Aave
As part of decentralized finance (→ DeFi), this → cryptocurrency is used to coordinate transactions on a decentralized lending marketplace. Aave allows to make loans (→ crowdlending) and to form lending pools with various other → cryptocurrencies (e.g., → ETH, → DAI), as well as to conduct asset (→ digital asset) and collateral exchanges. Aave’s → liquidity → token is based on → Ethereum’s → ERC-20 token and has also been used to establish other decentralized exchanges (→ DEX) such as → Uniswap.

Accelerator
Describes a initiative that comprises several stages that support or “accelerate” → startup companies over several months in their early business development. Similar to → regulatory sandboxes, selected → startup companies receive benefits from an organization known as accelerator or → incubator. Among the examples are the provision of premises (→ co-working spaces), technical equipment, or access to networks and coaching seminars. In return, accelerators receive future profit sharing, pre-emption rights or minority shares from the → startup companies they have supported.

Access-to-Account (XSTA, XS2A)
Third-party (→ TTP) access to payment services that has become a → business model of many → fintech companies. It is used, for example, to process financial transactions and to analyze customer-specific transaction data. In most cases this also refers to access to customer accounts, which banks need to grant third parties in connection with → PSD2. From a technological side, the European Banking Authority (→ EBA) is responsible for the development of standards (so-called regulatory technical standards, RTS) regarding security, authentication and communication. In the → four-corner model, this gives rise to account information service providers (→ AISP) and payment initiation service providers (→ PISP), who have direct access to transacting parties (see Fig. 1). These service providers penetrate the previously closed system of banks and thus open the way to → multi-bank services.

Account Information Service (AIS)
Account information services, as defined in the EU’s second payment services directive (→ PSD2), provide users with an overview of their financial situation. They consolidate information from one or more → payment service user accounts (→ PSP). AIS providers (→ AISP) are mainly → fintech companies, but also primary or house banks. With the consent of the account holder, it is possible to combine accounts of different banks on the → online banking platform or on personal finance management solutions (→ PFM, → multi-bank).

Account Information Service Provider (AISP)
Providers of account information services (→ AIS) yield access to payment accounts (→ XSTA) and are involved in the execution of → electronic payments as defined by the EC’s Payment Services Directive (→ PSD2).

Acquiring Bank/Acquirer
In the → four-corner model, the acquiring bank enables the merchant to accept card payments at the point of sale (→ PoS). Acquirers act as financial service providers in the credit card environment, offering acquiring services to retail companies on behalf of the credit card companies. Based on cooperation agreements with card providers (→ EMV), acquirers ensure the acceptance and processing of transactions on behalf of the merchant by forwarding commercial and technical data to the participating banks or financial institutions and the card providers. They establish a connection to their bank (→ issuer) and authorize the payment. If the acquirer and → issuer are the same institution, these are so-called → on-us transactions. Besides supporting the accepting activities within card-related transactions, they often also provide the necessary hardware equipment and software at the merchant’s → POS. Acquirers may also act as → integrators and offer other payment methods (e.g., invoice, PayPal, → cryptocurrency). In the past, the scale-elastic market of acquirers has experienced a strong concentration in recent years, resulting in two dominant companies, the Danish Nets Group and the French Worldline.

Advanced Encryption Standard (AES)
With symmetric encryption methods (→ cryptography) such as AES, the keys used for encryption and decryption are identical in contrast to → asymmetric encryption methods (→ RSA). AES encrypts and decrypts data block by block using a key whose length can be 128 (AES-128), 192 (AES-192), or 256 (AES-256) bits. Compared to the previously used data encryption standard (DES), which is limited to 56 bits, AES offers higher security and is therefore often applied in the financial industry (e.g., → MAC).

Agent
In the banking context, this term is used for representatives or agents of financial service providers (e.g., a clearing agent for → CSMs, a paying agent for imprest accounts, or a bank agent for banks in general). In a technological sense, the term also refers to intelligent software agents, which denote software with some form of autonomous activity. For example, → chatbots with artificial intelligence (→ AI) functionalities support customer advice, and agents are also used on → electronic markets to identify and compare offers and to negotiate with providers (e.g., regarding price, delivery terms, payment conditions).

