Index

Citation

(2022), "Index", Sood, K., Balusamy, B., Grima, S. and Marano, P. (Ed.) Big Data Analytics in the Insurance Market (Emerald Studies in Finance, Insurance, and Risk Management), Emerald Publishing Limited, Leeds, pp. 299-312. https://doi.org/10.1108/978-1-80262-637-720221018

Publisher

:

Emerald Publishing Limited

Copyright © 2022 Kiran Sood, Balamurugan Balusamy, Simon Grima and Pierpaolo Marano

---

INDEX

Note: Page numbers followed by “n” indicate notes.

ABI/INFORM Collection journal database, 233n1

Accuracy

, 46, 255

Actuarial science

, 60

Actuarial science cybersecurity in insurance sector

, 15–16

AdaBoost

, 35, 41–42

Advancement of insurance industry

big data use cases in insurance industry

, 225–228

characterisation of big data and insurance

, 224–225

findings

, 232–233

insurance companie’s big data strategies

, 228–230

new technologies and applications from big data in insurance

, 230–232

research methodology

, 224

review of literature

, 223–224

statement of problem

, 224

value

, 222

Adverse selection

, 69

Affirmation

, 3

Agent recruitment, AI and

, 291

Agriculture

Big Data in

, 107

insurance

, 87

Alexa (virtual assistants), 209, 235n25

Alibaba (e-commerce companies)

, 61

Alphabet (Google)

, 61

Amazon (e-commerce companies)

, 61, 276

Amazon Web Services (AWS)

, 289

American Express

, 65

American International Group (AIG)

, 197

Analytical methodologies

, 66–67

Analytics analysis

, 280

Anomaly detection techniques

, 34

Anxiety about market

, 73

Apache Cassandra

, 111

Apache Flink

, 230

Apache Hadoop

, 111, 153

Apache Hive

, 111, 153

Apache Kafka

, 111, 231

Apache project

, 75

Apache Spark

, 111, 230

Apache Storm

, 111

Apple (e-commerce companies)

, 61, 276

Application fraud

, 33

Application programming interface (API)

, 230, 267

Applied k-neighbour algorithm

, 25

Arguments

for CBDC increasing financial inclusion

, 244–245

against CBDC increasing financial inclusion

, 245–247

Artificial intelligence (AI), 22, 58, 107, 166, 176, 208, 234n8

, 276, 288

AI, insurance, and future prospects

, 293–294

AI and agent recruitment

, 291

AI and e-payment systems

, 292

AI and health insurance claims

, 292–293

AI and insurance industry in India

, 291

Chatbot

, 292

driving forces for adoption of AI and ML

, 256–257

in India

, 289–291

and insurance

, 294

literature review

, 289, 290

overview

, 289

robo-advisor insurance agents

, 292

training and virtual meeting

, 293

Artificial neural networks (ANNs)

, 24, 25

Assets under management (AUM)

, 23

Association rules

, 34

Atomisation

, 227–228

Automatic systems

, 32

Automation

, 157

Automobile Regulatory Planning Commission

, 277

Automotive industry

autonomous parking

, 142

driving

, 140

edge computing in

, 140

electric vehicle battery maintenance predictivity

, 142

infotainment systems

, 140–141

Keyless Car Entry

, 142

monitoring and alerting for car

, 142

smart city

, 142

vehicle-to-vehicle communication

, 142

Autonomous parking

, 142

Ayushman Bharat

, 86

Back-propagation neural networks (BPNNs)

, 22

Bank verification number (BVN)

, 63

Banking Financial Services and Insurance (BFSI)

, 291

Bayesian networks

, 34, 257

Behavioural fraud

, 33

Bibliometric analysis

, 209, 211

background

, 208–209

data and methodology

, 210–211

related works

, 209–210

result

, 212–218

Bibliometric R-package

, 208, 211

Biblioshiny (web interface app)

, 211

Big data (BD)

