Big Data in the Fintech Sector

On the occasion of World Data Protection Day, which took place this Sunday, January 28, 2024, let’s discover how big data is shaping the future of fintech.

How does Big Data impact the Fintech sector?

Companies developing digital technologies to optimize financial services have been heavily impacted by the emergence of the Big Data phenomenon.

Big Data is currently revolutionizing the Fintech sector in terms of data security, for example, as well as in fraud detection.

AI, coupled with data analysis, allows for the creation of more detailed customer profiles and the prevention of suspicious activities.

Alternative data (the term given to big data in the Fintech field) is used in:

• online payments 💳,
• insurtech (for digital insurance),
• lending,
• trading.

For example, Greather Than promises its clients to convert data collected from drivers’ GPS behavior into accident probability and climate impact.

Big data also leverages data from social networks to assess credit risks and meet changing customer expectations, thereby facilitating customer segmentation mechanisms and fraud detection.

Moreover, the contribution of Big Data allows for better data-driven decision-making, thus improving operational efficiency and enhancing customer experiences. Finally, Big Data is a key driver of the emergence of open banking, which offers more integrated and personalized financial services while increasing control over consumer data. Open banking is a practice adopted by banks and financial institutions to make their data accessible to everyone, allowing for their free use and sharing.

In the finance sector, information is ultimately the realm’s currency 🪙

Asset managers, in particular, rely on a constant flow of data to fuel their investment decisions. Every piece of information can influence the price of a stock 📈, the return on a bond, or the value of a portfolio.

The financial industry, a major consumer of information, relies on these specialists to feed its insatiable need for data. Investors, whether institutions or individuals, demand fast, accurate and relevant information to remain competitive. They turn to these financial information specialists to gain insights that will enable them to make informed decisions 💡.

Alternative data: the new Holy Grail of asset managers

In the financial landscape, the arrival of big data and alternative data has given rise to a considerable number of new players who transform information into high-value market insights 🧐. These innovative intermediaries, often agile startups, tap into the hidden riches of social networks such as Twitter and Reddit, analyzing trends and sentiments to generate relevant trading signals.

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The use of satellite images 🛰️ to assess economic activity, such as parking analysis to predict retail sales outcomes, has become a valuable tool. Similarly, mobile data provides unique fingerprints of consumer behaviors, which, when aggregated and analyzed, can provide insights into a company’s future performance.

This alternative data is processed by specialists who decipher and interpret these vast sets of unstructured data, transforming raw information into actionable insights. Portfolio managers, in turn, rely on these trends to make informed market positions, anticipating company results before they become public.

The relevance of this data in investors’ decision-making processes underscores a profound transformation in the financial sector, where the ability to interpret and act on the basis of alternative data becomes a powerful competitive advantage. By leveraging these new sources of information, asset managers can make more informed decisions, reduce risk, and maximize returns for their investors.

Companies using Big Data in their financial services

Here are some examples of companies that have integrated Big Data into their operations:

1. Ant Financial Services Group – Ant Financial uses big data to assess credit and offer loans to SMEs and consumers who have no traditional credit history. Their credit scoring technology, called Zhima Credit, analyzes data from various sources to assess users’ creditworthiness.

2. Square, Inc. – This company uses big data to offer loans to small businesses using its point-of-sale system. Square Capital assesses a company’s financial health by analyzing daily transactions and sales habits to decide if it is eligible for a loan and to determine repayment terms.

3. Credit Karma – Credit Karma provides personalized recommendations for credit cards and loans by analyzing users’ credit data. They also use big data to help users monitor their creditworthiness and detect potential fraud risks.

These companies demonstrate innovative use of big data to offer more accurate, personalized and efficient financial services, thereby improving user experience and decision-making in personal and business finance.

The impact of alternative data on financial analysis: between immediate insight and long-term strategy

The integration of alternative data into financial analysis redefines analysts’ expectations and forecasts, particularly in terms of timing.

This data, often focused on the very short term, refines short-term forecasts but could paradoxically harm long-term vision ⚠️. Indeed, the abundance of instant information and its processing by artificial intelligence tends to focus analysts’ attention on immediacy, to the detriment of a more enduring strategic approach.

This influx of short-term data is a double-edged sword. On the one hand, it gives analysts increased predictive power 🔮 over imminent market fluctuations, allowing them to capitalize on rapid movements and emerging trends. On the other hand, this hyper-focus on the present sometimes eclipses understanding of underlying trends and long-term dynamics that shape the future of companies and markets.

This results in a challenge for investment strategies: finding the right balance between effectively leveraging this data for short-term gains and preserving a strategic perspective that takes into account long-term trajectories. In other words, while AI and advanced analytics offer valuable insights, their effectiveness diminishes when it comes to navigating the complexity of long-term forecasting and sustainable investment strategy.

This observation raises a fundamental question for the financial industry:

How to judiciously integrate alternative data to enrich, rather than restrict, our financial vision?

The quest for a balance between these divergent time horizons is crucial to shaping the future of financial analysis and strategic decision-making.

In conclusion, big data and alternative data represent a major evolution in the fintech sector. Fintech companies, through their ability to exploit this data, offer unprecedented personalization and enhanced security, while laying the groundwork for a more open and integrated financial world through open banking.

Equipped with increasingly sophisticated analytical tools, asset managers are now able to anticipate market movements with remarkable precision. However, the abundance of short-term data invites deeper reflection on their long-term impact. The investment strategy must therefore adapt to leverage immediate benefits while preserving a strategic and sustainable vision.

In this context, a quote from Edgar Morin, philosopher and sociologist, seems particularly appropriate:

“We are in the era of knowledge.

Knowledge is the key to everything.”

In the financial domain, this knowledge is fueled by big data, opening the door to unprecedented opportunities while challenging our ability to look beyond the immediate horizon. To navigate this era of knowledge, fintech actors and investors must remain vigilant, ready to integrate new data while being aware of the long-term implications, to ensure not only economic prosperity but also financial sustainability for future generations 🌎.

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References:

Xerfi Canal: How does Big Data provide insight into investor uncertainty? Interview with Thierry Foucault, professor at HEC Paris, Hi! Paris center.