The data behind AI’s success in the financial sector

Or Lenchner, CEO at Bright Data


AI (Artificial Intelligence) has taken the world by storm. The OECD estimates that global spending on AI will double from $50 billion in 2020 to more than $110 billion in 2024. The financial sector is no different, AI is expected to drive competitive advantages for banks and investment firms. Whether it’s used to enhance the quality of services offered to customers, automate processes, reduce risks, or unlock new investment strategies, AI is transforming the world of finance.

The role of publicly available data

The adoption of AI systems by financial services was made possible due to advancements in public web data collection. AI-based tools rely on massive amounts of data to continuously learn from therefore the data itself must be up to date, otherwise, by the time the model is deployed it might no longer be relevant, and in finance, that could result in significant financial losses.

Additionally, the quality and diversity of the data used to train AI models plays a critical role in their performance and accuracy. Especially in the financial world, an AI tasked with generating insights for financial decisions can’t be trusted if the data it was trained on wasn’t diverse enough.

This is where public web data comes in. The internet is the world’s largest up-to-date database, and that data is invaluable to the insights AI models provide. AI that feeds off public web data will have a competitive advantage, so long as the publicly available data collected is verified and cleaned to ensure it contains only relevant and accurate data.

Or Lenchner

AI tools can have many applications in the financial sector, for example, the detection of fraudulent activity. As the Federal Trade Commission reported fraudulent activity cost consumers $8.8 billion in 2022. An AI trained on outdated data will not be able to learn from the latest scams, and will not be trusted to detect such activity.

Ethical data and alternative sources

As companies realise the importance of publicly available data for their AI-based tools, collecting that data is a time-consuming, costly and tedious endeavor. For that reason, most companies turn to public web data providers, to help them save time and money. Not only do they collect the data but they structure, clean and synthesise the public datasets for immediate use.

In most cases, companies directly purchase pre-collected datasets for high volumes of public web data to train their AI models. Pre-collected datasets are cost-effective, instant and can be frequently updated. Through outsourcing their public web data operation, many financial institutions have realised that insights can be drawn from different sources. AI models can be trained to better analyse customers by assessing their social media sentiment, the impact of the climate crisis or price changes on consumer habits. Sometimes, the insights generated from alternative data can be applied to entire populations in a shared location.

It goes without saying that the public available data used to train AI models should be collected in an ethical manner. The US Securities and Exchange Commission constantly targets financial institutions tied to unethical conduct, which could lead to significant monetary losses. For that reason, financial institutions must ensure that the public web data providers they work with are transparent in their operations and compliant with the law.

While financial institutions are racing to adopt AI tools in their daily operations, we have only scratched the surface of what they are capable of. AI-assisted tools will undoubtedly be an integral part of the future of finance, but advancements in public web data collection are no less decisive. The quantity and quality of public data will determine the success of AI tools, but we must ensure it is collected ethically.

Explore more