, 276

Big Data 1. 0

, 85

Big Data 2. 0

, 85

Big Data 3. 0

, 85

connect with insurance sector

, 278–280

knowledge

, 278

laws of BD

, 277–278

planning of BD use in life insurance

, 277

role in insurance sector

, 283

Big Data

, 59, 104–105, 109, 146–147, 208, 222

agriculture and forestry

, 107

analytical methodologies

, 66–67

application in different area of insurance

, 171–175

biomedicine sector

, 108–109

characterisation of big data and insurance

, 224–225

corporate sector

, 108

creation of Big Data in different sectors

, 105

definitions

, 67–68

disruptive technologies of

, 175–178

education sector

, 105–106

energy and transport sector

, 107–108

evolution

, 85

five vs. of

, 148–150

fusion of

, 78–79

healthcare sector

, 106–107

important for business

, 147

for insurance data

, 62

insurance sector

, 106

mining

, 154

processing

, 58

scientific research

, 109

security factors of insurance

, 69–75

several technologies are responsible for increase in data volume

, 63

significance of big data in insurance companies

, 168–171

three vs. model of Big Data

, 64–65

use cases in insurance industry

, 225–228

Big data analytics (BDA)

, 58–59, 83, 110–111, 146, 166

accuracy

, 46

achieving sustainable economic growth via

, 114–120

adoption in Indian insurance industry

, 92–94

architecture

, 85–86

BDA and Indian Health Care

, 157–158

benefits associated with use of

, 122–124

benefits in insurance industry

, 86

big data affecting different segments

, 151

big data strategies of insurance companies

, 151–152

big data use cases in insurance industry

, 154–157

challenges associated with use of Big Data analytics

, 124–128

comparative analysis with existing methods

, 49–50

data analytics benefit insurers

, 151

enhanced ML model

, 34–35

experiment and results on German CCF data set

, 47–48

experimental results on Taiwan CCF data set

, 48–49

F-measure

, 47

Five vs. and healthcare system and life insurance

, 158–161

five vs. of big data

, 148–150

fraudulent credit card payments

, 32–33

history

, 85

India’s insurance industry through big data

, 152

insurance industry in India

, 147–148

MCC

, 47

methods

, 38–46

performance measures

, 46

precision

, 47

recall

, 47

related work

, 35–38

research methodology and statement

, 152–153

results

, 46

review of literature

, 153

solution to deal with challenges associated with

, 128–129

value of Big Data insurance companies

, 150

worldwide card fraud losses in billions

, 33

Biomedicine sector, Big Data in

, 108–109

Blatant proxies

, 70

Blockchain (BC), 166, 209, 235n20

, 291

data

, 177

fraud detection using blockchain technology

, 259–260

innovation

, 5

in insurance sector

, 265–267

Bluetooth adapters

, 136

Bombay Assurance Company

, 83

Bombay Stock Exchange (BSE)

, 24

Bradford’s Law of Scattering

, 214–215

Brazil, Russia, India, China, South Africa countries (BRICS countries)

, 186–189

and FDI

, 189–190

insurance industry in

, 190–195

liberalisation of BRICS insurance markets

, 195–200

‘BRICK’ (see Brazil, Russia, India, China, South Africa countries (BRICS countries))

Business analytics (BA)

, 59

fusion of big data and BA data

, 78–79

types of analysis in

, 67

Business data analysis

, 65

Business devices

, 223

Business intelligence (BI), 58, 231 (see also Artificial intelligence (AI))

loss

, 10

Business model of insurance

brand new

, 62

new applications

, 61

use of data in

, 60

Business Source Complete, 233n1

Cash

, 243–244

Cashless India schemes

, 291

Casualty, big data application in

, 173

Catastrophe protection techniques

, 283

Central bank digital currency (CBDC)

, 242

arguments against CBDC increasing financial inclusion

, 245–247

arguments for CBDC increasing financial inclusion

, 244–245

Central Government Health Scheme (CGHS)

, 84

Central processing unit (CPU)

, 137

Chatbots, 177–178, 208, 234n14

, 292

Children Life Insurance Policies

, 283

China Banking Regulatory Commission (CBIRC)

, 199

Classification-based algorithms

, 255

Classifier’s confidence (see Precision)

Classifiers

, 34

Client lifetime value (CLV)

, 171

Clinical operations

, 157

Cloud computing, 63, 97, 209, 234n18

Cloud provider

, 12

Cloudera

, 231

Cognitive computing

, 209

Colombia, Indonesia, Vietnam, Egypt, Turkey, and South Africa (CIVETS)

, 187

Commodity Channel Index (CCI)

, 26

Communications technology (ICT)

, 3

Companies Act (2013)