Aggregator
Intermediary → business model (→ intermediation) based on the bundling of services, which is present in many → fintech solutions. Similar to the → broker model, an aggregator mediates between providers of competing and/or complementary services on the one hand and clients on the other. In contrast to brokers, aggregators take possession of the transacted offerings and they are also responsible for setting prices. The aggregator model is found with → e-commerce platforms such as Amazon or banks that provide services of cooperating insurance companies (→ bancassurance). If these digital services are also linked to digitized physical resources (→ smart product), such as → electronic payment services for vehicle parking services, then the term → smart service or → mobility service is also used.

Agile Organization
An agile organization features a high degree of maturity regarding → digitalization as well as transformation management and is a core characteristic of → startup or → fintech companies. → Agility should increase the ability to react on changing conditions in the business environment, as they result from new customer demand, new competitors or new technologies. The aim is being able to quickly develop measures that contribute to → customer experiences, improved business processes or → business models on the one hand, and to establish agile change structures and processes on the other hand. The latter should sustain explorative approaches, fast learning processes as well as self-control and organization in the sense of an “agile mindset”.

Agility
Known from the literature on lean management, agility has spread to other domains, in particular software development. It denotes a procedure in development projects, which aims at attaining results iteratively in smaller chunks. Agile principles have established themselves especially in the development of → IT applications (e.g., → front-end- and → back-end applications) and is present in concepts such as → Scrum or Kanban. As shown in Fig. 2, the agile approach conceives costs and time as stable and adapts project goals and quality accordingly. In contrast, classical development approaches like the waterfall procedure consider goals to be stable and are often linked with exceeding cost and time budgets. This has often been the case with the introduction of complex → core banking systems. → Fintech businesses have typically adopted agile development methods, but had the advantage of a “greenfield approach”, i.e., non-existing legacy structures and systems (→ legacy system).

AI Application Areas
As a general purpose technology, artificial intelligence (→ AI) may be applied to many use cases. To structure potential application areas in the financial industry, a periodic table known from chemistry has been suggested (see Fig. 3). The x-axis shows the abilities that determine intelligent behavior (→ AI) and the y-axis shows the complexity levels. It starts with applications that support the visual or acoustic perception of the world, e.g., for the recognition of images, speech or faces. In the case of visual perception, neuronal networks store characteristic features, such as eyes, nose and mouth, in order to recognize them as faces in other images. An example from the financial sector is the know-your-customer process (→ KYC), which supports the identification of “high risk individuals”. Areas of application with the functional scope of processing focus on the conclusive processing of the perceptions. Natural language processing (→ NLP) allows to recognize words with their meaning and, if the system is specified accordingly, also the mood of the text from the different word combinations, which might be valuable in oral or written conversations (→ sentiment analysis) with customers. Action comprises applications that involve actions to achieve specific goals. The simplest field is “control”, which refers to the control of software or hardware depending on defined measured variables. These could be automated → AI systems for financial investments, which maximize the value of investment portfolios in predefined asset classes. If the → AI system decides to buy or sell a security based on the preferences defined by the investor and the current market situation, it executes this transaction on an → electronic exchange and adjusts the portfolio accordingly (→ robo advisory). In contrast, the field of application collaborative robotics is more complex, since (physical) → robots are ultimately supposed to independently conduct actions that make sense in a given situation. An example is the (meanwhile discontinued) humanoid → robot Pepper, developed by the Japanese technology company SoftBank Robotics for customer advisory services in bank branches. This robot was programmed to pre-select the products relevant to the customer while the customer was waiting for a conversation with the (real) customer advisor. The learning cluster enhances the areas of perception, processing and action. Intelligent agents may generate new knowledge about their environment during perception and processing as well as during the execution of actions. Category learning, for example, aims to learn combinations of characteristic values and thus to categorize them. Pepper, for instance, could categorize faces according to their mood (e.g., does the new visitor in a bank branch appear safe or unsafe because of his behavior?) and learn new subcategories from the category of “unsafe visitors” (e.g., visitors who appear to be looking for something and visitors who Pepper would classify as potential bank robbers). The application area of relationship learning goes beyond learning categories, in that the → robot learns reactions to the respective category and assigns them to the categories (e.g., whether the customer is waiting for his appointment and might be happy about a first interaction with the expert robot). The → robot could identify the faces of customers and analyze their social media contacts to determine other friends of the customer who have also recently visited the store. Customers in a corresponding network could receive individual benefits for referring friends. These responses could be supplemented by other interaction possibilities (e.g., Pepper could learn that he only has to show some visitors the way to the water dispenser if they are categorized to look uncomfortable in the summer), whereby the usefulness of such scenarios or their cost-benefit-risk ratio requires critical validation in each case.

