovative Data

Management technologies, Predictive and Actionable Analytics along with Information

Visualization are imperative to harness

this vast data and achieve significant

insights for business benefit. While the

known techniques are used to gleaninformation for improvements in

customer experience and retention, semi-

structured and unstructured data

processing enables far deeper insight of

not only what customers need and want

and how or why they buy, but also of what would be the likely behavior. Ananto considers

fact based informed judgment as imperative in making business decisions, making them

more robust and repeatable with perfect combination of business acumen and data

insights. At the point of interaction with consumers (bank branches in this case), the issue

is accessibility of information in terms of broader transaction, socio-economic, or behaviors

that would improve success rates of increased revenue and enhanced customer experience.

The basic propensity models providing old method of running up-sell and cross-sell

campaigns is giving way to sophisticated smart-sell approach that not only increases the

share of wallet but also helps to retain brand image of being a caring and trustworthy

brand.

The solution is to embrace dynamic decision ability with real-time actionable analytics,

which will not only improve and streamline the tactical process, but also achieve holistic

end-objective of an optimal customer experience.


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Big Datasunified data management system from multiple sources helps execute high-per-

formance computing multivariate analytics, enabling revenue growth, reduction of risk,

prevention of fraud and accomplish regulation. Inside those millions of transactions are the

keys to consumer preferences, red flag indicating fraud and the means to manage risks.

Big Data Analytics in Banking

A large global payments firm uses predictive analytics to

determine pricing strategy and decisions. The company

analyzes 20,000 transactions per second, across 17

dimensions, to understand products, clients, products by

client, and clients by product. Big Data provides great

opportunities to understand customers in more depth than was possible earlier.

Big Data in banking usually refers to the tremendous volume and variety of data, often

arriving at extreme velocities from a wide variety of sources; such as customers, partners,

regulators, systems, and digital world. From billions of daily transactions, real-time market

feeds, detailed customer service records, web click streams, location data, social- media

posts and tweets, Big Data increases and accelerates, often beyond a banks ability to

manage and derive value from it. Big Datas complexity lies inthe following:

a. How can we understand and use Big Data presented in an unstructured format; suchas text, speech, or video;

b. How can we capture the most important data as it happens, and deliver that to theright people in real-time;

c. How can we store the required data for analysis, given its size with existingcomputational capacity;

d. How can we keep the data private and secure.

Big Data comes with big

opportunity.No industry stands to gain more

from Big Data than banking.


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In banks, speed of delivering the action is critical the faster the bank can take action at

the point of interaction, the better the odds are of a positive outcome. Big Data analytics

can help improve segmentation, targeting, acquisition, and retention of customers

taking into account the pre-emptive risk and fraud detection.

A number of banks and financial institutions have been quick to recognize and adopt

this emerging technology. It is giving banks and financial institutions previously

untapped savings, margins and profit. Today, they are looking for optimal ways to gain

further insights, in shorter reporting windows through high-performance analytics.

Diagram 1: The Path from Data to Dollar


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Diagram 2: The Analytics Framework

It's all about getting to the relevant data quicker and revealing previously unseen

patterns, sentiments and relationships, and delivering that information in real time. We

discuss some analytical techniques:

Acquisition StrategiesAnanto offers high value Banking and Financial Analytics including Modeling and Data

Insight Services, which increases Customer Life-Time Value. The Predictive Analytics

techniques like Response Models and Campaign Analytics help in identifying target

customers, offer the right product features, use the right channels and deploy the right

messaging strategy that works best, all in all to improve ROIs.


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Right-sell StrategiesAdvance analytics helps organizations optimize their up- sell and cross- sell campaigns

to build smart strategies and institutionalize them as an organization- wide operations

model. The key processes are:

o To understand the customer needs and help banks assess a customer's propensityto buy a product, identify the right time to make the product offer, increasing

profitability and customer relations

o To identify and target prospective customers for cross- selling thereby increaseshare of wallet

o Develop and deploy result oriented cross- sell strategies along with responsemodeling for enhanced tailor-made campaigns

Application Fraud/Bad Debt ScorecardsThe robustness of modeling lies in the variety of data captured and skills involved in

building the data model. Now it is possible to understand the customer intentions right

at the application stage enabling transaction fraud analytics that identifies behavior

patterns to identify propensity of fraud with fair amount of accuracy

Collection and Recovery strategiesThe application of advanced predictive modeling services contributes:

To estimate the customer's propensity to repay, as well as put a number to the likelyamount that the customer will repay

To distinguish self-cures from potential long term delinquent accounts therebymaximizing collections from delinquent accounts and retaining customer relationship

To size the effort and proper effort-mix without compromising on customer experiencewhile optimizing service cost


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To build the optimummultichannel recovery strategies based on multi-variables likedelinquency score-card, the collection amount, and the effort sizing for collection.

Diagram 3: The Analytics Application

Customer Relationship Management multi-Touch- point Based NBACustomer relationships indisputably matter most to the organizations focusing on

maximizing revenues and profitability. By accessing multi touch-point interactions ofcustomers (viz. contact center, branch, social media), we offer results oriented analytics

solutions that identify the most profitable customers. This segment is then targeted with

attractive retention schemes keeping their need & expectation in view. The Banking and

Financial Analytics with Modeling Services help organizations deploy effective integrated

CRM strategies. This helps increase customer retention, enhance customer loyalty and

maximize the portfolio value combined with the optimum servicing budgets. In the area of

customer focus, Big Data can add value in Next Best Action (NBA), which is now a top of

mind for banking executives. NBA tries to balance customer needs with company priorities

to come up with the Next Best Action to sell or service the customer (whether its an offer

for a new product or service, or a response to a service issue), while furthering company

objectives, such as increased revenue, higher profits, or improved customer retention.


