Index

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A

• accountants, 2
• acquisitions
  • discussion, 308–309
  • Numerix, 245, 266–267, 276–277
• AFM (Authority for the Financial Markets), 47
• Agile development process. See also microservices
  • discussion, 61, 71, 73–74
  • open source software and, 176, 183
  • programming languages and, 89
  • prototyping and, 355
  • rapid application development (RAD), 71, 73, 77–78
  • waterfall development versus, 71–73, 176
• Agile Project Management for Dummies (Layton, Ostermiller), 74
• AI. See artificial intelligence (AI)
• Alibaba, 24, 29
• Alipay, 28
• Allaire, Jeremy, 101
• Amazon, 21, 28–29, 33
• Amazon Web Services (AWS), 28, 112
• American Institute of Certified Public Accountants Auditing Standards Board, 130
• AMF (annual maintenance fee), 12
• AMF (Autorité des Marchés Financiers), 47
• AML (anti-money laundering), 42, 56
• Android OS, 152, 158
• angel investors, 284–285, 357
• ANN. See artificial neural network (ANN)
• annual maintenance fee (AMF), 12
• Ant Financial, 14, 21, 24, 26
• anti-money laundering (AML), 42, 56
• Apple, 28–29, 151
• Apple Pay, 28
• application programming interfaces (APIs)
  • benefits of, 62–64
  • cloud computing and, 110
  • discussion, 18, 30, 61–62
  • gateways, 74
  • graphical user interfaces (GUIs) and, 160
  • legacy systems and, 253
  • management, 76
  • open source software and, 183
  • strategy for building, 64–66
• apps
  • banking, 153
  • capital markets trading, 156–157
  • developer mentality toward, 151–152
  • discussion, 151–152, 154
  • graphical user interfaces (GUIs) for, 157–160
  • hybrid, 152
  • lending, 154–155
  • native, 152
  • RegTech, 155–156
  • requirements in developing, 160–161
  • types of, 152–153
  • wealth management, 153–154
  • web, 152
• artificial intelligence (AI). See also machine learning
  • alternative data for, 218–221
  • for analyzing investments, 291
  • artificial neural network (ANN) and, 212, 214–215
  • banking apps and, 153
  • capital markets trading apps and, 156–157
  • chat bots and, 217–218
  • decentralized applications (DApps) and, 102–103
  • developing skills for, 300–301
  • discussion, 32–33, 211, 305–306
  • disruptions caused by, 34–35
  • in Fintech Cube, 11
  • history of, 212–213
  • Julia programming language and, 91–92
  • learning methods within, 215
  • open source software and, 183
  • opportunities presented by, 37
  • practical applications for, 211, 215–216, 219–220
  • Python programming language and, 90
  • reinforcement learning, 212
  • subcategories within, 213–214
  • wealth management apps and, 154
• Artificial Intelligence (Time), 212
• artificial life, 214
• artificial neural network (ANN). See also machine learning (ML)
  • discussion, 212, 214–216
  • supervised learning in, 216–217
• Asia Pacific region, 26, 45
• Aspiration, 153
• asset management, 11, 13, 15, 22, 34
• asynchronous compute loads, 125
• asynchronous software, 69
• augmented reality, 11
• Australia, 45
• Authority for the Financial Markets (AFM), 47
• auto scaling, 111
• automation, 1, 23
• Autorité des Marchés Financiers (AMF), 47
• AWS (Amazon Web Services), 28, 112