Airdrop
In analogy to helicopter money, Airdrop refers to give away → coins of existing or new → cryptocurrencies for free. This may occur to retain active customers, to acquire new customers and, in the course of marketing campaigns, upon the introduction of new → cryptocurrencies or after a → fork.

Algorithm
Describes a procedure or a regulation for solving complex problems or a class of problems, which are sufficiently formalized to be processed by an artificial system. Algorithms are mathematical calculation rules or logical rules anchored in program code that determine the calculation and/or evaluation of data (e.g., if-then conditions). Algorithms are often present in solutions based on artificial intelligence (→ AI) and allow a high degree of automation in standardized or low-complexity financial processes, which are often typical for → fintech solutions. The application of algorithms in a specific setting is referred to as → algorithmization.

Algorithmic Banking/Algo Banking
Use of artificial intelligence methods (→ AI) to support or execute banking processes. This includes, for example, → robo advisory or the credit assessment of an applicant with the help of → algorithms and social media activities (→ social data). In some cases, no historical data of the applicant is necessary for the decision-making process.

Algorithmic Trading/Algo Trading
A method of → AI-based → agents that have been used in the banking sector since the → digitalization of stock exchanges (→ electronic exchange) from the 1980s onwards. Automated trading systems manage larger portfolios and place orders depending on rules and price developments on the exchange. Algorithmic trading systems lead to high frequency trading (→ HFT) when they place buy and sell orders in quick succession to realize (small) arbitrage.

Algorithmization
Describes the use of → algorithms in business processes. As shown in Fig. 4, existing processes are supported by information technology (→ IT) and deliver data that is subject to analytics (→ business analytics). The information gained on this basis yields the ability to adapt or learn independently and improve decisions. If, for example, an “intelligent” vehicle is involved in an accident, it can transfer the previously acquired data (n in Fig. 4) to the manufacturer’s network and suggest to other vehicles that they should brake earlier in this situation in future. This would lead to new insights and data, i.e., n+1 in Fig. 4, which in turn are labeled to make them amenable for machine processing. Similarly, → algorithms are used in credit allocation or complaint management processes.

Alipay
Chinese provider of a → mobile payment system, who has experienced numerous expansions from a pure online → payment system to become a dominant → mobile payment system. Founded in 2004, Alipay meanwhile comprises a large user base of over one billion active users and over 80 million merchants, making it the largest → mobile payment system worldwide. Since 2014, the service is part of the Chinese Ant Group, a subsidiary of the Alibaba Group, which still holds a 30% stake in the company. Alipay features a → mobile wallet and is used on → e-commerce platforms (especially those of Alibaba such as Taobao and TMall) as well as for car sharing, food or → mobility services. Payments are supported by credit card, direct debit or → P2P. If this is done between Alipay users, no classical financial service provider is required. In addition, Alipay acts as an intermediary (→ intermediation) to providers of insurances as well as investment and credit products (→ microfinance), which consumers may use to finance the goods purchased via numerous platforms (→ ecommerce). In addition to the large user base, the procedure for assessing creditworthiness based on artificial intelligence (→ AI) and → big data is considered a competitive advantage. A main competitor of Alipay is → WeChat Pay of the Chinese Tencent Group.

Altcoin
Alternative → coins are → alternative currencies in the → cryptocurrency environment, which often originate from splits (→ blockchain split) of existing → cryptocurrencies like → Bitcoin. Although these new → cryptocurrencies differ from “original currencies” like → Bitcoin, they typically feature many similarities regarding the → mining algorithm or the → wallet functionality. Well-known altcoins are → Bitcoin Cash, → Eos, → Iota, → Monero or → Xrp. One of the main motivations for the emergence of altcoins are higher levels of privacy and anonymity in → electronic payments based on → cryptocurrencies, for which the term → privacy coins has emerged. For example, Altcoins derive from → Bitcoins may be generated (“mined”), stored in virtual → wallets and traded via corresponding exchanges (→ crypto exchange), much like → Bitcoin.