, 6

Consumer Reports

, 9

Contextual integrity

, 71–72

Corporate sector, Big Data in

, 108

Corporate social responsibility (CSR)

, 122

Cost reduction

, 170

Counterfeit claims

, 293

COVID-19

, 105

Credit card

, 35

Credit card fraud (CCF)

, 32

Credit card fraud detection (CCFD)

, 32, 34

existing works on

, 37

Credit Rating

, 293

Credit risk assessment

, 65

Customer

experience

, 157, 175, 227

insight

, 156–157

loyalty

, 169

service

, 288, 289, 294

vision

, 226–227

Customer relationship management (CRM)

, 259

Customisation of insurance

, 170–171

Customised range of products

, 256

Cyber Event

, 11

Cyber laws

, 5–6

Cyberattacks

, 2–3

Cyberinsurance

, 13–14

as governance

, 12–13

Cybersecurity

, 2

cyber laws

, 5–6

cyberinsurance

, 13–14

cyberinsurance as governance

, 12–13

cyberinsurance workflow

, 4–5

dealing with potential for cyber catastrophe

, 16–17

emergence of actuarial science cybersecurity in insurance sector

, 15–16

insurance to improve cyber hygiene

, 14–15

issues in insurance industry

, 8

literature review

, 6–8

role of insurance

, 9–12

Dashboards

, 232

Data

, 278

analysis

, 232

brokers

, 123

cycle structure

, 281

fusion technologies

, 78

generation

, 104

integration

, 147

integrity

, 99, 149–150

mart

, 58

portability

, 74

Preparation

, 147

privacy

, 2–17, 267, 271

Data analytics (DA)

, 63, 82, 255, 277, 282–283

benefit insurers

, 151

in insurance sector

, 265–267

role in insurance sector

, 283

Data mining (DM)

, 22, 25, 34, 66

Data protection

, 264

data protection, privacy issues, and digital ethics

, 267–268

findings

, 270–271

insurance industry

, 265

literature review

, 265

risk management and role in data protection

, 268–270

usage of blockchains and data analytics in sector of insurance

, 265–267

Data Quality

, 147

Data reduction

, 38–39

Data Science (DS)

, 280

characteristics of

, 281

DS use in insurance sector

, 281

global interest in DS

, 281–282

role in insurance sector

, 283

Data sets

, 224

description

, 42

Data virtualization

, 146

Data warehouse technology

, 58, 64

Database management systems (DBMS)

, 223

Date availability

, 256

DB2®Analytics Accelerator

, 65

Decentralisation

, 259–260

Decision support system for insurance sector

, 260

Decision theory

, 66

Decision tree (DT)

, 34, 255, 258

Decision-making optimisation

big data technology development for insurance data

, 62–68

confidentiality and security of data

, 77–78

direction of future research

, 76

examples of big data and BA methods

, 78

examples of effective strategies for analysing heterogeneous data

, 78

fusion of big data and BA data

, 78–79

risk and benefit balance

, 75–76

security factors of insurance big data and privacy

, 69–75

social and economic benefits of customer in insurance

, 68–69

use of data in business model of insurance

, 60–62

Deep learning tools

, 230

Defenders

, 17

Descriptive analytics, 233n7

Diagnostic analysis

, 66

Digital attack

, 12

Digital currency

, 243

Digital danger protection

, 14

Digital divide

, 127

Digital ethics

, 267–268

Digital event

, 12

Digital illiteracy

, 247

Digital India (DI)

, 82, 288, 292

Digital insurers

, 62

Digital misfortunes

, 16

Digital monitoring

, 68

digital monitoring-based business models

, 71

Digital payments

, 253

Digital protection

, 3, 12

approaches

, 8

market

, 4

Digital Standard

, 9, 10

Digital technologies

, 73–74, 168

Digital transformation

, 288

Digital wallets

, 292

Digitalisation

, 209

of society

, 270

Digitisation

, 73, 86, 209, 259

‘Discrimination’