B

• B2B. See business-to-business
• B2B2C. See business-to-business-to-consumer
• B2C. See business-to-consumer
• B2G. See business-to-government/regulator
• BaFin (Bundesanstalt für Finanzdienstleistungsaufsicht), 47
• BAM (business activity management), 169–170
• banking, 21, 28–29, 33, 153
• Banking Circle, 155
• batch processing, 80–81
• Bear Stearns, 24–25
• Betterment, 154
• BFT (Byzantine fault tolerance), 141
• BI. See business intelligence
• big data analytics
  • artificial intelligence (AI) and, 212
  • disruptions caused by, 35
  • in Fintech Cube, 11
  • hybrid cloud computing and, 119–120
  • strategy for, 207–208
• big data sets, 218–219
• BigTech
  • barriers faced by, 30–31
  • discussion, 28
  • financial services offered by, 28
  • partnering with, 29–30
  • regulation of, 42–44
• biological authentication, 303–304
• biological neural network (BNN), 214
• Bitcoin
  • decentralized data structure (DDS), 99–100
  • discussion, 36, 95–96
  • origin of, 101
• Bitcoin Cash, 101, 141
• BitPay payments processing, 101–102
• blockchain. See also permissioned blockchains
  • anonymity and, 142–143
  • companies using, 101–102
  • concerns with, 141–142, 144–145
  • consensus protocol in, 96, 103, 138
  • considerations when implementing, 147–149
  • consortium, 140, 143
  • decentralized applications (DApps), 95–96, 99–100, 139
  • decentralized data structure (DDS) and, 135
  • discussion, 32, 95, 99, 135
  • distributed ledger technologies and, 135
  • in Fintech Cube, 11
  • forking in, 144
  • hybrid, 140
  • immutability, 146
  • industry disruption caused by, 135
  • key benefits, 100
  • main organizations, 142
  • mining, 96, 101, 136
  • network types, 139–140
  • online courses on, 308
  • principles, 140–141
  • private, 140, 143
  • public, 140, 143
  • security, 142–146
  • smart contracts, 138–139
  • uses for, 146–147
• Bloomberg, 41
• BNN (biological neural network), 214
• Brexit, 40, 47–48
• brokers, 22
• building technology
  • acceleration in, 333–334
  • buying versus, 228–229, 327
  • considerations when, 327–329
  • deciding factors in, 329–330, 332–333
  • for digital transformation, 229–230
  • open source instead of, 331–332
  • risks in, 330
  • trade-offs in, 330
• Bundesanstalt für Finanzdienstleistungsaufsicht (BaFin), 47
• Burns, Robert, 353
• business activity management (BAM), 169–170
• business intelligence (BI)
  • considerations when implementing, 172–174
  • strategy for developing, 163–165
  • tools, 158, 165–172
• business models, 10–11
• business plans, 351–354
• business-to-business (B2B)
  • consortium blockchain in, 143
  • discussion, 1–2
  • in Fintech Cube, 10–11, 13
  • investments in, 280
  • passive asset management in, 34
• business-to-business-to-consumer (B2B2C), 10–11, 280
• business-to-consumer (B2C), 10–11, 34, 280–281
• business-to-government/regulator (B2G), 10–11
• buying technology
  • building versus, 228–230
  • considerations when, 327–329
  • deciding factors in, 334–335
  • open source instead of, 331–332
  • to replace legacy systems, 230–232
  • risks involved in, 330, 335
  • selecting vendors and products when, 335–336
  • trade-offs in, 330
• Byzantine fault tolerance (BFT), 141

C

• C# programming language, 70, 89
• C++ programming language, 70, 89
• Canary Wharf Group, 308
• capacity planning, 93–94
• capital markets, 21, 156–157
• Capital Markets Tech, 32
• CD (confidential data), 122
• CDS (credit default swaps), 9
• Center of Internet Security (CIS), 112
• central processing unit (CPU)
  • discussion, 85
  • graphics processing unit (GPU) versus, 85–88
  • real-time transactions and, 76
• CEP (complex event processing), 170
• CES (cloud encryption service), 123–124
• CFPB (Consumer Financial Protection Bureau), 46
• CFT (crash fault tolerance), 141
• CFTC (Commodity Futures Trading Commission), 46
• challenger banks, 11, 281
• change agents, 264, 269
• chaos theory, 8
• chat bots, 217–218, 304–305
• Chat Bots Magazine, 218
• cheat sheet, 3
• Chief Digital Officer, 302
• Chime, 153
• China, 24
• China FinTech Bridge, 308
• Circle payments company, 101
• CIS (Center of Internet Security), 112
• CITRIX XenServer, 127
• Clarifying Lawful Overseas Use of Data (CLOUD), 132–133
• CLM (Component Lifecycle Management), 245
• cloud bursting, 111, 120–121
• cloud computing. See also specific types of cloud computing
  • advantages of, 111–112
  • Amazon Web Services (AWS), 28
  • application programming interfaces (APIs) and, 110
  • auto scaling in, 111
  • banking and, 33
  • capital markets apps and, 156–157
  • cloud bursting in, 111, 120–121
  • considerations when implementing, 133–134
  • delivery systems, 62, 183
  • deploying, 121–230
  • discussion, 109–110
  • disruptions caused by, 35
  • distributed ledger technologies and, 110
  • factors driving rise of, 109–110
  • in Fintech Cube, 11
  • hybrid, 119–120
  • key traits of, 111
  • legacy systems and, 121
  • private versus public, 117–119, 129–130
  • regulations, 131–133
  • security, 111–112, 121–122
  • service providers (CSPs), 111–112
  • virtualization and, 95, 126–127
• cloud encryption service (CES), 123–124
• cloud service providers (CSPs)
  • discussion, 111–112
  • encryption by, 123
  • security and, 129–130
  • self-service provisioning via, 127–128
• CloudMargin, 157
• COBOL programming language, 253, 323
• Commodity Futures Trading Commission (CFTC), 46
• Communications Platform as a Service (CpaaS), 113, 117
• complex event processing (CEP), 170
• Component Lifecycle Management (CLM), 245
• compute nodes, 137
• computer aided diagnosis, 213
• computer vision, 213
• computing technologies. See also decentralized applications (DApps)
  • capacity planning, 93–95
  • discussion, 93
  • quantum computing, 104–108
• confidential data (CD), 122
• consensus protocol
  • in blockchain, 96, 104, 138
  • in decentralized data structure (DDS), 97–98
  • discussion, 137–138, 141
  • manipulation of, 145
  • in permissioned blockchain, 103, 141
• consolidation, 309–310
• consortium blockchain, 140, 143
• Consumer Financial Protection Bureau (CFPB), 46
• containerization, 78
• Convercent, 156
• copyleft licensing, 177, 245, 348
• corporate venture capital (CVC), 235, 286–287, 310–311
• CpaaS (Communications Platform as a Service), 113, 117
• CPU. See central processing unit
• crash fault tolerance (CFT), 141
• Credible, 155
• credit default swaps (CDS), 9
• Credit Karma, 155
• credit value adjustment (CVA) calculations, 27, 50, 54
• CRM (customer relationship management), 115
• CrossAsset Software, 9, 19, 47
• crowdfunding, 11, 282–283, 357
• cryptocurrency
  • discussion, 32, 36
  • online courses on, 308
  • regulation of, 43
  • traditional institutions and, 24, 149
• CSPs. See cloud service providers
• currencies, 11
• customer relationship management (CRM), 115
• CVA (credit value adjustment) calculations, 27, 50, 54
• CVC (corporate venture capital), 235, 286–287, 310–311
• cybersecurity. See also security
  • in cloud computing, 111–112
  • discussion, 23, 33, 56
  • regulation, 42
  • third-party software and, 44