Alternative Credit Scoring
Refers to the use of publicly available consumer information to create reliable risk profiles of credit applicants. While traditional credit scoring is based on data from payment history, current debt, the length of credit history or the credit utilization ratio, alternative approaches also include additional data sources. Among the examples is Fico, which draws personal data from social networks (→ social data) or online profiles, transaction-related data from past purchases, outstanding payments, cell phone payments, income or bank information. It enables customers without bank accounts to qualify for loans.

Alternative Currency
Currencies that are neither issued nor regulated by national banks of individual countries and are not recognized as official means of payment. The latter might change with → cryptocurrencies that are officially accepted (e.g., → Bitcoin in El Salvador) or launched by official institutions, in particular, by central banks (→ CBDC). Among the examples for alternative or complementary currencies are → Ethereum Coins, → Ripple Coins and → Bitcoins, but also currencies that are present in regional communities. At least many of the electronic alternative currencies (e.g., → Bitcoin, → Diem, → Ether) are expected to face increasing regulation.

Alternative Financing
While traditional financing opportunities include banks, stock exchanges or over-the-counter (→ OTC) capital markets, alternative financing instruments have emerged for specific purposes. For example, these focus on projects or transactions with a lower capital volume for financing innovative or creative projects, which often comprise a large number of small investors. Well-known forms of this segment of → fintech services are → crowdfunding and → crowdinvesting.

Alternative Lending
With → digitalization, → digital platforms have emerged as alternatives to traditional forms of financing that bring together lenders and donors. This is particularly relevant for small and medium-sized enterprises (→ SME), people with low incomes (e.g., student loans) or lenders in emerging markets without access to the banking system. Alternative lending may be divided into the segments → crowdlending (for companies) and → P2P lending (for private persons).

Alternative Trading Platform (ATP)
Similar to electronic stock exchanges (→ electronic exchange), alternative trading platforms bring together buyers and sellers of securities or assets to settle transactions. As privately operated markets, they define the trading and price-setting rules and are often less democratic in yielding transparency on the market action for all parties alike. Although they are less regulated than official exchanges, they are still subject to national regulatory authorities, such as the SEC (U.S. Securities and Exchange Commission) in the case of alternative securities platforms in the US. While in the US the term alternative trading system (ATS) is often used synonymously for ATP, it is multilateral trading facility (→ MTF) in Europe. Among the established ATPs are the systems of large banks (e.g., Credit Suisse, Deutsche Bank, J.P. Morgan, Goldman Sachs or UBS) but also those of service providers such as Dealerweb or Instinet. Similarly, the more recent → cryptocurrency trading platforms (→ crypto exchange) are considered ATPs, which still await regulation.

Ambient Intelligence
Similar to → pervasive computing, environmental intelligence assumes a widespread or ubiquitous presence of information technology (→ IT). This includes mobile technologies and → wearables as well as small computing devices in physical objects (→ IoT), which are present in various assistance scenarios (e.g., assisted living, personal trainer, smart home) or sales scenarios (e.g., personalized advertising in bank branches). Such scenarios often include digital services (→ smart service), which are charged on a per-usage basis (→ PAYU).

Anti-Money Laundering (AML)
The prevention of money laundering is a key activity in financial systems and comprises checks on deposits or transfers of sums of money above a certain amount. The goal is to determine the origin of the assets as well as the identification of the owners of the assets (→ KYC). Regulations are enacted by the European Union (EU) as well as by individual countries. In Germany, for example, the money laundering act (GwG) implements the EU’s money laundering directive. According to this directive, cash payments are limited to an amount of 2000 € since 2020 without being subject to a money laundering audit and cross-border cash amounts in excess of 10,000 € require a declaration within the EU. With the amendment of the fourth EU money laundering directive (Directive EU 2018/843), the regulations also apply to → crypto custody transactions, which makes → digital currencies such as → Bitcoin also subject to money laundering checks. Financial service providers have traditionally implemented such → compliance checks in their internal → application systems, but external service providers with service offerings in this → regtech segment (e.g., the Estonian provider Salv) have recently emerged.

