Artificial intelligence is disrupting the financial market

AI is influencing how banks and lenders make investment and funding decisions.

It’s hard to say how much machine learning and other technologies will change the finance industry in the long run. However, the fact is that the revolution driven by artificial intelligence (AI) is already in full swing and is influencing the way banks and lenders make investment and funding decisions. Man has great difficulty in predicting the future. Do you remember “Back to the Future”? According to this film, we should all be flying in a DeLorean powered by nuclear fusion generators, and yet we are not yet.

It is therefore logical that the debate on AI and its consequences for the future of the finance sector is relatively hazardous. It’s even frankly impossible to get an exact picture of how AI will disrupt the way banks, portfolio managers, or insurance companies work. However, we can already say that the AI-driven revolution is in full swing, bringing with it significant investments. Alternative intelligence continues to grow in the FinTech and traditional banking markets, and is estimated to be worth $27 billion (+23% CAGR) by 2026 and $64 billion (+33%) by 2030.

Data-driven analytics to improve decisions

Artificial intelligence is revolutionizing the way banks and lenders make investment and funding decisions. It helps them to more accurately assess the credit history of borrowers. One of these companies is for example OakNorth. Its founders have worked, with great success, to exploit the maximum computing power and models of machine learning to develop, in just seven years, an AI-based credit platform. The primary objective of the company is to provide financing to small and medium enterprises with financial needs between 1 and 30 million dollars.

Unlike peer-to-peer lenders, OakNorth takes on the balance sheet risk itself.

And by leveraging this target audience, companies like OakNorth hit the nail on the head: for one thing, this financing segment is vastly underserved by big banks and other large financial institutions. Indeed, due to regulatory requirements and reductions in personnel and budget, these credit operations are often too expensive. On the other hand, the volume of these credits is too large for private lenders. OakNorth fills the gap by combining technology and machine learning algorithms.

Today, this technology enables financial institutions to make faster and better decisions throughout the credit lifecycle. Thanks to its data-driven approach, the company provides support in terms of credit analysis and monitoring. By relying on machine learning, the collection of huge data sets and the borrower’s lifetime credit history, it is now able to model a forward-looking view of the borrower’s financial situation. Unlike peer-to-peer lenders, OakNorth takes on the balance sheet risk itself.

The company generates its profits from the difference in interest rates between the subscription of the credit and the granting of the loan to the borrower. The result is spectacular: in just seven years, OakNorth has granted credit transactions worth more than 9 billion dollars, of which only a tiny amount has been in default.

Artificial intelligence, hedge funds…

Another area where AI plays an important role is in hedge funds. They are using AI at scale in their trading strategies to exploit arbitrage opportunities in the markets. Two Sigma, Renaissance and other heavyweights have been developing quantitative models for years that leverage billions of gigabits of data to identify and predict arbitrage opportunities, shifts in market sentiment and distortions in asset classes, etc. These models are mostly based on statistical results. They attempt to gauge the likelihood of a trade’s success using a wide range of signals that are in turn applied to various markets and time horizons.

Over the years, firms specializing in quantitative hedge funds have also begun to incorporate other sources of data into their models, namely sources that have absolutely no relation to market prices. These alternative sources of data range from assessing the impact of messages posted on Twitter by those involved to analyzing satellite photos of large mining operations, ports and warehouses measures the flow of goods, to the keywords used by CEOs in interviews with analysts to predict the evolution of their business.

… and private markets

To conclude, a trend observed relatively recently is the penetration of data banks and artificial intelligence in private markets. Even the most successful venture capital funds are now betting on new technologies so that they can better predict which companies will inevitably fall out of the fund’s “pack”. It is no longer sufficient to rely on internal networks as the primary source for analyzing transaction flow. Relatively new analytics systems now use machine learning to use ‘early detectors’ to gather information about companies from their online profiles through ‘crawling’.

Seed funds must be the first to discover talent who leave their position within the company or an existing unicorn to found their own company.

This method collects data from alternative sources like the company’s website, social media platforms, product libraries and news sources that are likely to deliver growth “signals” from transaction data. The goal is to predict where the next unicorn will come from. This implies, among other things, that we focus more on individuals than on companies. For example, seed funds must be the first to discover talent who leave their position within the company or an existing unicorn to found their own company. And that’s exactly what companies like Specter are doing. Thanks to a large number of internal sources and algorithms used to evaluate companies and talents, users are able to identify companies with high growth potential and seize opportunities before their competitors.

The need to increase efficiency

We can summarize by saying that it is certainly impossible to know where the penetration of artificial intelligence in the financial sector will lead. But it is already evident that the branch is undergoing profound upheavals.

In any case, it is very likely that there will be no going back to the “good old way of doing things.” Increased competition and pressure to quickly exploit arbitrage opportunities are forcing public and now also private markets to be more efficient. It is technology that has made this trend possible, with all its positive and negative implications on the decisions thus made.

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Artificial intelligence is disrupting the financial market


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