D

• DAE (distributed autonomous enterprise), 139
• DAO (decentralized autonomous organization), 139
• DApps. See decentralized applications
• data
  • semi-structured, 209
  • structured, 164, 208–209
  • unstructured, 164, 209
• data at rest, 124
• data cubes, 166
• data designations, 122
• data governance, 56
• data in transit, 124
• data in use, 124
• data lakes, 55, 205–206
• data lineage, 206
• data localization laws (DLLs), 132
• data management. See also ETL (extract, transform, load)
  • accuracy versus speed in, 82
  • database architecture and, 55, 204–206
  • differentiating data in, 208–209
  • discussion, 18, 81, 197
  • efficiency in, 83–84
  • ensuring accurate data in, 83
  • historization in, 206–207
  • key questions, 198
  • with legacy systems, 198–199
  • lineage in, 206
  • market data and, 201–202
  • NoSQL database in, 204, 210
  • refactoring, 210
  • regulation compliance in, 330
  • relational databases, 199–200, 209–210
  • security, 124–125
  • static data and, 201
  • structured query language (SQL) in, 199–200, 209–210
  • transformation, 199–200
  • types of data in, 81–82
• data mining, 167–168
• Data Platform as a Service (dPaaS), 116
• data portability, 37
• data porting, 204
• data priming, 204
• data privacy, 42
• data protection laws (DPLs), 131–132
• data protection officers (DPOs), 53
• Data Science Central, 219
• data sovereignty laws, 133
• data visualization, 168–169
• data warehouse, 204–205
• databases. See data management
• data-driven decision-making (DDDM), 275–276
• DDS. See decentralized data structure
• decentralized applications (DApps)
  • advantages and disadvantages of, 97–98
  • artificial intelligence (AI) and, 102–103
  • blockchain, 95–96, 99–100, 139
  • commonalities between all, 96
  • company-specific examples of, 101–102
  • discussion, 18, 95–96
  • distributed autonomous enterprise (DAE), 139
  • open source technologies and, 95–96
  • sources of, 100–101
  • traditional applications versus, 96–97
• decentralized autonomous organization (DAO), 139
• decentralized data structure (DDS)
  • Bitcoin, 99–100
  • blockchain and, 135
  • consensus protocol in, 97–98
  • discussion, 96–98
• Deep Blue, 212
• DeepMind, 212
• delegated proof of stake (DPoS) consensus, 137
• difference finite methods, 8
• digital banking, 11, 153
• digital currency, 29, 36, 101
• digital dashboards, 171–172
• digital transformation. See also building technology; buying technology; legacy systems; software license models (SLMs)
  • actively participating in, 301–302
  • causes for failure in, 226–228
  • consolidation and, 309–310
  • corporate venture capital investments and, 310–311
  • developing skills for, 300
  • discussion, 18, 225–226
  • ecosystems for, 302–303
  • embracing, 298–299
  • financial boards and, 296–298
  • mergers and acquisitions and, 308–309
  • new technologies and, 211, 303–306
  • online courses on, 308
  • partnerships for, 234–236, 238–241
  • planning for, 232–233, 295–296
  • sponsors, 306–308
• digital wallets, 24, 101
• disintermediation, 23–24
• distributed autonomous enterprise (DAE), 139
• distributed ledger technologies. See also blockchain
  • cloud computing and, 110
  • discussion, 33, 62, 95
  • in Fintech Cube, 11
• DLLs (data localization laws), 132
• Dodd-Frank Act, 39
• dPaaS (Data Platform as a Service), 116
• DPLs (data protection laws), 131–132
• DPOs (data protection officers), 53
• DPoS (delegated proof of stake) consensus, 137

