Is Data Lending the New European Startup Financing Trend? Here's what business leaders need to know.

All companies, no matter their size, must be able to generate revenue or secure capital to operate. In today's complex global economic landscape, this is proving to be challenging.

The European Central Bank April 2023 bank lending survey (BLS) revealed that euro area banks' credit standards for loans or credit lines to enterprises tightened substantially in the first quarter of 2023. The tightening remained at the highest level since the euro area sovereign debt crisis in 2011.

Influenced by the risks of uncertain economies and the growing demand to obtain capital through new channels, a new trend is gaining momentum. The new data lending wave, led by startups, leverages data-backed models, AI, and machine learning and will even accept a company's data as collateral.

In this report, we will examine the transformation of the industry, innovative new companies working in data lending, and the top challenges which business leaders should be aware of.

From limited exclusive models to an open and accessible market

The concept of data-backed loans is not totally new. In 2020, United Airlines secured a $6.8 billion loan backed by its MileagePlus program and customer loyalty data. That same year, American Airlines secured a $5.5 billion loan backed by its customer loyalty data. And while banks like Citigroup have jumped on the trend early, in recent years, the data lending market has diversified, with different companies taking different approaches.

With the global fintech lending market size valued at $1,385.66 billion in 2022 and expected to reach $23,110.83 billion by 2032, it is no surprise that new lending apps and services continue to rise. The European digital lending platform market was worth $1238.8 million in 2021 and is expected to more than triple that amount by 2029, peaking at $4981.81 million.

Emerging technologies such as AI, machine learning, and blockchain and increased data analytics power are driving the new fintech trends, cutting down costs, enhancing efficiency and profitability, and transforming time-consuming loan processes.

One company innovating in data lending is GulpData. The company recently secured $25 million to expand its lending capacity with a $10 million credit facility. GulpData has developed its own machine-learning technology to run rapid evaluations and uses the loaner's data as collateral.

Other startups in the sector include Credifi, using blockchain technology to connect loaners with small businesses, and FundBox, assessing creditworthiness with data-driven approaches.

What companies should keep an eye on.

Data lending models can be sophisticated and challenging to understand, leading to confusion when assessing the risks involved.

Data lending is still a relatively new concept, and while it has the potential to create new opportunities, it is not risk-free. From ethics to security to compliance, decision-makers should be well-informed before engaging in data lending.

Challenges.

The problem with using data as collateral is that it is extremely difficult to accurately value it. Additionally, to make correct assessments, the right technology must be used. Modern lenders will turn to AI and machine learning models to analyze large amounts of data.

A company that wants to secure data-backed loans must be sure its data warehouses and IT systems are fit for business. They must also ensure data security and maintenance, keeping data systems in an optimal state. This can be technically demanding and expensive.

Customer ethics, security, and compliance.

Several ethical issues are involved with using data for loans, but none deserves more consideration than the implications of turning in your customer data to a third party. Business leaders must understand their data responsibilities with their customers and secure compliance under European laws such as the GDPR.

Companies are also responsible for customer data security. Even if a cybersecurity incident affects a third-party provider, the exposure can be significantly damaging. Before deciding on the best data-backed lender, evaluate their security, compliance, and ethical practices. Failure to apply ethical standards and best practices and comply with regulations can have devastating effects, resulting in fines and criminal charges.

The takeaway.

Data lending will continue to grow, driven by demand. Undoubtedly, the industry has the potential to revolutionize the lending economy. Companies that are data-driven stand to benefit as they can also use data evaluations to make more informed decisions and reduce default risks while identifying market opportunities.

New technologies like machine learning and AI are also speeding processes leading to a reduction in costs. Modern data assessments improve loan interest rates, access to capital and credits, generate personalized lending portfolios, and create better terms.

While the risks involved with data lending require careful evaluation and consideration, they do not outweigh what the new trend brings to the table. By using data responsibly, both lenders and companies can streamline processes bringing about financial inclusion, a win-win for everyone.