Cashing In On Data - How Can Fintech Benefit from This Precious Currency?

Before the rise of Web 2.0, the burst of the dot-com bubble, and the widespread popularity of Google, consumer data has been used to pinpoint customer interests for specific products as early as 1998. Data serves as the lifeline for sizable enterprises, pulsating its influence on their operations, particularly in scalability. Among these enterprises, participating in the “Data Gold Rush” are fintech companies.????

Fintech emerged as a derivative of the banking sector to offer alternative financial services and solutions catered to a broader spectrum of users including those that are not in the financial brackets that use traditional banks. Fintech companies focus on technology-driven innovation and customer experience while banks comply with a foolproof infrastructure and regulatory structures. With the introduction of hybrid banking and fintech models combining both practices and approaches, data has become increasingly vital to understand individual user preferences, needs, and behavior.?

74% of consumers are left frustrated when website content is not personalized and 98% of marketing experts claim that personalization advances customer relationships. This is all possible thanks to available data. Users often don’t realize that they’re influenced by personalization constantly. Personalization has now become the default and companies that don’t prioritize building a proper foundation for customization will risk falling behind.?

Additionally, with the collected data, fintech companies can create customized offerings, recommendations, and targeted marketing campaigns. Here are five ways in which fintech companies can leverage data to strengthen their position and stay ahead of the competition.?

Optimize Efficiency – With data collection, fintech companies can look to improve and streamline their operations more efficiently. With data, they can allocate resources more effectively which can result in cost savings and improved workflow.?

Customer Feedback – Feedback is one of the most significant forms of data. If gathered in real-time, this converted data can help identify areas for improvement, address customer concerns, and enhance the product and services sector. Consequently, this will increase heightened customer satisfaction and loyalty, ultimately leading to amplified revenue streams.

Market Trends – By analyzing customer data and behavior, fintech companies can gain insight advantage over competitors with spending patterns and financial trends to help product development, market positioning, and scalability.?

Product Innovation – Fintech relies on innovation to remain competitive. Analyzing data can help fintech companies understand customer preferences and specific pain points to create innovative solutions to remain competitive and to manage a product and service that feeds into hyper-personalization.?

Risk Assessment and Fraud Prevention – Historically, data has always been used to detect fraudulent measures and attempts. By analyzing historical transaction patterns and data, fintech companies can identify any irregular activity and stop fraudulent transactions in real-time. In parallel, fintech companies can use historic data to build predictive models that can forecast market trends, malicious customer behavior, and risks. They will allow them to take decisions proactively and adapt to a market that is constantly changing.?

Artificial Intelligence in Fintech

Fintech and the financial industry have always been spearheaded by professionals who have identified gaps in the market and created innovative products and services to widen the industry and compete with the banking sector.?

With the explosion of data analytics, artificial intelligence can now analyze a fintech company’s data more effectively, accurately, and faster than ever before. Imagine the fintech companies leveraging data to improve their performance and competitiveness. Now imagine artificial intelligence powering algorithms to produce vast amounts of data and recognize patterns and trends that no person can identify. It's a technology that supersedes any human and does not stop working. Unlike a person who works during scheduled hours, AI can work at any given time.?

90% of fintech companies are already leveraging AI and projected estimations indicate that the AI market within fintech will achieve a value of $31.71 billion by 2027, displaying a substantial growth rate of 28.6%.?

Currently, AI is being deployed across various applications and the data supplied is the fuel that will enable algorithms to identify the patterns and set a forecast for the best practices for the best performances. However, what if the data that’s provided isn’t accurate or biased??

Challenges in Navigating a Data-Powered Ecosystem

Amazon’s recruiting tool was an efficient method to screen candidates by leveraging AI to scan each individual applicant and measure each person’s capabilities based on their overall experience with a set of keywords.?

However, Amazon specialists later uncovered the program’s decisions did not favor female applicants. Just as AI relies on data to create algorithms, Amazon’s database historically has generally favored men for the available vacancies.?

It based the decisions on observing certain patterns in CVs over a 10-year period, where most of the applicants were men. Because of this, the hiring system did not approve female applicants and rejected any incoming CV with “Woman”.?

Consequently, the hiring process would systematically dismiss female candidates upon encountering terms like "women." This bias emerged due to the skewed data that predominantly featured successful male candidates, thereby fuelling the algorithm to use biased data to make a decision.?

Fundamentally, AI depends on pre-existing data, but the question is where do companies get this data? Unfortunately, there is no specific answer since every day, people “accept” to give away their data. Without realizing it, our data is harvested and fed into training data for Large Language Models (LLMs) and for companies to purchase.?

The ethical and privacy concerns have gotten the attention of governments and what role they play in regulating data collection and the use of AI. The European Union's General Data Protection Regulation (GDPR) has introduced rigorous privacy rules that place limitations on how companies are allowed to gather, utilize, and profit from user information.

Exemplified by the GDPR, other nations began to follow suit and enacted data privacy regulations. These regulations impose limitations on how companies can employ and capitalize on user data. Businesses are obliged to secure user consent for specific data usage, potentially constraining the scope at which fintech companies can charge for utilizing such insights.

Ultimately, the very reason we enjoy hyper-personalization is oftentimes credited to our data collection without our consent.

Final Thoughts

About a decade ago, the market caps of the top 10 companies in the world were measured by the goods and services provided. These days, 50% of the top 10 companies are data-based platforms such as Meta, Google, Amazon, and Apple. The discovery dawned the Gold Rush for data and the next precious commodity; the new “oil” as Clive Humby, the world-renowned British mathematician and entrepreneur, coined in 2006.?

In 2023, hyper-personalization is shaping the future where users will expect a personalized experience rather than a “one-size-fits-all”. While fintech companies can store all this data and need it to optimize their operations and services, they must equally handle customer data with responsibility ensuring compliance with data protection regulations to safeguard sensitive information.?