E

• E*Trade, 154
• EBA (European Banking Authority), 48
• eBay, 28
• e-commerce, 13–14, 97–98
• ecosystems
  • advantages of strong, 297–298
  • for digital transformation, 302–303
  • FinTech, 228
• EFTs (exchange traded funds), 34
• Eggar, Daniel J., 107–108
• EIOPA (European Insurance and Occupational Pensions Authority), 48
• encryption, 123–124, 130
• enterprise software, 12
• e-payment apps, 154
• ESFS (European System of Financial Supervision), 48
• ESMA (European Securities and Markets Authority), 47–48
• Ethereum, 36, 102, 137–139
• ETL (extract, transform, load)
  • common tools, 201
  • discussion, 168, 170, 199
  • software requirements, 200–201
  • steps in, 199–200
• European Banking Authority (EBA), 48
• European Central Bank, 47–48
• European Commission, 47–48
• European Insurance and Occupational Pensions Authority (EIOPA), 48
• European Securities and Markets Authority (ESMA), 47–48
• European System of Financial Supervision (ESFS), 48
• European Systemic Risk Board, 48
• European Union
  • data portability and, 37
  • FinTech in, 26
  • regulation in, 39, 47–48, 132
• event-driven software, 67–70
• exchange traded funds (EFTs), 34
• expert systems, 213
• Extensible Markup Language (XML), 200
• extract, transform, load. See ETL
• extreme programming, 71, 73

F

• FaaS (Function as a Service), 113, 117
• Facebook, 13–14, 21, 29
• facial recognition, 211, 303–304
• FCA (Financial Conduct Authority), 40, 47–48, 357
• feature drive, 73
• Federal Deposit Insurance Corporation (FDIC), 46
• Federal Reserve, 46
• feedforward multilayer perceptron, 214–215
• Feigenbaum, Mitchell, 8
• Financial Conduct Authority (FCA), 40, 47–48, 357
• financial crisis (2008)
  • European Union response to, 48
  • Lehman Brothers bankruptcy, 27, 31
  • regulation reforms triggered by, 39
  • Sowood failure, 24–25
• Financial Policy Committee, 49
• financial services
  • automation in, 23
  • criteria in choosing, 22–23
  • discussion, 2
  • disintermediation in, 23–24
  • in Fintech Cube, 10–11
  • startup companies in, 21–22
  • traditionally on Wall Street, 25–26
• Financial Stability Board, 43
• Financial Stability Oversight Council, 46
• financial technologies, 1, 8–9, 12, 21. See also FinTech
• fingerprint scans, 303–304
• FinTech
  • acquisitions, 308–309
  • changes in, 13–15
  • company creation, 351–357
  • consolidation, 309–310
  • corporate venture capital (CVC), 310–311
  • decisions when starting in, 41
  • dimensions of, 10–11, 13
  • discussion, 1–2, 7–9
  • disruptions caused by, 18, 21–25, 27, 31, 33–37
  • ecosystems, 228
  • in European Union, 26
  • factors driving, 61–62, 109–110
  • global size of, 15–16
  • mergers, 308–309
  • new technologies used in, 211, 303–306
  • online courses on, 308
  • passporting in, 40
  • regulation compliance and, 56
  • regulatory limitations on, 40
  • subcategories within, 32–33
  • technological benefits of, 59–61
  • terminology, 18–19
  • in United Kingdom, 26
  • in United States, 26
• FINTECH Circle
  • discussion, 3–4, 10
  • Fintech Cube and, 10
  • https://fintechcircle.com, 284, 307–308
  • MasterClass, 301, 308
• FinTech Cube
  • discussion, 10–11
  • distributed ledger technologies in, 11
  • financial services in, 10–11
  • quantum computing in, 105–106
  • virtual reality in, 11
• FinTech Innovation Lab, 306
• FinTech start-ups
  • advantages enjoyed by, 21–22
  • cybersecurity and, 23
  • locations of, 25–26
  • responsiveness of, 23–24
• foreign exchange, 11, 14
• forking, 142, 144–145
• FOSH (free open source hardware), 347
• France, 47
• free open source hardware (FOSH), 347
• free software, 177–178
• Free Software Foundation (FSF), 184
• freeware, 178
• front-to-risk enterprise systems, 27
• FSF (Free Software Foundation), 184
• Function as a Service (FaaS), 113, 117
• Fundamental Review of the Trading Book (FRTB), 55
• Funding Circle, 155

G

• General Data Protection Regulation (GDPR)
  • alternative data sets and, 221
  • cloud computing security and, 130
  • discussion, 37, 52–53, 132
  • RegTech and compliance with, 55–56
• general partners (GPs), 285, 287
• Germany, 47
• GLBA (Gramm-Leah-Bliley Act), 130, 132
• Global Financial Innovation Network (GFIN), 49
• Goldenfeld, Nigel, 8
• Goodkin, Michael, 8
• Google
  • Cloud, 112
  • discussion, 13–14
  • focus of, 29
  • industry disruption by, 21, 28
  • services provided to banking, 33
• GPs (general partners), 285, 287
• GPU. See graphics processing unit
• Gramm-Leah-Bliley Act (GLBA), 130, 132
• graphical user interfaces (GUIs)
  • application programming interfaces (APIs) and, 160
  • for apps, 157–160
  • discussion, 70
• graphics processing unit (GPU)
  • central processing unit (CPU) vs., 85–88
  • discussion, 85
  • real-time transactions and, 76
• Great Recession, 41
• Greenwich Associates, 156
• GUIs. See graphical user interfaces