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Conventionally, banks have relied on basic segmentation and static offers delivered

through standard scripts from their service representatives. These have often focused

largely on retention and product promotion campaigns, and common static decision trees,

regardless of an individual customers past history with the bank. The new breed of NBA

solution takes into account all the known information about the customer, including

interactions or events, to arrive at optimal next best actions in the form of real-time

recommendations, or real-time automated actions considering the optimal channel for the

offer or interaction based on current channel, channel preferences, and geo-location; be it

the branch, web, contact center, ATM, or smart phone.

Diagram 4: Leaveraging advanced analytics to build profitable customer relationship

Driving BIG valueHyper-competition, loss of personal touch and use of on the fly channels result in low

switching cost to consumers, denting banks profitability. Creating and nurturing long-term

relationship with the customer is the key to maximize share of wallet. To do so, and to


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achieve faster time to market, it is crucial as well as essential for banks to anticipate

customer expectation well in advance.

Implementation of analytics is a cross-functional collaboration, whose potential towards

business gains is infinite. It needs careful integration of science and art, in choosing and

using right platforms, best breed of technology, most optimized statistical processes, multi-

source data capture, accurate data curation, and best- case predictive modeling. The most

important resources are the people who can see patterns in data. While the science,

software and computing power has increased tremendously, it still needs a fine eye to

detect possible correlations, unlikely patterns, outliers, and important insights. Its the

people and platform combination that provides one with a proactive actionable

intelligence.

Essence of building an implementation strategy lies in the Five C framework of Ananto;

i CuriosityAre we asking the right question? Leaders in near future will not have to worry about

providing the right answers but in asking the right question that can help them gather

correct insights

ii CaptureCapture data from multiple sources to create an encompassing image. The variety of

data flows and the velocity of data flows are important considerations in building a

right capture strategy. This function needs to be automated to manage velocity coming

in from multiple sources

iii CurateData, when compiled through multiple sources, needs careful treatment. It needs to be

put in similar structure, errors need to be corrected, and unstructured format

converted to a align with common structure


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iv CrunchAt this stage, the business issue defines shortlisting of data set that needs to be

processed. Sophisticated tools, various permutations by data scientists, model

generation and validation are some common tasks in this phase

v Create VisualizationAnanto believes that insights from analytics are worthless if leaders are unable to play

with it and take decisions. Visualization is a very important aspect of all the hard work

done in earlier phases. It not only displays information gathered, but also has the ability

to drill down and run what if scenarios. All this on the finger tip of these leaders, on adevice of their choice.

Ananto also recommends a crawl-walk-run strategy in implementation. It is not

important to start by investing in significant infrastructure and people. We have seen

relatively low hanging fruits in most business cases where some specific problem can be

solved for credible business gains. Since big bangapproach requires significant changes

within working business processes, it usually faces significant change management

challenges. By using crawl-walk-run strategy,initial investments are small. Once results

prove themselves there is a buy-in from critical business owners followed by a clear

impetus to invest more dollars.

Real-time Analytics can help leverage the data that resides in banksmultiple systems to

generate useful insights towards business progress with measurable ROI benefits such as:

i. Reduced data compression, maintenance & support cost with low time to run queries& response

ii. Increased data loading speed and processing power


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iii. Easy implementation of any data model from any data source with no/limitedalteration and no additional response time with data growth in terms of volume,

velocity, and variety

iv. Leverage existing hardware and storage spacev. Real time actionable analytics with improved decision ability on the fly, hence enhanced

customer experience

vi. More accurate future predictability of revenue, costs and profits, and scientificallydriven insightful data.

We conclude with the following essential

steps for the banks to enhance profitability:

Integrate additional datasources with existing customer data

Capitalize on real timeintelligence and customer intelligence with

key business driver analysis on continuous

basis and customized treatments and offers for each segment

Provide seamless service portability with optimum quality of experience

Ananto is your partner in this journey from data to dollars.

Comparing the capacity of computers to

the capacity of the human brain, Ive

often wondered, where does our success

come from? The answer is synthesis, the

ability to combine creativity

and calculation, art and science. . . .Garry Kasparov


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About Ananto

Ananto is Big Data Analytics Solutions and Services Company, supporting firms with Data

Insight, Predictive Intelligence, Technology Integration and Advisory and Professional

Solutions and Services.

The word Ananto means Infinite, depicting the very nature of endless possibilities of

business solutions that can be achieved using effective analytics.

Our mantra is to convert your data to dollars. Data itself has no value, unless it can be

harnessed smartly and transformed into deeper consumer insight and channelized

towards enhancement in productivity and efficiency. This deep insight into data can help

organizations to Re-focus, Re-align, Re-engineer and be Relevant and Resilient.

Started by customer experience industry veterans with global experience in technology,

process management, consulting and business solutions. In his last assignment, Aparup

Sengupta was the CEO and MD while Anil Modi was the CMO of a $1bn business services

organization.

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ananto whitepaper banking

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