Financial technologies – or FinTech for short – means new financial services that are only possible through digital technologies and the Internet. These can lead to new applications, processes, products or even business models. The range of innovations extends from banking via smartphone to automated financial advice and digital cryptocurrencies. Drivers are often specialized start-ups, but increasingly also established banks, insurers or auditors. FinTech poses challenges for the financial sector, but at the same time offers it the potential to make processes more efficient and to make its own offerings more modern and customer-friendly.
Artificial intelligence and machine learning are among the most widely used financial technologies and have the potential to play a major role in the financial industry. Some of these fintech applications include credit scoring, risk assessment, fraud detection and regulatory compliance and wealth management. It is also possible to use machine learning to monitor trends in real time and automatically price in political, economic and social developments, for example. A skill that is particularly in demand by participants in the financial sip trunk providers.
Typical fintech applications in the consumer segment are particularly mobile and innovative forms of investment such as social trading, in which private investors share information about the market, the stock exchange and their own investments. Blockchain technology is also used extensively in the financial industry, largely due to its ability to securely store transaction records and other sensitive data. Every transaction is encrypted and the probability of successful cyber attacks is low when using blockchains. Blockchain technology is also the backbone of many cryptocurrencies.
Fin-Tech Key Technologies
- AI and machine learning
- Text Analysis and Predictive Analytics
- Robotic Process Automation (RPA)
Motivated by the world-wide surge of FinTech lending, we analyze the implications of lenders’ information technology adoption for financial stability. We estimate bank-level intensity of IT adoption before the global financial crisis using a novel dataset that provides information on hardware used in US commercial bank branches after mapping them to their parent bank. We find that higher intensity of IT-adoption led to significantly lower non-performing loans when the crisis hit: banks with a one standard deviation higher IT-adoption experienced 10% lower non-performing loans.
High-IT-adoption banks were not less exposed to the crisis through their geographical footprint, business model, funding sources, or other observable characteristics. Loan-level analysis indicates that high-IT-adoption banks originated mortgages with better performance and did not offload low-quality loans. We apply a simple text-analysis algorithm to the biographies of top executives and find that banks led by more “tech-oriented” managers adopted IT more intensively and experienced lower non-performing loans during the crisis. Our results suggest that technology adoption in lending can enhance financial stability through the production of more resilient loans.