, 70

Dishonest insurance agents

, 253

Disruptive innovation in insurance sector

AI

, 176

big data application in different area of insurance

, 171–175

Blockchain data

, 177

Chatbots

, 177–178

disruptive impact of disruptive technologies

, 178–179

disruptive technologies of big data

, 175

findings

, 179–180

Insurtech

, 177

IoT

, 176–177

literature review

, 167–168

machine learning

, 175–176

numerous authors define big data

, 166–167

predictive analytics

, 176

significance of big data in insurance companies

, 168–171

social media data

, 177

Distributed File Stores

, 146–147

Distributed public ledger

, 259–260

Doubters

Drones

, 208

E-commerce companies

, 61

E-Know Your Customer (e-KYC)

, 289

E-payment systems, AI and

, 292

EconLit Full Text, 233n1

Economic benefits of customer in terms of insurance

, 68–69

Edge computing

, 135–136

AdaBoost for OBD-II data classification

, 137–138

in automotive industry

, 140–142

background

, 136

OBD-II data

, 137

procedure of model

, 139

results

, 140

working principle

, 137

EdgeX Foundry server

, 137

Education sector, Big Data in

, 105–106

Effective fraud detection techniques

, 32

Effective strategies for analysing heterogeneous data

, 78

Electric vehicle battery maintenance predictivity

, 142

Electronic health record (EHR)

, 86, 172

Embarrassment on right to self-determination

, 71

Emerging and Growth-Leading Economies (EAGLES)

, 187

Emerging technologies in insurance market

BD connect with insurance sector

, 278–280

BD knowledge

, 278

DA

, 282–283

data

, 278

DS

, 280–282

financial data in insurance sectors

, 277

laws of BD

, 277–278

planning of BD use in life insurance

, 277

role of BD, DS, and DA in insurance sector

, 283

safety net provider

, 276–277

Employees State Insurance Scheme (ESIS)

, 84

Energy sector, Big Data in

, 107–108

Enhanced data

, 270

Enterprise data centre (EDH)

, 226

European Union (EU)

, 6

Exclusion criteria

, 88

Experience of customers

, 227

Exterior card fraud

, 34

External card fraud

, 34

F-measure

, 47

Facebook

, 110, 111, 276

Fair Information Practices (1980)

, 76

Fake web portals

, 254

Falsification

, 6

Feature-based Selection Algorithms

, 255

Financial data in insurance sectors

, 277

Financial fraud

, 32

Financial inclusion

, 242

arguments against CBDC increasing financial inclusion

, 245–247

arguments for CBDC increasing financial inclusion

, 244–245

literature

, 243–244

Financial technology (FinTech)

, 291

Financially engineered services (FE services)

, 280

FinTech

, 87, 209

Firmer authorisation techniques

, 267

First Industrial Revolution (Insurance 1. 0)

, 84

FLANN-GA model

, 25

Foreign direct investment (FDI)

, 186

barriers in Indian insurance sector

, 200–202

BRICS and

, 189–190

BRICS countries

, 187–189

impact on India’s Life Insurance Sector

, 202–203

impact on India’s non-life insurance sector

, 203

insurance firms

, 185–186

insurance industry in India

, 190–195

liberalisation of BRICS insurance markets

, 195–200

problem statement

, 186

Foreign Investment Promotion Board

, 83

Forestry, Big Data in

, 107

Fourth Industrial Revolution (Insurance 4. 0)

, 84

Fraud

, 32

pattern detection, 234n11

Fraud detection

, 65, 156, 225

using blockchain technology

, 259–260

traditional methods of

, 254

Fraud detection systems (FDSs)

, 32

Fraudsters

, 253

Fraudulent claims

, 169–170, 253

Fraudulent credit card payments

, 32–33

Functional danger

, 15

Fusion of big data and BA data

, 78–79

General Agreement on Trade in Services (GATS)

, 199

General Data Protection Regulation (GDPR)

, 74, 267

General Insurance Corporation of India (GICRE)

, 23

Genetic algorithm (GA)

, 34

Genomic analysis

, 158

German CCF data

attributes

, 43

experiment and results on German CCF data set

, 47–48

German credit card

, 35

German data set

, 42

Global Data Fabric

, 63

Global interest in DS

, 281–282

Global positioning system (GPS)

, 63, 118

Google

, 276

Google Big Query

, 65

Google Drive

, 65

Google Meet

, 105

Google scholar

, 88

Google Tensor Flow

, 230

Governance, cyberinsurance as

, 12–13

Government insurance companies

, 84

Gradient boosting (GB)

, 35, 41

Gross domestic product (GDP)

, 188

Gross national product (GNP)

