FinTech For Dummies Cheat Sheet

The following definitions are often associated with the technologies utilized by FinTech firms engaged in the modernization of financial and insurance institutions:

• Agile development: A fast, iterative development process built on use cases and minimum viable product definitions coordinated through a daily scrum process and close collaboration by cross-department teams.
• Application programming interface (API): A set of reusable functions, procedures, and other tools encapsulated into a type of development shorthand that permits the coder to call native code without knowing it. Anytime you place an order on Amazon, you’re using an interface driven by an API. The API is a loose compilation of code that is called in a sequenced way to get the book you wanted for a given price by the time, date, and place you’ve determined.
• Artificial intelligence (AI): Machines programmed to think like humans, to reach conclusions, and to act independently in accordance with those conclusions.
• Cryptocurrency: A type of internet-based financial exchange using highly encrypted digital assets, generally through blockchain technologies.
• Digital transformation: Using new technologies to modify data that has been stored in traditional ways to make it more meaningful, easily consumed, consistent, and transparent across whole sectors of an organization. Chat bots that answer support questions are examples of the transformative use of old data.
• Distributed ledger: Shared data that exists without ownership, controls, or centralization, but that requires consensus around its validation, replication, and synchronization across multiple nodes. For example, Bitcoin transactions are recorded on a distributed ledger.
• Machine learning (ML): A subset of artificial intelligence; allows systems to automatically identify patterns and learn expected outcomes from historical results and then change the system to improve future results. When Yelp offers you a new restaurant suggestion or Netflix suggests a movie you may like, it’s using machine learning to select these offerings.
• Metadata: The road map that directs and defines aspects of data; provides insights into the data’s basic structure and the best ways of utilizing it. For example, data like your name, date of birth, time of birth, the hospital you were born in, and the city and state may all be assembled into one view that represents your birth certificate. That same structure can be used for any number of other individuals.

FinTech providers assist financial and insurance institutions in identifying and remedying problem areas or determining future opportunities for growth. They do so because they have a broader reach into the whole industry. This FinTech reach exists because they

• Work with many customers throughout the industry, and see the same problems repeatedly.
• Are focused on an organization’s operations and infrastructure.
• Can often make hard decisions that are too political for internal staff to view objectively.
• Take a holistic approach to solving a client company’s technology problems.
• Can be 100 percent customer-focused and value-led.

The following tools are utilized by FinTech firms engaged in the modernization, invigoration, or replacement of legacy systems within financial and insurance institutions:

• Microservices: A software development architecture of loosely coupled services expressed as an application.
• Application programming interface (API) strategies: The strategies used to make APIs readily available and consumable by both internal and external users.
• Real-time delivery: Data delivered immediately as it is received and processed.
• Data management: The process around acquiring, validating, cleansing, managing, and storing data for real-time or future use.
• Distributed ledger technologies: Shared data where there is no ownership, controls, or centralization of that data, but there is consensus around the validation, replication, and synchronization of that data across multiple nodes.
• Cloud/web-based delivery systems: Applications, infrastructure, services, or platforms delivered on the cloud or internet.
• Open source development: Publicly available code or applications that aren’t owned by anyone but can be modified by all.
• Artificial intelligence (AI) and machine learning (ML): The structured approach conducted by machines programmed to think like humans and to reach conclusions and act independently in accordance with those conclusions. ML is a subset of AI; machine learning is the first stage of independent self-assessment conducted by a computer. ML is coded to allow systems to automatically identify patterns and learn expected outcomes from historical results and then change the system to improve future results.