Intelligent Multimedia Technologies for Financial Risk Management: Trends, tools and applications

Cover
Contents
Call for Authors – The IET International Book Series on Multimedia Information Processing and Security
About the editors
Foreword – Prof. Ramona Rupeika-Apoga
Foreword – Series editors Singh and Berretti
1 Applications of multimedia in diverse fields: an overview
Abstract
1.1 Introduction
1.1.1. Trends in intelligent multimedia data analytics
1.2 Tools used in IMDA
1.3 Application software used in IMDA
1.4 Write an essay on using IMDA in risk management and internal controls
1.5 The metaverse
1.6 Medical devices
1.7 Entertainment
1.8 Security
1.9 Health
1.10 Financial services
1.11 Insurance
1.12 People’s needs and retail shops
1.13 Banking services
1.13.1 Benefits of e-banking
1.13.2 Electronic banking protocols
1.13.3 Services
1.14 Machine learning
1.15 Deep learning
1.16 Natural language processing
1.17 Blockchain technology
1.18 Robotic automatic process
1.19 Distributed computing technology
1.20 Administrative consistence intricacies
1.21 Future technology in finance
1.22 Conclusion
References
2 Evolution of multimedia banking and technology acceptance theories
Abstract
2.1 Introduction
2.2 Evolution of multimedia in the banking sector
2.3 ATMs and telephones
2.3.1 Telecommunication – vitalization through ATM
2.4 PCs and online services
2.5 E-cash and interactive video
2.6 TAM
2.7 TRA
2.8 Conclusion
References
3 Banking, Fintech, BigTech: emerging challenges for multimedia adoption
Abstract
3.1 Introduction
3.2 Trends and patterns of BigTech entry into emerging markets and developing economies (EMDEs)
3.2.1 A case study of digital payment trends in India
3.3 Drivers of BigTech activity in EMDEs
3.4 Pros and cons of BigTech firms entering the financial services
3.4.1 Benefits to the financial services industry from BigTech activities
3.4.2 Risks associated with the BigTech firms to enter financial services
3.5 Technological growth: opportunities & risks for BigTech firms in EMDEs
3.5.1 It is the ‘DNA’ of big tech’s business strategy
3.5.2 Access to financial services and big data
3.5.3 Regulating the financial sector
3.5.4 Power in the market and rivalry
3.5.5 Coordination of policy and the need for education
3.6 Venture capital from EMDEs in facilitating BigTech firms
3.6.1 Meaning of venture capital
3.6.2 Venture capitalists’ impact on BigTech management
3.6.3 The BigTech firm and the dependency perspective
3.7 Impact of COVID-19 on BigTech firms’ activities
References
4 Multimedia technologies in the financial market
Abstract
4.1 Introduction
4.2 Cloud-based software-as-a-service (SaaS)
4.3 Self-service multimedia banking kiosks
4.3.1 SSTs in banking sector: global and local contexts
4.4 Image-enabled ATMs
4.5 Digital account opening
4.5.1 What does digital financial inclusion look like?
4.6 Interactive banking portals
4.7 Person-to-person (P2P) payments
4.7.1 Nonbank-centric P2P payment methods
4.7.2 Bank-centric P2P payment methods
4.8 Chatbots/virtual personal banker
4.8.1 Banking chatbot business
4.9 Video banking services
4.10 Mobile and TV-based banking
4.11 Safe deposit boxes with iris-scanning biometrics
4.11.1 Physiological biometrics
4.12 Conclusion
References
5 Data analytics in finance
Abstract
5.1 Forecasting economic variables through linear and nonlinear time series analysis
