English in baking and finance

Challenges of Big Data for the Banking industry ingeneral and for Vietnamese Banks in particular...10 III.Application of Big Data for the Banking sector...11 IV.Practical contacts in Vie

MINISTRY OF EDUCATION AND

TRAININGNATIONAL ECONOMICS UNIVERSITY

THE GROUP EXERCISE

Subject name: English in Baking and Finance

INDEX 2

INTRODUCTION 3

CONTENT I Introduce about Big Data and Big Data Analytics 4

1 Big Data 4

2 Big Data Analytics 7

II The role and impact of Big Data on Banking sector 8

II.1 The role of Big Data in Banks 8

II.2 The impacts of Big Data on Banking sector 9

a Opportunities of Big Data for Banks 9

b Challenges of Big Data for the Banking industry ingeneral and for Vietnamese Banks in particular 10

III.Application of Big Data for the Banking sector 11

IV.Practical contacts in Vietnam 16

CONCLUSION 21

REFERENCE 22

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Nowadays, big data affects almost every field and industry The banking

industry, too, suffered much from big data Big Data is playing a big role in

the banking sector with specific applications such as: analysis, customer

satisfaction and customer classification; analysis of detection and warning,

prevent risky and fake acts; optimize data processing activities during the

operation of analysis and support decision making

Big Data will bring many benefits to banks in business such as: Reduce costs;increase product development and optimization time; at the same time supportthe management, bank officials to make more appropriate and reasonable

decisions; saving customers' information processing time and preventing

fraud risks

However, when exploiting Big Data, banks also face many financial

challenges; policies and regulations of the law on data access and use; data

mining and management level; IT infrastructure Although Big Data is

having a profound impact on the business and marketing strategy of the

Banking and Finance Industry, but without proper technology, knowledge andpractical application of effective As a result, it will be difficult for banks to

maximize the potential benefits of Big Data

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I Introduce about Big Data and Big Data Analytics

1 Introduce about Big Data

a The Concept

According to Gartner:

Big Data are information resources with properties such as high-volume,

high-velocity and high-variety, requiring innovative and cost-effective forms

of information processing to enhance understanding and make a decision

Big data is a term applied to data sets whose size or type is beyond the ability

of traditional relational databases to capture, manage and process the data

with low latency Big data has one or more of the following characteristics:

high volume, high velocity or high variety Artificial intelligence (AI),

mobile, social and the Internet of Things (IoT) are driving data complexity

through new forms and sources of data

For example: Big Data comes from sensors, devices, video, networks, log

files, transactional applications, web, and social media much of it generated inreal time and at a very large scale

Big Data is a broad term for processing very large and complex data sets that

traditional data processing applications cannot handle Includes challenges

such as analysis, collection, data monitoring, search, sharing, storage,

transmission, visualization, querying and privacy

The term Big Data is often simply understood as used for predictive analysis

or as some other obvious advanced method for extracting values from data

with little reference to the size of the data set Accuracy in Big Data can lead

to better right decisions, and better decisions can lead to better performance

such as cost and risk reduction

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b Five Vs of Big Data

 Volume

Talk about the amount of data created and stored The size of the data will be assessed as valuable and potential, and to consider whether it can be

considered as big data

For example: Facebook receives nearly 350 million images, more than 4.5

billion likes, and nearly 10 billion messages and comments every day For

that reason, traditional types of data storage and analysis are in no way

possible But with the technology we are talking about here, it can easily

process and store all the information on separate small branch systems

 Variety

This concept is about the data type and the nature of the data This helps thosewho analyze it effectively use detailed information about the results They arecomposed of text, images, audio and video; plus it completes the missing part through data aggregation algorithms

 Velocity

In this day and age, the speed with which data is created and processed to

meet the needs and challenges lies in the path of growth and development

Big data is usually available in real time

For example:

People can chat with each other on Facebook with fast speed in today's

network environment Big Data allows us to analyze the parameters of a

generated data without saving them to the database

technology can guarantee the reduction of unfortunate deviations that may

occur

 Value

The data quality of the data collected can vary greatly, which will greatly

affect the exact analysis We can see this is the nature as well as the concept

that businesses or researchers who want to use and exploit Big Data must holdand understand it first

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c The growth of Big Data:

Worldwide Big Data market revenues for software and services are

projected to increase from $42B in 2018 to $103B in 2027, attaining a

Compound Annual Growth Rate (CAGR) of 10.48% As part of this

forecast, Wikibon estimates the worldwide Big Data market is growing at an

11.4% CAGR between 2017 and 2027, growing from $35B to $103B

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( Source: Wikibon and reported by Statista.)

unknown correlations, market trends and customer preferences that can help

organizations make informed business decisions

b The importance of big data analytics

Driven by specialized analytics systems and software, as well as

high-powered computing systems, big data analytics offers various business

benefits, including:

- New revenue opportunities

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- More effective marketing

- Better customer service

- Improved operational efficiency

- Competitive advantages over rivals

Big data analytics applications enable big data analysts, data scientists,

predictive modelers, statisticians and other analytics professionals to analyze growing volumes of structured transaction data, plus other forms of data that

are often left untapped by conventional BI and analytics programs This

encompasses a mix of semi-structured and unstructured data

For example: internet clickstream data, web server logs, social media

content, text from customer emails and survey responses, mobile phone

records, and machine data captured by sensors connected to the internet of

things (IoT)

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II The role and impact of Big Data on Banking sector.

