(Tiểu luận) group 1 the application of big data in banking sector focus on risk managment

NATIONAL ECONOMICS UNIVERSITY -*** - FINANCIAL RISK MANAGEMENT Topic: The application of big data in banking sector: focus on risk managment Group 1: Nguyễn Hoàng Anh Vũ Thị Lan Anh Mai Linh Chi Nguyễn Phương Hoa Nguyễn Xuân Kiên Lê Thùy Linh Nguyễn Thị Tình Table of Contents I MARKET OVERVIEW Market size Future of Big Data II WHAT IS BIG DATA? TYPE OF BIG DATA Definition Types of Big Data Identify data sources of Big data: Data warehouse & Data lake: Interbank shared data: Outsourced data: 10 III CHARACTERISTICS OF BIG DATA 11 What is VOLUME? 11 What is VELOCITY? 12 What is VERACITY? 12 What is VARIETY? 13 What is the VALUE? 14 IV APPLICATION OF SOME INDUSTRY .14 Retail Industry .14 E-commerce 18 Digital Marketing 20 V APPLICATION OF BIG DATA IN THE BANKING SECTOR 22 Analyzing customer spending habits 22 2.Customer segmentation and audit records 23 Improve service quality by establishing a system to collect and analyze customer feedback 23 Personalized marketing 23 Detect and prevent fraud and illegal behavior .24 Risk control, legal compliance, and financial reporting transparency 24 VI CASE STUDY BANKS USE BIG DATA .25 TP BANK 25 MCREDIT 27 Case study of how banks and other financial institutions use big data in risk management: 32 VII CHALLENGES OF BIG DATA IN THE BANKING SECTOR 36 1 Financial resources .36 Human Resources 37 Technology 37 Data 38 VIII LEGAL FRAMEWORK 39 IX REFERENCES 40 I MARKET OVERVIEW Market size ● With the rise of the internet, smartphones, and other apps, digital data has escalated The tremendous potential of using this knowledge, also known as Big Data, is recognized by private businesses and governments alike, to generate real value for consumers and increase productivity over time Big data might take over companies and economies, but data science is the real game changer ● The Big Data as a Service Market size is expected to grow from USD 25.44 billion in 2023 to USD 86.76 billion by 2028, at a CAGR of 27.81% during the forecast period (2023-2028) ● As businesses increasingly adopt data-driven marketing strategies, mobile and hybrid working environments, and worldwide supply networks, cloud computing is becoming more ubiquitous In Banking ● Big data analysis in the Banking market is estimated at 5.83 million USD this year The market is expected to reach USD 19.72 million, growing at a CAGR of 23.11% during the forecast period Big data analytics can help banks understand customer behavior based on inputs received from various insights, including investment patterns, shopping trends, investment motivations, and business backgrounds personal or financial background With the improvement in big data analytics, banks can analyze market trends and make decisions regarding reducing or increasing interest rates for individuals in different regions With the help of big data analytics, major services are actively using it to store data, derive business insights, and improve scalability as the number of records increases Future of Big Data With the data and predictions about Big Data above, we can predict what Big Data will be like in the future Continued growth and expansion: The Big Data market has been experiencing significant growth over the past decade, and it is likely to continue expanding in the future Organizations across various industries are recognizing the value of using data analytics and insights to drive better decision-making and operational efficiencies Increasing adoption of advanced analytics: As Big Data technologies become more mature, there will likely be an increasing trend toward adopting advanced analytics capabilities This includes machine learning, artificial intelligence, and predictive analytics that can provide more accurate and actionable insights from vast amounts of data An example of Bank used Big data A number of typical businesses have applied Big Data in the banking sector not only abroad but also in Vietnam Here are some examples: JPMorgan Chase: The largest bank in the United States has used Big Data to analyze customer information and provide expectations about consumer behavior, thereby enhancing risk management and improving customer experience Wells Fargo: This bank has also developed Big Data to identify customer spending patterns, thereby creating products based on actual user needs Techcombank: In Vietnam, Techcombank is one of the banks that has successfully applied Big Data They use data from different sources to analyze customer actions, creating products and services tailored to user needs VietcomBank: This bank has also applied Big Data to business activities Data from sources such as transactions, signal records, and client data are analyzed to optimize risk management and provide key financial solutions to clients II WHAT IS BIG DATA? TYPE OF BIG DATA Definition Big data refers to extremely large and complex data sets that cannot be easily managed, processed or analyzed with traditional data processing applications Types of Big Data a Structured Data (with example) Structured data refers to organized and formatted data that is easily understandable by computers It typically follows a specific schema or data model, making it easier to search or data model, making it easier to