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Data, Machine Learning/Artificial Intelligence and the Financial Sector

In a world where revolutionary, emerging technologies are rapidly evolving, digitalization plays an integral role in the development of humanity. Today, the integration of multi-dimensional digital systems and the Internet of Things (IoT) are essential in overcoming global security challenges including energy conservation, water distribution, quality of living, transportation management, and health care systems. With the growth of digitalization, investors are seeking to harness innovate technologies as a means to optimize their deployment of capital. In 2015, ideas of Green FinTech, or green financial technology, were launched with the aim to intersect the fields of finance, technology, and sustainability. Using digital technologies such as algorithms, software’s, and other smart applications, Green Fintech seeks to transition the traditional finance system to a greener, more environmental one.

Emerging technologies are an essential tool to enhance and promote sustainable economic growth. One such emerging technology is Machine Learning (ML) and Artificial Intelligence (AI). ML/AI techniques are used to analyze and optimize big data that is being generated. There are global forecasts that over the next 5 years data creation will reach over 180 zettabytes. The unprecedented volume of data that is produced by Information and Communication Technology (ICT) applications is too much to be processed by humans at an adequate pace. Due to this, ML/AI models are going to be critical tools in evaluating extensive amounts of data across various sectors. In the financial sector, it will help reduce costs, minimize resources, save time, and add value to financial business strategies. For example, ML/AI services can help with fraud detection, increased security in online transactions, analysis of spending patterns, and digital documentations that can be traced.

Digitalizing traditional finance systems through technologies like ML/AI will allow banks and financial institutions to highlight key data at an efficient pace, using the data to detect risks and opportunities for investors seeking more sustainable projects. One way that companies and organizations are attempting to hold themselves accountable to new sustainable standards is through the integration of ESG criteria. With the rising drive for environmental action, both consumers and investors are placing high value on ESG ratings and reports; in 2019, ESG funds in Europe attracted inflows of US $132 billon and US $20.6 billion in the United States. With the industry on the rise, ESG data has skyrocketed. In 2021, ESG funds reached an estimated $330 billion. The overwhelming amount of ESG data is too much for humans alone to analyze. With data being the backbone that investors are making their decisions off, technology is essential in interpreting the data and considering long-term objectives and assessments. ML/AI can help create portfolio mitigation, adaptation and transition for more climate aware projects. Data analytic tactics allow financial institutions the robust technological infrastructure to evaluate more accurate and effective solutions for sustainable development. Detecting patterns in the data and optimizing solutions will allow for a shift to adapted financial strategies, more targeted measurements and desired objectives, as well as a greater drive for ESG across all sectors.

The exponential evolvement of emerging technologies, like AI/ML, and increased interconnectivity between digital networks is only going to increase moving forward. With the increased global awareness of the detrimental effects of human activity alignment between sectors will be essential to combat environmental threats; it is essential that we align our technological advancements with financial capital, two key drivers in shifting our society towards a greener future.