1 The roles of Big Data in the Banking sector:

Diversity with data from various sources specific to banking operations creates

a large data source from structured data, such as transaction histories,

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customer records to unstructured data, such as customer activities on the web, mobile applications, etc Using big data to exploit this data will bring

competitive advantages and great effects in the field of banking

Specifically, Big data is playing a big role in the banking sector with specific

applications such as: analysis, satisfaction classification and customer

behavior; analysis of detection and warning, prevent risky and fake acts;

Optimize data processing operation during analysis operation

Big data is very important to the bank’s activities, and analyzing big data also helps the bank to make appropriate business decisions

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2 The impacts of Big Data on the Banking sector:

a Opportunities for Banking:

 Fraud Detection: It helps Banks to detect, prevent and eliminate

internal and external fraud as well as reduce the associated costs

 Risk management: Banks analyse transaction data to determine risk

and exposures based on simulated market behavior, scoring customer and potential clients

 Contacts Center Efficiency Optimization: It helps Banks to resolve

problems of customers quickly by allowing Banks to anticipate

customers need ahead of time

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 Customer Segmentation For optimize Offers: It provides a way to

understand customers’ needs at a granular level so that Banks can

deliver targeted offers more effectively

 Customer Churn Analysis: It helps Banks to retain their customers by

analyzing their behavior and identifying patterns that lead to a

customer abandonment

 Customer Experience Analytics: It can provide better insight and

understanding, allowing Banks to match offers to customers’s needs

b Challenges for Banking:

 Difficult to fully harness customer profile data:

Banking services data is highly diverse, stored in different departments It is

difficult to profile a customer based on customer investment behavior as his

accounts, loans, insurances, etc may be spread over various branches and

departments of the bank Big data needs to collate all such data first, in order

to provide comprehensive intelligence

 Legacy infrastructure needs to be upgraded before integrating big

data capabilities:

Most banking solutions are not equipped to handle constant influx of data,

which is a pre-requisite for big data, even if they have moved

to cloud solutions Integrating big data requires a complete revamp of most of the existing bank solutions in partnership with a big data consulting company This is not easy to implement, as the system needs to be constantly up even

when the changes are being deployed

 Staff has limited access to modern digital technologies:

Not only are there limitations in the old machinery system, human resources

with modern digital technology in banks are still weak and thin, and there is a shortage of personnel capable of capturing and deploying digital technologies

in the world

 Customer concerns about privacy and security:

Although the data logged by big data systems is anonymous at the high level,

if the bank wishes, they can track behavior patterns of each individual

customer It is advantageous in detecting illegal activities, but is a

serious security threat to the customer if it falls into wrong hands

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c Challenges of Big Data for Banking in Vietnam:

- The complex traditional core banking system is the biggest barrier to the success of digital banking Without depth changes, banks may lag

behind in the race to provide digital experiences to customers Outdatedinformation technology (IT) systems with inflexible structure and

monolithic operations are also hindering banks from developing to

digital banking while system changes are complex and expensive abouttime and money

- Strategic investment budgets for new technologies are limited as

Vietnamese banks currently focus on short-term business At the same time, due to the lack of a digital strategy and vision, limited knowledge

of digitization and the potential of digitization are also restricting banksfrom investing properly in modernizing the system

- Not only are there limitations in the old machinery system, the

human resources with modern digital technology in Vietnamese banks are still weak and thin, there is a shortage of personnel capable of

capturing and deploying the technologies modern in the world

- With the current pace of digital technology development, security is also a problem for global banks, including Vietnam, to pay great

attention to when the qualifications of cyber attackers and crime is also much higher, along with the high level of globalization brought about

by the Industrial Revolution 4.0, the attack on Vietnamese banks is not only encapsulated within the country but in any country In any case,

criminals can attack Vietnamese banks

- The competition comes from technology finance companies, when

Apple Pay and Samsung Pay are in turn, and are direct competitors to

the payment products of traditional banks

III Application of Big Data for the Banking sector

1 Identifying and Eliminating Fraud

- Combining and analyzing large volumes of data like transactions,

geo-location, merchant information and more helps financial services companies

identify anomalies and behavior patterns that signal potential fraud With

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these insights, you can dramatically reduce the risk of fraud and tighten

security

For example, Danske Bank is fighting fraud with deep learning and AI

techniques The bank struggled with low fraud detection rates (40 percent)and has over 1200 false positives per day After the implantation of a modernenterprise analytics solution, the bank realized a 60 percent reduction in falsepositives; increasing true positives by 50 percent

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2 Improved cybersecurity and risk management

- Operational risk mitigation is near the top of every bank’s operating agenda With your employees and your business operating at a greater pace and level

of complexity than ever, it’s critical to monitor and report on employee

behaviors, key operational process performance and other KPIs to ensure you mitigate risk

Example, UOB bank from Singapore is an example of a brand that uses big

data to drive risk management Being a financial institution, there is huge

potential for incurring losses if risk management is not well thought of UOB bank recently tested a risk management system that is based on big data The big data risk management system enables the bank to reduce the calculation

time of the value at risk Initially, it took about 18 hours, but with the risk

management system that uses big data, it only takes a few minutes Through

this initiative, the bank will possibly be able to carry out real-time risk

analysis in the near future (Andreas, 2014)

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3 Preventing Overdraft Fees and Other

- A lot of times, bank customers feel the need to create an automated savings plan, but are afraid to do so if an unexpected charge comes up and causes an

overdraft With smart AI, banks can mitigate such a circumstance With steps like forward cash flow predictions, aggregated account data and data-driven

intelligent awareness, banks can hold transfers to the automated savings

account until there’s more money in the account, alert the customer of a

possible overdraft and suggest a top up, and take other steps to prevent

penalties

Example, Metro Bank is already doing that with Insights, an in-app money

management tool that gives customers complete control of their finances It

alerts customers when there’s not enough money to cover a likely spend,

recommends a top up before an automated payment is due, flags if a customerhas accidentally been charged twice and alerts the customer when there has

been any kind of unusual activity

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