English in baking and finance

MINISTRY OF EDUCATION AND TRAINING NATIONAL ECONOMICS UNIVERSITY THE GROUP EXERCISE Subject name English in Baking and Finance The members 1 Quách Thị Ngọc Ánh 2 Lê Minh Anh 3 Nguyễn Công Trình 4 Phan[.]

MINISTRY OF EDUCATION AND

TRAININGNATIONAL ECONOMICS UNIVERSITY

THE GROUP EXERCISESubject 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

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

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

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

 Variability

Because of the variety of data types, the inconsistency of a data set can hinderprocesses to process and manage it Therefore, the accuracy of this

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

( Source:  Wikibon and reported by Statista.)

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

- 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,

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

 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

 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

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

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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4 Improving Customer Experience

- With so many financial institutions in the market, it gets tough for the

customer to decide which bank to transact with Customer experience, in this case, becomes a deciding factor Big data analysis presents with the

customised analysis for each customer, thus improving their services and offerings

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5 Personalised Marketing

- Personalized marketing is nothing but the next step of highly successful segment-based marketing where we divide the customers into a different segment based on some parameters and then follow with them accordingly to convert to sales

- Personalized Segmentation using Big Data In personalized marketing, we target individual customer based on their buying habits Industries can take help of the data from e-commerce profiles like what they are buying, what they are browsing etc to get the data of individual customers These data will