API Banking
Describes the use of application interfaces (→ API) for coupling digital services among service providers in and with the financial sector. It aims at connecting modular → application systems is based on the concepts of → open banking and → platform banking to enable efficient networking and → sourcing strategies among businesses in the financial industry. In a broader sense, API banking allows the connection of actors in the primary value creation process (i.e., an automotive manufacturer like in Fig. 5) with providers in the secondary value creation process (i.e., financial service providers like the → PSP in Fig. 5) in the sense of embedded finance (→ EFI). Figure 5 shows the example of an automobile manufacturer, which links various → payment services to a platform via → API to process master and transaction data. It can also be interpreted as a form of → outsourcing of financial functions of the car manufacturer.

App Economy
Generic term to denote use of → applications (“apps”) within the economic environment. It includes the orientation and construction of these → ecosystems, the → business models within these → ecosystems as well as the value creation logic (e.g., → network effect). Since apps use → digital platforms such as → app stores for distribution, the term “platform economy” (→ platform) is often used in a similar sense.

App Store
Short form for a digital distribution platform for application software or → applications (“apps”). Typically, an application or app store comprises a → digital platform with an electronic catalog, which includes a large number of apps from various providers that may be searched, ordered and downloaded by users on their devices. Since app stores bring together many providers and many users, they may be regarded as → digital marketplaces. In the financial industry, the app store principle is increasingly present in the concepts of → core banking systems that follow the idea of → open banking.

Application (App)
The abbreviation “app” for application has established itself as a term for application software (→ application system) in the domain of desktop computers and mobile devices. The latter, i.e., smartphones or tablets, also use the term mobile apps, which are often limited in their range of functions and are related to or individualized for the respective user. If the functional logic of the application is mainly located on the server (instead as on the client device), it is referred to as a web application.

Application-Specific Integrated Circuit (ASIC)
Application-specific circuits is computing hardware that comprises a set of instructions that is specific for certain application purposes (e.g., graphics, file handling, computing operations). Compared to central processing units (CPUs) that may serve a broad range of application purposes, ASICs are less universally applicable, but they are more powerful in their field and tend to be more cost-effective by omitting functions that are not required for them. The ASIC approach dates back to the concept of reduced instruction set computers (RISC) from the 1960s and has recently gained importance with the spread of → artificial intelligence (e.g., for → deep learning) and cryptographic calculations (e.g., for → consensus mechanisms such as → PoW). In particular, → miners and → mining pools rely on ASIC hardware, while alternative → consensus mechanisms such as → proof-of-stake have emerged that claim to be ASIC-resistant.

Application Programming Interface (API)
Denotes an interface of an application software (→ application system), which comprises the specification of an input and an output. It allows application programs to exchange data directly or without media discontinuities (→ media break) and are the basis for → real-time processing in distributed environments. In contrast to a data interface, which yields access to a database, APIs are function interfaces where the input consist of commands with corresponding parameters that is needed to trigger functions in the target application. The concept has long been known in the modularization and integration of → application systems, as it reduces the complexity of large (so-called monolithic) systems on the one hand and creates the possibility of flexibly integrating specialized systems inside and outside the company on the other. In the financial industry, APIs are key in the current developments towards → API banking, → banking-as-a-service, → platform banking or → open banking. The connectivity that is embedded in these initiatives is also driven by regulations such as → PSD2, which requires banks to offer standardized APIs for accessing accounts on a non-discriminatory basis. Thus, they enable financial service providers such as → fintech companies to access banking systems to read transaction data and to derive their own services (e.g., → multi-bank functionalities) on this basis. In principle, all market participants may offer APIs, but in the financial sector approval is often required regulating authorities (e.g., → BaFin in Germany). An example of an API implemented using the → REST technology is shown in Fig. 6 for querying an exchange rate of 100 USD in Euros at the → neo- or → smartphone bank Revolut. It contains the function call (request/input) and the individual transferred data fields with their descriptions. The feedback (response/output) is displayed in → JSON format.

