Artificial intelligence (AI) and its applications are developing at a rapid pace and are increasingly penetrating our daily lives. Today, AI applications are a benchmark that seems to have come to reshape or even flip the balance sheets and norms in critical areas such as investing, risk assessment and transaction fraud prevention.

According to Forbes, 70% of financial firms are already using artificial intelligence to predict problems related to credit risk prediction and fraud detection. JPMorgan Chase is already using fraud detection apps, especially in relation to consumer fraud, which is currently a huge social and moral problem in Greece, for which banks are also jointly responsible. Specifically, it uses an algorithm to send credit card transaction data to data centers that decide if it is fraud.

Of course, such a revolutionary tool could not ignore the most pressing and pressing issue of the 21st century: climate change and sustainable development.

Traditionally, ESG rating agencies such as Moody’s have relied on analysts to identify and evaluate data. However, in order to efficiently process the huge amounts of data that might be needed to invest in ESG, today they have adopted computational algorithms that can automate complex tasks and analyze information at high speed. For example, RepRisk uses daily media as sources of information, company announcements and other news to evaluate ESG, while other platforms use data from sustainability reports.

In particular, artificial intelligence is already widely used in:

Data collection and analysis: Significantly reduces the time to collect and analyze huge amounts of data. It helps investors understand market trends, consumer preferences, and company growth forecasts.

70% of financial companies are already using artificial intelligence for credit risk prediction and fraud detection.

Market forecasting and investment decisions. Market trends are predicted using machine learning algorithms and neural networks, allowing investors to make more informed decisions.

ESG Performance Metrics: The use of machine learning algorithms analyzes historical and real-time data, allowing investors and organizations to measure and compare ESG performance across companies, industries and regions.

The impartial collection and use of data is one of the most important issues of concern. Google, aware of this risk, says it is trying to educate developers about fairness when building, evaluating, and deploying AI and machine learning models. The second question concerns the transparency of decision-making. AI algorithms are considered black boxes because they do not provide transparency in the decision-making process.

The issue of security and privacy is also important. Access to sensitive data such as personal information, financial records or corporate data requires the development of strong security measures to prevent unauthorized access or data leakage.

In conclusion, although AI-based investment decisions and ESG ratings are not likely to completely replace analysts in the near future, they are a very useful complementary tool that contributes to forecasts and greatly improves understanding of the evaluation results (ESG ratings) that are now performed on a daily basis. after the issue of processing a huge amount of data has been resolved.

Mr. Nikos Avlonas is President of the Center for Sustainability (CSE) and Visiting Professor at the University of Economics in Athens (IMBA) and the University of Illinois at Chicago.