, 198

Guarantors

, 17

Hadoop

, 75, 111, 153

Hadoop Distributed File System (HDFS)

, 98, 153

Hadoop/Map-Reduce framework

, 87

Haven Life

, 227

HDFC Chiller

, 292

HDFC Life Insurance Company Ltd. (HDFCLIFE)

, 23, 292

Health care

Big Data in

, 157

for customers

, 156

Five vs. and healthcare system and life insurance

, 158–161

system in India

, 82

Health insurance

AI and health insurance claims

, 292–293

scheme

, 86

Healthcare sector

BDA in

, 172

Big Data in

, 106–107

Human resources (HR)

, 16

IBM®Enterprise® EC12

, 65

ICICI Lombard General Insurance Company Ltd. (ICICIGI)

, 23

ICICI Pockets

, 292

ICICI Prudential Life Insurance Company Ltd. (ICICIPRULI)

, 23–24, 292

Identity theft

, 253

Illegitimate insurance companies

, 253

Image recognition, 234n11

Immutability

, 259–260

In-memory data fabric

, 146

Inclusion criteria

, 88

India

AI and insurance industry in

, 291

AI in

, 289–291

FDI impact on India’s life insurance sector

, 202–203

FDI impact on India’s non-life insurance sector

, 203

insurance industry through big data

, 152

Indian Health Care, BDA and

, 157–158

Indian health insurance industry

, 292–293

Indian insurance companies

background of study

, 24–26

GICRE

, 23

HDFCLIFE

, 23

ICICIGI

, 23

ICICIPRULI

, 23–24

insurance industry

, 22

methodology

, 26–28

NIACL

, 24

results

, 28–29

SBILIFE

, 24

Indian Insurance Companies Act

, 83

Indian insurance industry

background

, 83

BDA adoption in

, 83

BDA architecture

, 85–86

BDA benefits in insurance industry

, 86

BDA history

, 85

characteristics of included studies

, 89–94

data extraction and data synthesis

, 89

eligibility criteria

, 88

findings of included studies

, 95–99

healthcare system in India

, 82

insurance history in India

, 83–84

InsurTech history

, 84–85

literature overview

, 87–88

methodology

, 88

research gap

, 86–87

research objectives

, 87

results

, 95

search strategy and selection process

, 88

Indian Life Assurance Companies Act

, 83

Indian Mercantile Insurance Company Ltd.

, 83

Indian Penal Code (IPC)

, 6

Individualisation

, 72

insurance individualisation improvement concern

, 72

Inequitable field of play

, 74

Information and communication technology (ICT)

, 99, 247

Information collection

, 226

Information system software

, 65

Information technology (IT)

, 16, 66, 82, 130, 186

Information Technology Act (ITA)

, 5

Infotainment systems

, 140–141

Infringement on right to self-determination

, 71

Inner card fraud

, 34

Innovation

, 265

Instagram

, 110

Insurance

, 58, 185, 252, 288

AI and

, 293–294

awareness

, 202

BI loss

, 10

business in India

, 83

case

, 12

change in control

, 11

characterisation of big data and

, 224–225

cloud provider

, 12

customisation of

, 170–171

digital attack

, 12

digital event

, 12

digital protection

, 10

Digital Standard

, 9–10

enterprise

, 264–265

example of insurance claims fraud

, 253–254

firms

, 172, 185–186

history in India

, 83–84

to improve cyber hygiene

, 14–15

individualisation improvement concern

, 72

insurance fraud

, 252–253

products

, 62

research areas of cyberinsurance

, 11

role

, 9

security factors of insurance big data and privacy

, 69–75

situation

, 11–12

social and economic benefits of customer in terms of

, 68–69

traditional methods of fraud detection

, 254

Insurance Act

, 83, 201

of India

, 190

Insurance claims fraud

, 253–254

Insurance companies

better product pricing

, 171

better profit space

, 168

big data strategies

, 228–230

big data strategies of

, 151–152

with business models

, 270

cost reduction

, 170

customisation of insurance

, 170–171

enhancing sales and maintain customer loyalty

, 169

evaluate customer lifetime value

, 171

greater market share

, 168–169

greater premium dispersion

, 171

impact on internal processes

, 170

monitoring online reputation

, 169

more growth opportunities

, 169

prevent fraud

, 169–170

risk assessment

, 170

significance of big data in

, 168

Insurance fraud

, 252

types of

, 252–253

Insurance industry

, 22, 265

in BRICS

, 190–195

in India

, 190–195

Insurance Laws Amendment Act (2015)