H

• hackers, 341
• Halo, 157
• hard forking, 142, 144–145
• hardware description language (HDL), 347
• hardware virtualization, 126
• Hawking, Stephen, 213
• hedge funds, 9, 13
• HTTPS, 123
• hybrid apps, 152
• hybrid blockchain, 140
• Hyperledger Fabric, 141
• hypervisors, 95, 126–127

I

• IaaS. See Infrastructure as a Service
• ICAP, 19
• identity management system (IMS), 129
• ILF (initial license fee), 12
• IMF (International Monetary Fund), 43
• IMS (identity management system), 129
• incremental development, 71, 73
• information governance, 56
• Infrastructure as a Service (IaaS)
  • discussion, 113–114
  • legacy systems and, 115
  • self-service provisioning in, 128
• initial license fee (ILF), 12
• in-memory computing, 94
• Innovate Finance 2019 Fintech Investment Landscape Report, 15–16
• insurance industry
  • discussion, 15
  • disruption to, 21, 34–35
  • in Fintech Cube, 11
• InsurTech
  • challenges by, 281
  • discussion, 32, 34–35
  • online courses on, 308
• Integration Platform as a Service (iPaaS), 116
• Intel, 14
• International Monetary Fund (IMF), 43
• Internet of Things (IoT), 11, 33, 219–220
• interoperability issues, 59–60
• investments
  • in business-to-business (B2B) arena, 280
  • in business-to-business-to-consumer (B2B2C) arena, 280
  • in business-to-consumer (B2C) arena, 280–281
  • in challengers to financial institutions, 281
  • by corporate venture capitalists (CVC), 286–287
  • discussion, 2, 9, 11, 279–280
  • environmental, social, and governance, 282
  • in financial institutions collaborators, 281
  • by private equites (PE), 279–280, 357
  • researching, 288–294
  • by venture capitalists (VC), 279–280, 285–287
  • via angel investors, 284–285, 357
  • via crowdfunding, 282–283, 357
• iOS, 152, 158
• IoT (Internet of Things), 11, 33, 219–220
• iPaaS (Integration Platform as a Service), 116
• iPhone 3G, 151

J

• Japan, 45
• Java programming language, 71, 89
• JavaScript Object Notation, 200, 210
• JavaScript programming language, 70
• Jazari, Ismail al-, 212
• Julia programming language, 89, 91

K

• Kasparov, Gary, 212
• key performance indicators (KPIs), 169
• killer apps, 152
• know your client (KYC), 42, 56
• KPIs (key performance indicators), 169
• Kurzweil's singularity, 212
• KYC (know your client), 42, 56

L

• LabCFTC, 46
• Larson, Jeff, 24–25
• lawyers, 2
• legacy apps, 153
• legacy systems. See also building technology; buying technology; digital transformation; event-driven software
  • ailing, symptoms of, 315–325
  • application programming interfaces (APIs) and, 253
  • challenges with, 247–248
  • cloud computing and, 121
  • cost of maintaining, 250–251
  • data management with, 198–199
  • determining obsolescence of, 249–250
  • discussion, 59, 61
  • Infrastructure as a Service (IaaS) and, 115
  • microservices architecture and, 248–249, 253, 258–260
  • migration away from
    • building, 229–230
    • considerations, 251
    • overview, 198–199
    • planning, 232–233
  • monolithic nature of, 248–250
  • partnerships for migrating from, 234–236, 238–241, 251–252
  • updating, 253–257
• LegalTech, 33, 35–36, 308
• Lehman Brothers, 27, 31, 41, 43
• lending apps, 14, 154–155
• Level39, 308
• LexisNexis, 220
• Libra, 29, 43
• licensing. See software license models (SLMs)
• limited partners (LPs), 285, 287
• Linux Foundation Collaborative Project, 195

M

• machine learning (ML). See also artificial intelligence (AI)
  • analysis of unstructured data, 164
  • for analyzing investments, 291
  • banking apps and, 153
  • capital markets trading apps and, 156–157
  • developing skills for, 300–301
  • discussion, 32–33, 216, 305–306
  • disruptions caused by, 35
  • in Fintech Cube, 11
  • open source software and, 183
  • opportunities presented by, 37
  • reinforcement learning in, 217
  • supervised learning, 216–217
  • unsupervised learning in, 217
• macOS, 158
• management information system (MIS), 164
• Markets in Financial Instruments Directive (MiFID II), 39
• MBaas (Mobile Backend as a Service), 113, 117
• MBS (mortgage backed securities), 9
• McCarthy, John, 212
• mergers, 308–309
• Merrill Lynch, 8, 154
• microservices
  • advantages of, 75–77, 258–259
  • challenges of, 77
  • composability of, 79, 258–259
  • continuous integration of, 78–79, 258
  • development teams, 80
  • discussion, 19, 60–61, 71, 74
  • legacy systems and, 248–249, 253, 258–260
  • open source software and, 183
  • prototyping and, 355
  • qualities of, 74–75
• Microsoft, 28–29, 33
• Microsoft Azure, 27, 112
• Microsoft Hyper-V, 127
• Microsoft Project 2019 for Dummies (Dionisio), 269
• MiFID II (Markets in Financial Instruments Directive), 39
• minimum viable product (MVP), 160, 234
• mining, 96, 101, 136
• MIS (management information system), 164
• ML. See machine learning
• MLAT (mutual legal assistance treaty), 133
• mobile apps, 152
• Mobile Backend as a Service (MBaas), 113, 117
• mobile devices, 151–152
• mobile technologies, 24, 32
• mobile website, 24
• MoneyLion, 154
• Monte Carlo simulations, 8, 50, 88
• mortgage backed securities (MBS), 9
• multi-asset class pricing, 25, 27, 31
• Musk, Elon, 213
• mutual legal assistance treaty (MLAT), 133
• MVP (minimum viable product), 160, 234

