Artem VasinWhen fintech development emerged in the late 20th century, the term was originally applied to technologies used in the back-end systems of established banks and their ATMs. Later, with the proliferation of the Internet, financial companies began providing customers with online access to their bank account balances and recent transactions. In the mid-2000s, however, fintech reached a new level as online payments and money transfers became available to consumers. Financial technology not only allowed faster access to services but also helped reduce the costs associated with each transaction.
Today, fintech software development has further expanded the range of financial opportunities. Fintech companies build personal finance applications, investment platforms, insurance applications, and peer-to-peer (P2P) lending services.
Fintech also includes the development of cryptocurrencies, which operate on a distributed public ledger called a blockchain. Although this segment of fintech often appears on front pages, the big money still lies within the traditional global banking industry, which remains the cornerstone of the financial world.
This article explores the most promising trends in financial technology, delving into advancements in open banking, alternative credit scoring, mobile-first applications, and the cutting-edge capabilities of fintech software made possible by machine learning.
Table of Contents
The rise and impact of open banking
Traditionally, banks have stored customer transaction data in closed systems. However, in 2015, the European Parliament revised the Payment Services Directive to promote innovative online and mobile payments through open banking. Following this revision, several countries introduced laws requiring banks to provide third parties with access to customer data via application programming interfaces (APIs). As a result, bank customers can now share their financial data with other banks, payment providers, insurance companies, and lending platforms.
This approach allows financial companies to offer personalized services and benefits, such as optimizing savings, enhancing investment options, or avoiding overdraft fees by moving money between accounts on behalf of the customer.
One example of open banking in action is personal finance tools that consolidate all of a customer’s financial accounts, including mortgages, credit cards, and online payment platforms. These tools allow users to analyze their financial habits, automate regular payments, and monitor their credit scores.
However, open banking has its critics, who highlight the increased risk of exposing users’ financial and personal data. Fraudulent actors find it easier to target third-party financial applications than to breach the robust security of large banks.
Mobile-first banking and biometric authentication
Many banks today are mobile-first by default, and some mobile banks may not even have physical branches. Users can open a new bank account from their mobile devices and have a physical card delivered to them by courier. All services are available on smartphones, including remote deposit management, fund transfers, bill payments, expense tracking, guidance on how to do an expense report, and online assistance from a personal banking advisor.
In addition, mobile banking is convenient because customers receive real-time alerts that help them keep track of their transactions and prevent overdraft fees.
Recent trends in mobile banking include AI-powered customer service, advanced security, and biometric authentication. Multi-factor protection of financial accounts and transactions is no longer sufficient; modern apps feature built-in user identification through fingerprint, facial recognition, or retina scanning. The latest smartphone models are equipped with the necessary technologies, so users rightfully expect biometric authentication to be implemented in mobile banking applications.
AI and machine learning yield more accurate financial predictions
The development of machine learning dates back to the early 2000s, when computer hardware became powerful enough to collect and process vast amounts of data. Following the breakthroughs in deep learning and transformer architecture, investments in artificial intelligence skyrocketed in the 2020s as generative AI entered the public consciousness.
Nowadays, AI technology is enhancing the capabilities of financial software, including predictive analytics, fraud detection, personalized financial advice, and efficient customer service.
Large language models (LLMs) are designed to analyze and summarize vast amounts of text data and generate human-sounding responses. LLMs can provide fintech customers with personalized advice on achieving their financial goals, such as budget planning, choosing the best financial products, or tracking stock price movements.
Machine learning is used in fintech to discover complex fraud patterns in real time. It also evaluates individuals’ creditworthiness and helps avoid bias in credit decisions by relying on data-driven, objective parameters. Finally, machine learning algorithms assist in making more accurate trading decisions by processing vast amounts of data at unprecedented speeds, significantly improving the accuracy of predictions.
Robo-advisors are digital platforms that provide algorithmic financial planning and investment management. Thanks to machine learning, they constantly adapt to market changes and client needs, offering more accurate, personalized advice. Specifically, robo-advisors adjust to clients’ individual risk tolerance and financial goals, helping to create and manage investment portfolios. Users simply enter their current financial assets and expected income, set their goals, and ask the robo-advisor to allocate assets among investment opportunities in accordance with these defined goals.
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Insurtech
The insurtech phenomenon is relatively new. This field seeks to address the problem of traditional insurance, where some people pay more than they should based on basic rates and rough classifications of risk groups. By using data from various internet-connected devices, insurtech products can offer customized insurance coverage.
For instance, insurtech companies can gather data from wearables such as fitness trackers and smartwatches to monitor physical activity and heart rate, or detect driving patterns and accidents through geolocation. As a result, they create more accurate and distinct risk profiles and dynamically adjust premium prices.
In addition, insurtech companies are exploring a range of potential game-changers, such as advanced big data collection methods. By gathering information on millions of customers, they can analyze customer characteristics and habits, create personas, and calculate appropriate insurance premiums based on these risk profiles.
Alternative credit scoring for unbanked customers
The absence of a traditional credit score can deprive customers of access to loans. Recent immigrants, students, young adults, and individuals with low credit scores from traditional credit bureaus may struggle to secure loan approvals. However, even without traditional credit score data, financial companies can adopt new methods to determine creditworthiness and attract unbanked customers.
Lenders are now using machine learning and APIs to gather and analyze alternative data sources for lending decisions, such as mobile phone bills, short-term rentals through Airbnb, in-app purchases, and online subscriptions. Even paying utility bills consistently indicates positive spending habits. For lenders, it is crucial to ensure that applicants can make regular payments.
In addition to a customer’s financial discipline, fintech companies may also consider their digital footprint, including social media activity and connections with people.
Improving protection against synthetic identity threats
With easier access to financial services comes a greater sophistication of malicious attacks. One recent trend is the creation of a synthetic identity that combines real personal information with fake data. For example, an attacker might use someone’s social security number and date of birth but provide a fake name and address. These treacherous methods are often employed to create a bank account for money laundering or to obtain a loan through an online application.
Synthetic identity fraud primarily affects fintech companies rather than individual customers. For instance, fraudsters may take out a small line of credit, pay it off, and then secure a larger loan, disappearing without repaying the debt.
Financial companies should implement advanced security measures to detect fake identities before they inflict credit losses. They can use liveness detection via mobile phone cameras to confirm that a user is a live person rather than a static image, apply optical character recognition (OCR) to identify abnormalities on government-issued documents, such as altered text or mismatched fonts, and join anti-fraud networks – large databases of fraud reports collected by various banks and financial institutions.
Final thoughts
Financial services that were once confined to physical branches and in-person interactions with financial advisors are now increasingly available on digital devices. With advancements in machine learning, faster computing power, and shifting consumer behavior – where smartphones, tablets, and wearables are now commonly used for everyday needs – the financial industry has significant room for growth.