, 201

Insurance management systems

, 166

Insurance Market, DS use in

, 280–282

Insurance Regulatory and Development Authority (IRDA)

, 84, 190, 277

Insurance Regulatory and Development Authority of India (IRDAI)

, 23

Insurance sector

Big Data in

, 106

DS use in

, 281

significance of ML in

, 255–256

Insurers

, 60–61, 69

data analytics benefit

, 151

InsurTech

, 83, 177, 209–210, 265

entrepreneurs

, 61

history

, 84–85

Integrity concerning surrounding context

, 71–72

Intel

, 111

Intelligence transformation

, 288

Intelligent assistants

, 208

Intelligent fraud prediction

, 256

decision support system for insurance sector

, 260

driving forces for adoption of AI and ML

, 256–257

fraud detection using blockchain technology

, 259–260

intelligent methods for fraud prediction

, 257–259

Intelligent methods for fraud prediction

, 257–259

‘Intelligent’ chatbots, 234n15

International Data Corporation (IDC)

, 62

International Monetary Fund (IMF)

, 195

International Standardization Organization (ISO)

, 136

Internet

, 222

Internet of things (IoT)

, 58, 107, 109, 136, 166, 176–177, 208, 222, 266

IRDAI (Reinsurance) Regulations

, 202

Istanbul Stock Exchange National 100 Index (ISE National 100 Index)

, 24

Italian National Framework for Cybersecurity and Data Protection

, 6

Jan-Dhan Yojana

, 291

Japanese Life insurer, 234n17

John Platt’s sequential minimal optimisation approach

, 26

Jordanian stock exchange

, 25

Jupyter Notebook

for insurance

, 140

for smart city

, 141

K-nearest neighbour (KNN)

, 34, 255

Keyless Car Entry

, 142

KNN

, 40–41

Knowledge technology

, 280

L1-Based selection algorithms

, 255

Laws of BD

, 277–278

Learning organisation culture

, 96

Liberalisation of BRICS insurance markets

, 195–200

Life Insurance

Big Data in

, 157

Five vs. and healthcare system and life insurance

, 158–161

sector

, 147

Life insurance companies in India

, 86

Life Insurance Corporation (LIC)

, 83, 147, 193

Like resource-based theory

, 83

Linear programming models

, 66

LinkedIn

, 111

Literature review of fraud detection

, 254–255

Log files

, 149

Logistic regression (LR)

, 34, 258

Lopsidedness of data

, 276

Lotka’s Law

, 215–216

Low-code/no-code platforms

, 208

Machine learning (ML)

, 32, 35, 175–176, 230, 255

algorithms, 234n9

for fraud detection

, 254

insurance

, 252–254

intelligent fraud prediction

, 256–260

literature review

, 254–255

significance of ML in insurance sector

, 255–256

strengths and limitations of

, 38

theoretical contributions and implications, 256–

Machine Learning Library (MLlib)

, 230

Machine-generated data

, 149

MAD method

, 25

Madras Equitable Life Insurance Society

, 83

Map-Reduce model

, 230

MapReduce

, 153

Market power for one’s gain

, 73–74

Marketing

, 65

mix

, 202

research

, 175

Massive data sets

, 58

Mathew correlation coefficient (MCC)

, 47

Maximum apparent oversights

, 268

Mean absolute percentage error (MAPE)

, 25

Mean square error (MSE)

, 25

Mexico, Indonesia, South Korea, and Turkey (MIST)

, 187

Micro-insurance

, 86

Microsoft

, 111

Min-Max

, 25

Mitigating risk related to exclusion of finances

, 268

Mobile device

, 63

Mobile phones

, 209

Mobile wallets (MB)

, 292

Modified fisher discriminant analysis

, 34

Monetary policy

, 243

Money Coach

, 292

MongoDP

, 111

Monitoring and alerting for car

, 142

Moral hazard

, 69

Motor coverage-associated documents

, 266

Multivariate adaptive regression splines (MARS)

, 24

Mutual aid, consequences for

, 72–73

Naive Bayes (NB)