N

• NASA Astrobiology Institute for Universal Biology, 8
• national competent authorities (NCAs), 48
• National Vulnerability Database (NVD), 341
• native apps, 152
• natural language processing (NLP), 164, 212–213, 218
• NCAs (national competent authorities), 48
• neo-banks, 11
• Netherlands, 47
• NLP (natural language processing), 164, 212–213, 218
• non-copyleft licensing, 178, 348–349
• NoSQL database, 204, 207, 210
• Numerix
  • acquisitions, 245, 266–267, 276–277
  • adoption of software license models (SLM), 12, 14
  • Bloomberg partnership, 41
  • capital markets dominance, 271–272
  • capital markets trading apps, 157
  • company responsiveness to industry, 232
  • Counterparty Risk software, 50
  • CrossAsset Software, 9, 19, 47
  • CrossAsset XL software, 50
  • difficulties faced by, 8
  • Engine software, 17
  • FinTech culture of, 240
  • five tenets of operation, 9
  • Lehman Brothers bankruptcy and, 27, 31, 41, 43
  • Library software, 17
  • LiquidAsset software, 50
  • multi-asset class pricing, 25, 27, 31
  • Oneview software, 25, 267, 276–277
  • pivot toward risk management, 47, 50, 54–55, 227
  • Portfolio Products, 50
  • pricing analytics, 19–20
  • product refocusing, 17–18
  • provision of Risk Greeks, 25
  • revenue-increase initiatives, 237
  • software provided by, 8
  • Toolkit software, 17
  • wwww.numerix.com, 157
• Nutmeg, 154
• NVD (National Vulnerability Database), 341

O

• OCP (Open Compute Project), 347
• Office of the Comptroller of the Currency (OCC), 46
• O'Hanlon, Steve, 9, 12, 14, 17, 19, 25, 27, 31, 43, 47, 54, 227, 232, 237, 240, 267, 271
• OLAP (online analytical processing), 165–166
• online analytical processing (OLAP), 165–166
• open banking, 14, 51
• Open Compute Project (OCP), 347
• open innovation, 196
• open source software
  • advantages of, 181–185, 331, 338
  • as alternative for buying, 331–332
  • application programming interfaces (APIs) and, 183
  • community, 176–177, 338–339
  • considerations when using
    • business model, 338
    • code audits, 341–342
    • hardware impact, 346–347
    • hidden costs, 343–345
    • legal considerations, 347–349
    • open source community health, 338–339
    • overview, 337
    • reliability, 342–343
    • security, 340–341
    • tech support, 339–340
    • updates and upgrades, 345–346
  • development process, 179–181, 185
  • disadvantages of
    • code quality, 190, 341–342
    • documentation, 183, 186–188
    • licensing, 191
    • maintenance, 187
    • marketplace sustainability, 191
    • other issues, 192
    • security, 188–190
    • support, 186–187
    • testing methodologies, 183
  • discussion, 175–176
  • factors driving rise of, 175–176
  • free software versus, 177–178
  • freeware versus, 178–179
  • implementing, 192–195
  • licensing, 177–178, 191–193, 244–246
  • maintenance costs of, 344
  • microservices and, 183
  • organizations, 195–196
  • questions to ask regarding, 332
  • setup costs of, 344
  • shareware versus, 179
• open source technologies
  • decentralized applications (DApps), 95–96
  • discussion, 19, 90
  • for GUI framework creation, 159
• Open Virtualization Alliance (OVA), 195
• OpenPOWER Foundation, 196
• OpenStack, 195
• operating system virtualization, 126
• Oracle VM VirtualBox, 127
• OracleVM, 127
• OVA (Open Virtualization Alliance), 195

