21st century FinTech products are considered disruptive innovations. These products leverage digital technology and consumer data, using both data aggregation and advanced data analytics to create entirely new financial services products. Some examples of these products are outlined below.
Insurance is a familiar product for both individuals and small businesses. Over the past several years, insurance products have made significant changes; partly, because the insurance industry has become—according to some—the first “Big Data” business.
Because insurance companies collect and use big data, they can deliver better prices and tailor-made products. Some insurance companies are starting to use IoT (Internet of Things), however, its use comes with cybersecurity issues. Smart IoT data collection devices are not designed with security in mind. Two-thirds of the most commonly used IoT devices [mobile apps, biometric, and environmental sensors] contain vulnerabilities that a hacker can exploit. Naturally, that is not the kind of risk insurers can or would like to price into their models. Going forward, consumers and insurers must find a balance between lower insurance premiums and how much customer data an insurer collects and how best to protect it.
Many financial services products now use algorithms. An increasingly common use of algorithms is algorithmic credit scoring. An algorithmic credit score collects a customer’s digital footprint and pairs it with traditional credit scores to create an enhanced credit score for approving borrowers. Banks and other lenders are increasingly turning to additional, unstructured, and semi-structured data sources. Those sources are a combination of social media activity, mobile phone use, online browsing, email receipts, text message activity, and geotrackers. Together they capture a more nuanced view of creditworthiness, and improve the rating accuracy of loans.
Applying machine learning algorithms to the collection of data enables an assessment of qualitative factors such as purchasing behavior and willingness to pay. Traditionally, banks and other lenders have relied entirely on credit bureau scores to determine a person’s creditworthiness. If a potential borrower had never used credit, they would not have a credit bureau score and most likely would be unable to obtain credit and build a credit history. By pairing alternative data sources and machine learning algorithms, lenders are better able to assess a customer’s ability and willingness to repay, and make better credit decisions than previously possible.
Fingerprint and iris scans are static biometric security devices that have been in use for several years. Behavioral biometrics are a new security device; they rely on the user’s behavior. Motions such as how hard the user strikes the keys, the angle their finger swipes the touchscreen, or a particular typing rhythm all create a user identity. Using machine learning algorithms, behavioral biometrics builds a unique picture of an individual user. The end results is better fraud identification, particularly for mobile payment authentication.
In the recent past, FinTech products predominately focused on consumer lending and payments, now new FinTech segments such as Risktech and Regtech have entered the market. To learn more about these FinTech sectors, scroll down to the Book section, and look for books about Risktech and Regtech. Also, click on the Databases tab and search through the databases listed.
The following sources from the Internet and from the print collections at the Library of Congress are useful in learning more about FinTech in the 21st century.
The following bibliography is just a selection of the books available on this topic. The books listed below link to fuller bibliographic information for each item in the the Library of Congress Online Catalog. Links are provided for additional online content when available.