5.1.1 Autoregressive dependent framework
5.1.2 Models based on moving averages
5.1.3 Artificial neural networks in finance
5.2 Big data analytics tools for financial forecasting
5.2.1 How could back groups conquer the difficulties of working with enormous amounts of information?
5.2.2 How does robotization help enormous information examination?
5.2.3 How can arising advances enable huge information?
5.2.4 How can huge information change finance?
5.2.5 What’s next for huge information in finance?
5.2.6 About cash analytics
5.3 Financial time series analysis
5.4 Web analytics, visual analytics, service analytics, multimedia analytics, textual data analytics
5.4.1 Interactive media analysis
5.4.2 Visual analytics
5.4.3 Multimedia analysis
5.4.4 Interactive media analysis
5.4.5 Visual analytics
5.5 Predictive, prescriptive, descriptive analytics
5.5.1 What is descriptive analytics?
5.5.2 What does the spellbinding investigation show?
5.5.3 Instances of expressive examination
5.5.4 What is diagnostic analytics?
5.5.5 What does symptomatic examination show?
5.5.6 Instances of demonstrative examination
5.5.7 What is predictive analytics?
5.5.8 What does the prescient investigation show?
5.5.9 Instances of prescient investigation
5.5.10 What is prescriptive analytics?
5.5.11 What does the prescriptive investigation show?
5.6 Expert methods for financial regression and classification problems
5.7 Factor models for big data in options stochastic modelling and pricing
5.7.1 Bachelier design
5.7.2 Scholes–Merton (BS) model in the dark
5.7.3 Demand model
5.8 Financial mathematical and statistical tools
5.8.1 Insertion and extrapolation
5.8.2 Decision theory
5.8.3 Decision-making under states of assurance
5.8.4 Decision-making under states of vulnerability
5.8.5 Correlation analysis
5.8.6 Cost volume benefit (CVP) or break-even investigation
5.8.7 Tests in ventures
5.8.8 Serial relationship tests
5.8.9 Run tests
5.8.10 Simulation
5.8.11 Decision tree analysis
5.8.12 Sampling technique
5.8.13 Standard deviation
5.8.14 SAP R/3 vs. ERP
5.8.15 Modules for SAP
5.9 Conclusion
References
6 Machine learning and deep learning for financial data analysis
Abstract
6.1 Machine learning and deep learning for financial data analysis
6.2 Graph neural networks for investor networks analysis
6.3 Using ML to predict the defaults of credit card clients
6.4 Application of deep learning methods for econometrics
6.5 AI and multimedia application in finance
6.5.1 AI & ML techniques for simulation of markets, economics, and other financial systems
6.5.2 Infrastructure to support AI & ML research in finance
6.5.3 Chatbots & robot advisors for payment and innovation
6.5.4 AI/ML-based evaluating models
6.5.5 Validation and calibration of multi-agent systems in finance
6.6 Advance ML for financial stability
6.6.1 AI-based blockchain in financial networks
6.6.2 Business challenge: deep learning seen as too resource-intensive
6.7 Credit scoring models using ML algorithms
6.8 Python to implement methods from stochastic
6.9 Conclusion
References
7 Self-supervised, unsupervised & semi-supervised learning for multimedia banking and financial services
Abstract
7.1 Supervised learning for money-laundering prevention, document analysis and underwriting loans, trade settlements, high-frequ