P

• P2P. See peer-to-peer
• PaaS (Platform as a Service), 115–116, 128
• Palantir Technologies, 156
• Parallels, 127
• partnerships, for updating legacy systems
  • evolving solutions, 239–241
  • overview, 234–235, 251–252
  • pros and cons, 236
  • research, 238–239
• passporting, 40, 47
• payment platforms, 28–29, 36
• payment service providers (PSPs), 51–52
• Payment Services Directives, 51
• Payment Systems Regulator (PSR), 49
• PayPal, 28, 101
• PayTech, 32, 36, 281, 308
• PD (private data), 122
• PE (private equity), 279–280, 287–288
• peer-to-peer (P2P)
  • blockchain and, 99–100
  • discussion, 95
  • e-payment apps, 154
  • in Fintech Cube, 10–11
• permissioned blockchains
  • consensus protocol in, 103, 141
  • discussion, 103–104, 135
• permissive licensing, 178
• perpetual license model (PLM), 12, 242–243
• personal identifiable information (PII), 123, 132, 221
• PII (personal identifiable information), 123, 132, 221
• Pitchbook, 15–16
• Platform as a Service (PaaS), 115–116, 128
• platform-based business, 10–11
• PLM (perpetual license model), 12, 242–243
• PoA (proof of activity) consensus, 137
• POPI (Protection of Personal Information) Act, 132
• PoS (proof of stake) consensus, 96, 137–138
• PoW (proof of work) consensus, 96, 137–138
• PRA (Prudential Regulatory Authority), 49
• predictive data models, 167
• Price Waterhouse, 8
• pricing analytics, 14, 19–20
• privacy, 23, 37
• private banking, 11
• private blockchain, 140, 143
• private data (PD), 122
• private equity (PE), 279–280, 287–288
• procedural software, 67–69
• programming languages. See also specific programming languages
  • discussion, 70–71
  • need for new, 88–89
  • older, 253, 323
  • principle, 89–92
• proof of activity (PoA) consensus, 137
• proof of stake (PoS) consensus, 96, 137–138
• proof of work (PoW) consensus, 96, 137–138
• Protection of Personal Information (POPI) Act, 132
• prototyping, 354–355
• Prudential Regulatory Authority (PRA), 49
• PSPs (payment service providers), 51–52
• PSR (Payment Systems Regulator), 49
• Pub (public data), 122
• public blockchain, 140, 143
• public data (Pub), 122
• public trust, 22
• Python programming language
  • discussion, 70, 89–91
  • GUI framework creation and, 159

Q

• Quandl, 220
• quantum computing
  • advantages, 107–108
  • discussion, 104, 212
  • drawbacks of, 106–107
  • in Fintech Cube, 11
  • functioning, 105–106

R

• R programming language, 89, 92
• Raisin, 154
• RAML (Restful API Modeling Language), 65, 79
• rapid application development (RAD), 71, 73, 77–78, 89
• RD (restricted data), 122
• real-time data, 76, 110, 156, 205–206
• recurring software subscription model, 9
• Red Hat Enterprise Virtualization, 127
• RegTech
  • apps, 155–156
  • discussion, 9, 32, 35, 39, 134
  • GDPR compliance and, 55–56
  • online courses on, 308
• regulations. See also General Data Protection Regulation (GDPR)
  • 2008 financial crisis and reforms in, 39
  • anti-money laundering (AML), 42
  • balancing act in, 40
  • of BigTech companies, 42–44
  • of BigTech participation, 30–31
  • blockchain and compliance with, 145–146
  • compliance challenges, 56, 330
  • cybersecurity, 42
  • data privacy, 42
  • of FinTech companies, 296, 298
  • information sharing in, 42
  • know your client (KYC), 42
  • by location, 45–49, 51–52, 132
  • need for increased, 42
  • of online wealth management industry, 45–46
  • overview, 39
  • passporting and, 40
  • of payment service providers (PSPs), 51–52
  • RegTech and compliance with, 155–156
  • risk governance, 42
  • in United Kingdom, 40
  • vendor risk issues and, 44
• regulatory change management, 56
• regulatory sandboxes, 40, 42
• reinforcement learning, 212, 217
• relational databases. See also structured query language (SQL)
  • common, 204, 209
  • discussion, 83, 170, 209–210
  • historization in, 206–207
• REpresentational State Transfer (REST) API, 65, 74, 79
• Restful API Modeling Language (RAML), 65, 79
• restricted data (RD), 122
• retail banking, 11
• retina scans, 303–304
• return on investment (ROI), 282–283, 297
• right to information (RTI) laws, 133
• Ripple, 36
• risk governance, 42
• Risk Greeks, 25
• risk management, 27
• Robinhood, 154
• robo-advisers
  • discussion, 15, 32, 34, 153
  • regulation of, 45–46
• robots, 213
• ROI (return on investment), 282–283, 297
• RTI (right to information) laws, 133

