Social Finance Innovators

Distributed ledger technology is increasingly acting as the infrastructure of the digital world. It is the technology behind Bitcoin and other cryptocurrencies, but it can be used for many more applications. The potential of blockchain technology has not even been remotely touched yet. Some new groundbreaking developments could emerge within this field over the next few years. Decentralized finance (DeFi) and non-fungible tokens (NFTs) are only two examples of how blockchain might change the world of finance. There are countless other ways in which people can use this technology, and it is difficult to predict what new developments will appear within these areas over the next few years. Blockchain has tremendous potential for growth and several challenges to be overcome before it becomes more widespread.

Neobanks is one of the most popular fintech services disrupting traditional banking. A neobank is a new type of bank operating online only and is built with mobile-first design principles. Customers can open an account through an app on their smartphone instead of going to a physical branch or filling out endless paperwork in paper format. Such apps feel more user-friendly, and most offer a wide range of banking features, including savings accounts, loans for customers’ cars or mortgages, and easy payments and remittances. For example, the apps of neobanks like the UK-based Monzo or Starling Bank and Germany’s Number26 have been growing rapidly in Europe and often ranked higher than banks. According to a study by Forbes, $1 trillion (USD) has been invested by banks in digital banking across the world to remain competitive.

While its potential is yet untapped, smart contracts can provide numerous benefits for the financial services industry: improved security (e.g., eliminating third parties), increased efficiency (with faster transactions and lower fees), better transparency (increasing accountability), and reduced fees (eliminating overhead costs). Examples of smart contracts in financial services include Compound Finance, which uses smart contracts to allow users to take out a short-term loan using Ether as collateral. Another example of a startup using smart contracts is called Agrello, which aims to develop smart contracts for enterprise customers which execute when certain conditions have been met. Most people associate smart contracts with blockchain technology; however, they deserve their own category since older examples include automated clearinghouses (ACHs) and central securities depositories (CSDs) used for bond issuance.

Virtual cards are based on VISA or Mastercard, and people can use them instead of physical cards for online transactions. There is no plastic involved, only a sixteen-digit card number, CVV code, and expiration date. Some virtual cards also allow users to store loyalty programs on them and use the same account for both fiat spending and crypto transactions, making it easier to manage funds by creating one consolidated balance across all accounts. Virtual cards can also be used as a backup payment method in cases where physical cards get declined or cannot be found. It is straightforward to set up virtual card accounts on mobile apps such as Zumo and iCard. One disadvantage of using virtual cards is they may not work correctly with all retailers.

How do you automate finance? In simple words, autonomous finance is a system of machines and devices that can automatically perform financial transactions without the involvement of humans. The use cases for this type of technology include automatic payments for insurance premiums or autonomous investing using robo-advisers such as Wealthfront or Betterment. Another example of autonomous finance would be using blockchain-based smart contracts to automate fund management and insurance premiums. Etherisc allows users or organizations to set up “flight-delay” insurance policies to automatically payout if flights get delayed by two hours or more, removing the hassle of filing a claim manually after something happens

There is a wealth of research and use cases regarding artificial intelligence in financial services. Applications include risk assessment, forecasting, data management, automation, and other yet-discovered use cases. One notable development is robo-advisors; they are now one of the most popular trends in fintech. These online platforms can independently manage investments and suggest a personalized portfolio best suited to individual interests. They use cognitive computing technology and big data trends to determine the most optimal investment strategy. Other examples of AI in finance include chatbots used by banks to provide basic customer service queries or IBM Watson for financial analysis. With AI increasingly being used by these bots, they can learn from client conversations and customize future customer interactions accordingly. FinTech companies could achieve this thanks to machine learning, where bots use historical data (such as purchase history) and real-time inputs (like news) to learn and predict future customer behavior. Machine learning is a subcategory of AI used to learn and evolve from data to solve complex problems. Examples of machine learning in finance include fraud detection, compliance analysis, and algorithmic trading.