, 255

National Institute of Standards and Technology (NIST)

, 268

National Stock Exchange (NSE)

, 24

National Strategy for Artificial Intelligence (NSAI)

, 288

Natural Language Processing (NLP)

, 281, 292

Network safety affirmations

, 3

Neural networks (NNs)

, 34, 255, 257

New distribution models

, 61

New economic policy (NEP)

, 190

New India Assurance Company Ltd. (NIACL)

, 24

NIST Cybersecurity Framework

, 6

Non-governmental organisations (NGOs)

, 84

Non-life insurance

, 69

Non-linear regression approach

, 25

Normalisation methods

, 25

NoSQL Databases

, 146

Not best SQL (NoSQL)

, 223

On-board diagnostics (OBD-II)

, 136

data

, 137

On-demand insurance

, 62

Online analytical processing (OLAP). See Data warehouse technology

Online travel agencies (OTAs)

, 168

Open-source analytics

, 66

Open-source distributed computing platforms

, 63

Open-source software

, 75

Openness Of Market

, 74–75

Operations research methodology

, 65

Opportunity

, 264

Optimisation of price

, 226

Organisational learning theory

, 83

Oriental Life Insurance

, 83

Packages

, 266

Patient profile analytics

, 157

Pay-per-use protection models

, 5

Payment product episodes

, 16

Paytm

, 291, 292

Peer group analysis

, 34

Peer-to-peer insurance

, 62

Pension Life Insurance Policies

, 283

People’s Insurance Company of China (PICC)

, 198

Performance measures

, 46

Personal information

, 61

Phishing

, 253

PhonePe

, 291

Point of sale

, 292

Policy fraud

, 253

Policyholders

, 15

Portable gadgets

, 266

Post-privatisation

, 202

Postal theft fraud

, 34

Precision

, 47, 255

Predicted stock prices

, 25

Predictive analysis

, 146

Predictive analytics

, 62, 176

Predictive modeling

, 66, 209, 253–254

Premium

, 252

Prescriptive analytics, 66, 234n7

Price ceilings

, 201

Pricing

, 155

Principal component analysis (PCA)

, 35, 39–40

PRISMA Framework

, 89

Privacy

issues

, 267–268

security factors of insurance

, 69–75

Private sector insurance companies

, 148

Privatisation of insurance

, 84

Probabilistic Neural Networks (PNN)

, 255

Probability theory

, 60

Process automation

, 61

Products

, 69

Professional detectives

, 34

Property & Casualty (P&C)

, 208

Property, big data application in

, 173

Protection

, 8

Protection business

, 276

Public health

, 158

Public sector insurance companies in India

, 147

Qlik

, 232

QlikView

, 231

Quadratic discriminant analysis (QDA)

, 35, 41

Quality improvisation of insurance sector

, 227

Quality of services

, 202

‘Quiet digital’

, 14

Radial basis function (RBF)

, 26

Random Forest algorithm (RF algorithm)

, 34, 42, 255, 257, 258

Raspberry-Pi Model 3b

, 135–136

Raw data

, 38

Real-time data analysis

, 59

Recall, 47 l

, 255

Registrar of Company (RoC)

, 24

Regulating privacy

, 270

Reinsurers

, 17, 198, 201, 276

Related literature

, 254

Relational database management system (RDBMS)

, 58, 99, 153

Relative strength index (RSI)

, 26

Research and development (R&D)

, 157

Reserve Bank of India (RBI)

, 289

Retail weighted received premiums (RWRPs)

, 23

Revenue management

, 174

Ridge classifier

, 35

Right for Control of Data

, 268

Right for Secure Transaction and Data sharing

, 267

Risk

, 186

assessing

, 226

consequences for risk-sharing

, 72–73

diversification

, 170

management and role in data protection

, 268–270

pooling

, 73

Robo-advisor insurance agents

, 292

Robotic process automation

, 208

Routine maintenance

, 172

S&P CNX Nifty index of India

, 24

Safety net providers

, 5, 14, 16

SBI Intelligent Assistant (SIA)

, 292

SBI Life Insurance Company Ltd. (SBILIFE)

, 24

Scientific research, Big Data in

, 109

Search and Knowledge Discovery

, 147

Second Industrial Revolution (Insurance 2. 01)

, 84

Securities and Exchange Board of India (SEBI)