7.1.1 What exactly do detecting fraud paradigms perform?
7.1.2 Customer experience and segmentation
7.1.3 Underwriting and credit scoring
7.1.4 Difficulties to industry adoption
7.2 Robo-advisors is a tool of supervised learning for optimizing portfolios
7.2.1 What is a Robo-advisor?
7.2.2 Understanding Robo-advisors
7.2.3 Portfolio rebalancing
7.3 Fundamental advantages of Robo-advisors
7.4 Hiring a Robo-advisor
7.5 Robo-advisors and regulation
7.5.1 How Robo-advisors make money
7.5.2 The best-in-class Robo-advisors
7.6 Component of ML
7.6.1 What is PCA?
7.6.2 Calculation of PCA
7.6.3 Benefits and limitations of PCA
7.6.4 Assumptions of PCA
7.6.5 Practical working in PCA
7.6.6 Production programming for cutting-edge information science
7.7 Financial asset clustering using cluster analysis
7.8 Latent variable modeling for financial volatility
7.9 Association rule learning for financial revenue analysis
7.10 Semi-regulated text mining for environmental, social, and governance
7.11 Performance of companies
7.12 Conclusion
References
8 Natural language processing and multimedia applications in finance
Abstract
8.1 Financial technology and natural language processing
8.1.1 NLP-based finance
8.2 NLP-based investment management
8.2.1 Instances of some key NLP applications in asset management
8.3 NLP-based know your customer approach
8.4 Applications or systems for FinTech with NLP methods
8.5 Crowdfunding analysis with text data
8.6 Text-oriented customer preference analysis
8.7 Insurance application with textual information
8.8 Telematics: motor & health insurance
8.8.1 Telematics and automobile insurance
8.8.2 Benefits of telematics-based auto insurance
8.9 Text-based market provisioning
8.10 Conclusion
References
9 Digital disruption and multimedia technological innovations in the banking world
Abstract
9.1 Background of multimedia banking
9.2 Phases of multimedia banking
9.3 Challenges and acceptance of multimedia banking
9.3.1 Safety and security
9.3.2 System
9.3.3 Significant charges
9.3.4 Internet connection
9.3.5 Customer awareness
9.3.6 Cash-dominated rural society
9.4 Acceptance for multimedia banking
9.4.1 Convenience
9.4.2 Confidentiality
9.4.3 Communication with customer
9.4.4 Personalization
9.4.5 Add-on-services
9.4.6 Accessibility
9.4.7 FinTech
9.5 Future of multimedia banking
9.5.1 Neobanks
9.5.2 Physical decline
9.5.3 Thinner wallets
9.5.4 Cardless payments
9.5.5 Competitions with non-banks
9.5.6 Micro-personalization
9.5.7 Interoperability
9.6 Multimedia banking making a prolific growth
9.7 Reasons for rapid growth in multimedia banking
9.7.1 Adoption of digital banking by SMEs
9.7.2 Neobanks boosting the growth of India’s multimedia banking
9.7.3 Mushrooming mobile banking sector giving a boost to multimedia banking
9.7.4 Deployment types – India digital banking platform market
9.7.5 Regional insights of multimedia banking in India
9.7.6 The pandemic impacted multimedia banking
9.8 Customer perspective on multimedia banking in India
9.8.1 Demography of customers
9.8.2 Personal banking experience
9.8.3 Technology experience
9.8.4 Psychology and culture
9.8.5 Security challenges and trust
9.9 An approach to build customer relationship management (CRM) through multimedia banking
9.10 Real multimedia banking fraud in India
9.11 SWOT analysis of multimedia banking
9.12 Conclusion
References
10 Fraud and cyber operation countermeasures for multimedia in financial services
Abstract
10.1 Introduction of cyber fraud
10.2 Fraud and cyber operation countermeasures for multimedia in financial services
10.2.1 The top cyber threats to financial services
10.2.2 Tax evasion and tax fraud
10.2.3 Retail cybersecurity: challenges and threats
10.2.4 Security-operations center and network-operations center, which enable monitoring
10.2.5 Cyber security costs, cyber breaches; confidentiality, integrity, systems availability
10.2.6 Customer identification and authentication
10.3 Cyber security tools and technologies
10.3.1 Cybersecurity monitoring tools
10.3.2 SolarWinds security event manager
10.3.3 Heimdal
security
10.3.4 Packet sniffer software
10.4 Tools for cyber fraud
10.4.1 Safe Back
10.4.2 The Dark Web
10.4.3 Telegram
10.4.4 PII
10.4.5 Your Internet browsing “fingerprints”
10.4.6 Burner phones
10.4.7 Spoofing tools
10.4.8 SOCKS5 proxies
10.4.9 Fake driver’s licenses and documents
10.4.10 Remote desktop protocols (RDPs)
10.5 Tools for financial crime (anti-money laundering tools)
10.5.1 SEON – Block bad users and stop fraud
10.5.2 Active – smarter digital decisioning
10.5.3 AML check – smarter digital decisioning
10.5.4 Dow Jones – risk and compliance
10.5.5 Feedzai – fight financial crime with AI