search, and process Examples of structured data formats include relational databases, XML, and JSON b Unstructured Data (with example) Unstructured data refers to any type of data that doesn't follow a specific data model or is not organized in a predefined manner It is typically in the form of free text, audio recordings, images, videos, social media posts, emails, and other types of unorganized data For example, a collection of customer reviews for a product or service would be unstructured data because each review may be written in a different format and may contain different types of information c Semi-structured data (with example) Semi-structured data is a type of data that does not conform to a strict schema or data model but still has some organizational structure It contains tags, markers, or delimiters that separate different elements of the data Document continues below Discover more from: Risk Management Đại học Kinh tế Quố… 63 documents Go to course 37 19 10 English version 36 2014 TT-NHNN… Risk Management 100% (1) 76374 - 04 2012 Paolinietal Petra Risk… Risk Management None The application of E KYC in banking sector Risk Management None Exercises c3 - Chap 3 Risk Management None Derivatives Swaps Risk Management None Eng CEAC - Nexif 13 Draft 2006 - to chi… Risk A few examples of semi-structured data sources are emails, XMLManagement and other markup languages, binary executables, TCP/IP packets, zipped files, data integrated from different sources, and web pages Identify data sources of Big data: - Direct data transmission: data from the Internet of Things (IoT) and connected devices are transmitted into information technology systems from devices such as smartphones and smart cars - Administrative data such as electronic medical records, insurance records, and banking records; … - Data from commercial activities such as credit transactions, and online transactions on mobile devices - Data from sensor devices such as satellite images, road sensors, and climate - Data from tracking devices such as cell phones and GPS - Behavioral data such as online searches for products and services - Published available data: is information and data that is widely and publicly available such as official websites of the Governments of countries - Other sources: some other data sources come from customers, suppliers, or cloud data Data warehouse & Data lake: Some big data can be stored on-premise in traditional data warehouses but there are also flexible, low-cost options for storing and processing big data through cloud solutions, data lakes, data pipelines, and Hadoop The solution of building a data warehouse has helped business organizations integrate data from many different systems in departments and divisions The design and implementation of a data warehouse simplifies data access and helps organizations get the answers they need in their business However, the volume of data increases every day, leading to increasingly greater challenges when data warehouses face the problem of responding and providing in-depth results from the data collected Therefore, the data lake solution was born to overcome the limitations that data warehouses cannot None Differences between data warehouse and data lake: Criteria Data lake Data warehouse Type Data All data is retained Includes data extracted regardless of source and from transactional original structure Data is systems Data is cleaned kept in raw form, and transformed converted only when ready for use History Big data technology used Unlike big data, the in data lakes is relatively concept of data new warehouses has been in use for decades Collect data All types of data and Structured data and structured, semi- arranged them in schemas structured and as defined to build a data unstructured in their warehouse original form from source systems Time Data lakes can retain all During data warehouse data This includes not development, significant only data that is currently time is spent analyzing in use but also data that different data sources may be used in the future Additionally, data is retained at all times so that it can be turned back in time and performed analysis User Data lakes are ideal for Data warehouses are ideal users who want deep for users because they are analysis like data well structured, easy to scientists who need use and understand advanced analytics tools with capabilities like predictive modeling and statistical analysis Cost Storage costs are cheaper Relatively more expensive than data warehouses Mission Contains all data and data Provides insights on types, it allows users to predefined questions for access pre-transformed, predefined data types cleaned and structured data Processing Time Fast processing time Data Processing time is slower lakes empower users to The data warehouse access data before it is provides insights on transformed, cleaned, and predefined questions for structured Therefore, it identified data types allows users to get their Therefore, any changes to results faster than the data warehouse traditional data require additional time warehouses Benefit Integrate different types of Provide reports and key data to ask completely performance indicators new questions Limit Data is kept in its raw form, transformed only when it is ready for use No possibility of change