, 292

Securities and Exchange Commission (SEC)

, 10

Security

, 7, 256, 259–260

access to services and lack of participation

, 72

anxiety about market

, 73

consequences for mutual aid and risk-sharing

, 72–73

embarrassment and infringement on right to self-determination

, 71

factors of insurance big data and privacy

, 69

inequitable field of play

, 74

inequity and unequal treatment

, 70–71

insurance individualisation improvement concern

, 72

integrity concerning surrounding context

, 71–72

using market power for one’s gain

, 73–74

openness of market

, 74–75

privacy and data protection

, 70

volatility

, 73

Security accreditation

, 3

Self-organising networks

, 34

Semi-structured data

, 149

Sensitivity

, 47

Serious Frauds Investigation Office (SFIO)

, 6

Settling policy claims

, 155–156

Sigmoid Median

, 25

Siri (virtual assistants)

, 209

Smart city

, 142

Smart grid

, 229

Smart meters

, 229

Social benefits of customer in terms of insurance

, 68–69

Social media

, 177, 222, 259

Social network analysis (SNA)

, 257, 258–259

Society of Automotive Engineers (SAE)

, 136

Spark GraphX

, 230

Spark SQL

, 230

Spark Streaming

, 230

Specialists

, 15

Splunk

, 231

SQL

, 223

SQLite DBs

, 137

Statistical tools

, 65

Strategic marketing

, 174

Stream analytics

, 146

Structured data

, 149

‘Structured information’

, 278

Subscriber identification module (SIM)

, 63

Supervised approach

, 34

Support vector machines (SVMs)

, 22, 34

Support vector regression (SVR)

, 24, 25

Sustainable development goals (SDGs)

, 112–113

challenges associated with use of Big Data analytics in governance and achievement of

, 124–128

data sources against some critical SDGs

, 120–121

Sustainable economic growth

, 111–112

achieving sustainable economic growth via big data analytics

, 114–120

benefits associated with use of big data analytics

, 122–124

Big Data

, 104–105, 109–111

Big Data analytics

, 110–111

challenges associated with use of BIG DATA ANALYTICS

, 124–128

creation of Big Data in different sectors

, 105–109

SDGs

, 112–113

solution to deal with challenges associated with Big Data analytics

, 128–129

Systematic literature review (SLR)

, 83

Tableau

, 111, 232

Taiwan credit card

, 35

Taiwan data set

, 42, 44–46

experimental results on Taiwan CCF data set

, 48–49

Tanh and Sigmoid estimator method

, 25

Tanh estimator

, 25

Technology

, 267, 293

in Big Data analysis

, 146

Telematics

, 208, 209

10 Vs. of big data

, 154

Third Industrial Revolution (Insurance 3. 0)

, 84

Threat mapping

, 156, 226

Three vs. model of Big Data

, 64

Time constraint

, 127

Trade-offs

, 58

Traditional methods of fraud detection

, 254

Training and virtual meeting

, 293

Transport sector, Big Data in

, 107–108

Travel insurance, big data application in

, 174–175

Tree-Based selection algorithms

, 255

Triton Insurance Company Ltd.

, 83

Trust

, 270

Truthfulness of data

, 149

Underwriting

, 155, 265

Unified payment interface (UPI)

, 292

Unit Linked Insurance Policies (ULIPs)

, 283

Universal asynchronous receiver-transmitter (UART)

, 137

Universal Life Insurance Policies

, 283

Unstructured data

, 63, 149

Unsupervised approach

, 34

Used based insurance (UBI)

, 62, 82

Value

, 222

of big data insurance companies

, 150

Vehicle-to-vehicle communication

, 142

Versatility

, 127

Virtual assistants

, 209, 256

Virtual meeting, training and

, 293

Volatility

, 73

Volume, velocity, variety, veracity, value (Five vs. of big data)

, 148–150, 158–159

impact of big data on future of insurance159–161

and healthcare system and life insurance

, 158

impact

, 159

primary challenges

, 161

Wal-Mart

, 65

Wearable technology

, 208

Web Application Programming Interface

, 99

Web interface app

, 211

Web of Science (WoS)

, 210

World Health Organization

, 82

World Wide Web (WWW)

, 85

You Only Need One (SBI YONO)

, 292

Z-Score, Decimal Scaling

, 25