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.  These products are being developed by FinTech and eCommerce companies as well as financial institutions, including banks. Some examples of these products are outlined below.

Big Data

Big data is being used in the financial services and insurance industries. In financial services, big data enhances the digital customer service experience, assists with fraud detection, enhances market analysis, provide predictive analytics that go into risk management strategies including credit decisions, and assists in developing and marketing new financial products.

The insurance industry is undergoing a digital revolution, with big data and telematics transforming how risks are assessed, and policies are priced. While this offers benefits like personalized coverage and potential cost savings, it also raises significant privacy concerns and the potential for discrimination. Insurers are increasingly collecting and analyzing vast amounts of personal data, from driving habits to social media activity, often without explicit consent. This raises the possibility that individuals could be unfairly discriminated against based on their data profile, leading to higher premiums or denied coverage. As the industry continues to evolve, it's crucial to strike a balance between innovation and individual rights, ensuring that data is used responsibly and ethically, with safeguards in place to protect against bias and discrimination.

Algorithms, Machine Learning, Digital Footprint, and Alternative Data

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.

Behavioral Biometrics

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.

RiskTech and RegTech

Risktech refers to the application of technology to manage various types of risks within financial institutions. These risks can include credit risk, market risk, operational risk, and regulatory risk. By automating aspects of risk management, RiskTech allows financial institutions to make informed decisions, reduce costs, and improve efficiency.

Regtech, short for regulatory technology, focuses on using technology to enhance compliance with regulatory requirements. As financial regulations become increasingly complex, these solutions help financial institutions streamline their compliance processes, reduce costs, and minimize the risk of regulatory penalties. Regtech often involves the use of automation, data analytics, and artificial intelligence to monitor regulatory changes, automate reporting, and identify potential compliance gaps.

Risktech and Regtech are essential tools for fintech companies operating in a complex and evolving regulatory environment. By embracing these technologies, fintech companies can mitigate risks, enhance compliance, and drive innovation.