S

• SaaS. See Software as a Service
• SC (serverless computing), 113, 117
• SCA (Software Composition Analysis), 349
• scalability, 125
• Schwab, 154
• SD (sensitive data), 122
• Secure Sockets Layer (SSL), 124
• security
  • blockchain, 142–146
  • cloud computing and, 111–112, 121–122
  • cloud service providers and, 129–130
  • data vulnerability and, 124–125
  • Platform as a Service (PaaS) and, 116
  • Software as a Service (SaaS) and, 115
  • in traditional database structures, 97
• self-service provisioning, 127–128
• semi-structured data, 209
• Senior Managers and Certification Regime (SMCR), 44
• sensitive data (SD), 122
• server virtualization, 126
• serverless computing (SC), 113, 117
• service level agreements (SLAs), 111
• Service Organization Controls (SOC), 130
• shareware, 179
• Silicon Alley, 26
• Silicon Roundabout, 26
• Silicon Valley, 25–26
• siloing, 277–278, 299, 301
• Simple, 153
• Singapore, 45
• SLAs (service level agreements), 111
• SLMs. See software license models
• small-to-medium enterprise, 155
• smart contracts, 138–139, 146–147
• smart hardware, 146–147
• SMCR (Senior Managers and Certification Regime), 44
• SMEs (subject matter experts), 59, 90, 133–134, 184
• SOC (Service Organization Controls), 130
• social media, 13–14
• SoFi, 155
• soft forking, 144–145
• Software as a Service (SaaS)
  • advantages of, 241, 330
  • banking and, 33
  • discussion, 14, 60–61, 114–115
  • disruptions caused by, 35
  • self-service provisioning in, 128
• Software Composition Analysis (SCA), 349
• software license models (SLMs)
  • cost considerations, 329
  • discussion, 12, 14, 241
  • source code access and, 243–244
  • types of, 241–246
• software providers, 14
• Sokol, Alexander, 8
• Sowood, 24–25
• SQL (structured query language), 164, 199–200, 209–210. See also relational databases
• Square, 154
• SSAE16 (Statement on Standards for Attestation Engagements), 130
• SSL (Secure Sockets Layer), 124
• Stallman, Richard, 177
• Startupbootcamp FinTech, 306–307
• Stash Wealth, 154
• Statement on Standards for Attestation Engagements (SSAE16), 130
• storage virtualization, 126
• Stripe, 154
• structured data, 164, 208–209
• structured query language (SQL), 164, 199–200, 209–210. See also relational databases
• subject matter experts (SMEs), 59, 90, 133–134, 184
• subprime mortgage failures, 24–25, 27
• subscription license model (SLM), 12, 241–242
• supernodes, 137
• supervised learning, 216
• surveillance, 56
• Sybil attack, 141

T

• TD Ameritrade, 154
• TechFin, 24
• technology, 10–11, 13
• Techstars, 307
• Tencent, 26
• term agreements, 243
• term license model (TLM), 12
• term software pricing model, 9
• TFG Financial Systems, 245, 276–277
• TLM (term license model), 12
• TLS (Transport Layer Security), 124
• “too big to fail” culture, 39, 42
• Toronto Dominion, 19
• trading, 11
• traditional banking
  • advancing technology and, 24
  • disadvantages faced by, 22
  • discussion, 14
• traditional institutions
  • advantages enjoyed by, 23
  • BigTech and, 29–30
  • disadvantages faced by, 23–24
• transaction banking/payments, 11
• TransferWise, 51, 154
• transformation projects
  • communication during, 264
  • data-driven decision-making (DDDM) and, 275–276
  • discussion, 261–262
  • failures in, 268–269
  • FinTech partnerships for, 262, 268
  • personnel
    • breaking down siloing, 277–278
    • considerations, 262–263
    • leadership paradigms, 264–265
    • recruitment, 263–264
    • retaining, 272–275
    • role assignments, 265–266
  • requirements document for, 269
  • return on investment (ROI), 297
  • setting expectations during, 268–269
  • statement of work (SOW) for, 269
  • supporting change agents during, 264, 269
• “Transforming Paradigms” (University of Cambridge), 213
• Transport Layer Security (TLS), 124
• tree finite methods, 8
• Trulioo, 156
• Turing, Alan, 217–218
• Turing complete code, 138

U

• unified modeling language (UML) diagram, 160
• United Kingdom
  • FinTech in, 26
  • regulation in, 40, 45, 47–49
• United States
  • FinTech in, 26
  • regulation in, 39, 45–46, 132
• unstructured data, 164, 209
• unsupervised learning, 217
• Upgrade, 155
• user experience (UX), 157–158

V

• value-at-risk calculations, 8
• Varo Money, 153
• vendor risk issues, 39, 44
• Venmo, 154
• venture capital (VC)
  • discussion, 2, 13–14, 356–357
  • investments, 279–280, 285–287
• virtual machine monitor (VMM), 126
• virtual private network (VPN), 124
• virtual reality, 11
• virtualization
  • cloud computing and, 126–127
  • discussion, 95, 114–115
  • Function as a Service (FaaS) and, 117
• Visual Basic programming language, 70
• VMM (virtual machine monitor), 126
• VMware Fusion, 127
• VMware Server, 127
• VMware Workstation, 127
• voice technology, 211, 303–305
• VPN (virtual private network), 124

W

• waterfall development, 71–73
• wealth management industry, 21, 45–46, 153
• WealthTech
  • challenges by, 281
  • discussion, 9, 13, 32, 280
  • online courses on, 308
  • portfolio self-management in, 34
• web apps, 152
• WeChat, 14
• Whitten, Greg, 9
• Windows, 158
• Woerner, Stefan, 107–108
• workflow engine, 160
• WorldRemit, 51

X

• XML (Extensible Markup Language), 200
• XVA, 27, 54

Z

• Zopa, 155