Application System
Part of an information system (IS) consisting of a hardware and a software system (see Fig. 7). The latter comprises the basic software such as the → operating system and the utilities (e.g., compilers, device drivers) as well as the application software, which contains functionalities for various application purposes. These refer to general functions such as e-mail or web browsers as well as industry- or sector-specific application functions, such as → core banking systems. The aim of business application systems is to achieve high degrees of business process automation and integration between an organization’s functional areas. For example, this is the case when securities are purchased electronically at → electronic exchanges via digital brokerage systems or payments are effects without manual intervention (→ electronic payment). Although information systems may be seen as application systems with strong user interaction, the terms information system, → application and application are often used synonymously.

Architecture 4.0
Based on the → Industry 4.0 concept, architecture 4.0 comprises the design of → application systems that are modular and include → real-time processing capabilities via → APIs. This allows for flexible → sourcing strategies as well as the integration of innovative → fintech solutions (e.g., → blockchain frameworks) and → cloud computing functionalities. Examples are → core banking systems that rely on → API banking and → banking-as-a-service.

Artifact
In software development, an artifact refers to tangible results or byproducts of the development process (e.g., documentation, source code, models). In the → blockchain context, it refers to → digital assets that are managed and transferred in → application systems.

Artificial Intelligence (AI)
Generic term for technologies that emulate intelligent behavior to solve complex tasks with a certain degree of autonomy and adaptability. In the → fintech sector, AI is present in application areas (→ AI application areas) such as → robo advisory, → algorithmic trading or fraud detection (e.g., → AML). Examples for underlying technologies are → machine learning (e.g., → deep learning) and → NLP, which often rely on → big data.

Asymmetric Encryption
Unlike → symmetric encryption (e.g., → AES), asymmetric encryption uses key pairs consisting of a → public key and a → private key (e.g., → RSA). In the → fintech context, this is often used to secure → electronic payments or → blockchain transactions (e.g., → Bitcoin) and relies on technologies such as → ECDSA.

Atomic Swap
A → cryptocurrency transaction technology that allows the direct exchange of different → cryptocurrencies (e.g., → Bitcoin and → Ether) across → blockchains without a trusted intermediary (→ disintermediation). It typically relies on → hashed timelock contracts (→ HTLC) to ensure that either both parties fulfill the transaction or it is canceled.

Augmented Reality (AR)
Technology that enhances real-world environments with digital information (e.g., graphics, sounds) and is often linked to → wearables or mobile devices in → fintech scenarios. Examples are AR-based customer advisory in bank branches or visualizations in → robo advisory.

Authentication
Procedure to verify the identity of a user or system, which is essential in → fintech for securing → electronic payments or accessing → online banking. It includes methods such as passwords, → biometrics, → 2FA or → OTP and is a key element in regulations like → PSD2.

Authorization
Process of granting permissions to access resources or perform transactions after successful → authentication. In → fintech, this applies to approving → electronic payments or accessing account data (e.g., via → AISP in the → four-corner model).

Automated Clearing House (ACH)
An electronic network for processing financial transactions, primarily used for batch processing of → electronic payments like direct deposits or bill payments. In the US, it contrasts with → RTGS systems (e.g., → Fedwire) and is similar to → BACS in the UK.

Automated Market Maker (AMM)
A → decentralized finance (→ DeFi) mechanism that uses → algorithms to provide → liquidity in → cryptocurrency trading without traditional order books (→ order book). Examples include → Uniswap or → Aave, which rely on → liquidity pools.

Automated Teller Machine (ATM)
Physical device allowing customers to perform banking transactions (e.g., cash withdrawals, deposits) without human interaction. Modern ATMs may integrate → cryptocurrencies (e.g., → Bitcoin ATMs) or → NFC technologies.

Average Revenue Per User (ARPU)
A key performance indicator (→ KPI) in → fintech and → app economy contexts that measures the average revenue generated per user over a specific period. It is often used to assess the profitability of → digital platforms or → mobile banking services.

These definitions represent all entries starting with “A” from the “A-D” section of the Fintech Dictionary by Rainer Alt and Stefan Huch. Let me know if you need further assistance with these or other sections!