10.5.6 HM treasury – official UK and EU sanctions lists
10.6 Risk severity matrix
10.7 Benefits of risk severity matrix
10.7.1 Cybersecurity as a pressing concern for financial organizations
References
11 Blockchain technology for the financial markets
Abstract
11.1 Key features and main applications of blockchain technology in the financial world
11.1.1 What is the process for a transaction to be added to the blockchain?
11.1.2 Types of blockchain
11.1.3 Features of blockchain technology
11.1.4 Blockchain technology’s power and its revolutionary applications in the financial sector
11.2 Modern banking ledger with blockchain technology
11.2.1 The blockchain’s itemised components are immutable
11.2.2 The blockchain’s data is transparent
11.3 Blockchain and banking business models
11.3.1 The top 5 blockchain applications in banking
11.3.2 Benefits of blockchain for banking
11.3.3 Five blockchain application examples in banking
11.4 Inherent drawbacks of digital currencies such as bitcoin
11.4.1 Drawback 1: scalability
11.4.2 Drawback 2: issues with cybersecurity
11.4.3 Drawback 3: price fluctuation and a lack of inherent value
11.4.4 Drawback 4: regulations and policies
11.5 Potential drawbacks to using cryptocurrencies and DLTs
11.5.1 Bitcoins are not accepted across the board
11.5.2 Wallets can be misplaced
11.5.3 The value of bitcoin fluctuates
11.5.4 There is no buyer protection
11.5.5 Technical flaws that are not known
11.5.6 Deflation is built-in
11.5.7 There is no physical form
11.5.8 There is no guarantee of valuation
11.6 Fraud detection and claims management using blockchain management
11.6.1 Three features of blockchain which prevents frauds
11.6.2 What types of frauds are detected?
11.6.3 Identity fraud cases
11.7 Cryptocurrency in the financial markets
11.7.1 Currency
11.8 Regulation of blockchain technology
11.8.1 National efforts: applications developed and piloted
11.9 Conclusion
References
12 Automation to handle customer complaints: a grievance handling system
Abstract
12.1 Introduction
12.1.1 E-complaint
12.1.2 Service-oriented architecture
12.1.3 Examination of the system
12.1.4 System architecture
12.1.5 SQL server database layer
12.2 Literature review
12.3 Grievance handling through integrated grievance management system (IGMS)
12.3.1 As-is complaint handling process
12.3.2 The disadvantage of the as-is module
12.4 Measuring the efficiency and effectiveness of IGMS
12.5 Transforming customer experiences and leveraging AI solutions
12.5.1 Compensation diagnosis
12.6 Achieving customer service excellence in claims management through technology intermediation
12.7 Customer protection – building a robust complaint management system
12.7.1 Education regarding workplace safety and knowledge of the value of education
12.8 Conclusion
References
13 Robotic process automation applications area in the financial sector
Abstract
13.1 Introduction contact centre optimization
13.2 Trade finance operations
13.3 Literature review on customer on-boarding
13.4 Anti-money laundering (AML)
13.5 Bank guarantee closures
13.6 Bank reconciliation process
13.7 Loan application process
13.7.1 RPA abilities
13.8 Automated report generation
13.9 Account closure processing
13.9.1 RPA programming
13.10 Credit card application processing
13.11 Conclusion
References
14 Multimedia sustained benefits for financial services
Abstract
14.1 Introduction
14.1.1 Concept of MMT
14.2 Billings and account receivables
14.3 Account payable
14.4 Collections
14.5 Cash flow management
14.6 Tax preparation
14.7 Cash disturbance
14.8 Budgeting process
14.9 Financial analysis and reporting
14.10 Payroll administration
14.11 Compliance
14.12 Conclusion
References
15 Extensive use of multimedia technologies: real-world case studies of multimedia banking
Abstract
15.1 Introduction
15.2 Multimedia technologies in the banking sector
15.2.1 Social media in banking
15.3 Mobile banking
15.3.1 Cases of customer experience in banking and FinTech design
15.4 Google Pay
15.5 PayPal
15.6 24/7 Gadgets
15.6.1 Banking development with ICT
15.7 Artificial Intelligence (AI) in banking
15.7.1 Uses of AI in banking
15.8 Metaverse in banking
15.8.1 The progression that resulted in metaverse in the banking sector
15.8.2 Real-world financial use cases for the metaverse
15.8.3 Global case studies of real-world multimedia banking
15.9 Conclusion
References
16 Concluding remarks—fintech and technology of today and tomorrow
16.1 Introduction
16.2 Virtual reality and augmented reality banking
16.3 Digital revolution in the fintech era
16.4 Conclusion
References
Index
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