For more than a decade, the financial sector has been heavily investing in data collection and processing technologies like data warehouses and Business Intelligence. The impact—and necessity of accessing—Big Data cannot be overstated.
Ten years ago, computers could only analyze structured data, or data that is easily quantifiable and organized in a set form. New technologies have allowed fin-tech professionals to analyze unstructured data, or data that is not as easily quantified. These innovations have enabled people to interpret information from a much wider variety of sources, including language, images and speech for the first time.
Data-driven investing builds on what AI models can achieve by enabling investors to attain increased granularity from their analyses. Investors now compare behavior patterns and trends related to a potential investment.
They also focus on data-driven investment models to evaluate public companies. Investors used to have access to only internal company and market data, Now, they have web traffic, patent, and even satellite data to check a potential investment from every possible angle.
Access to new types of data and enhanced ability to process that data has led to new ways to invest. Firms can now focus more clearly on investment momentum, value, profitability, and sentiment.
Due to changing customer expectations and the increased competition of Fin-tech players, banks have also had to adapt to Big Data artificial intelligence algorithms.
Banks store huge amounts of data already: ATM deposits and withdrawals, point-of-sales purchases, online payments, customer profile data collected for KYC (know your customer) guidelines, etc. Now banks can use this data to maximize satisfaction and increase their competitive advantage.
Thanks to the quantitative nature of the financial domain and the large volumes of historical data, machine learning is poised to enhance many aspects of the financial ecosystem.
Humans remain a big part of the trading equation but AI plays an increasingly significant role. Machine learning algorithms help investors make better trades on the stock market. The models can process thousands of datasets simultaneously. They can also monitor the news, make stock predictions, and make real-time trades at the best prices.
Hedge funds, meanwhile, are more difficult to automate but many of them are using AI-powered analysis to get investment ideas and build portfolios.
Machine learning techniques provide investors the room to build models specifically adapted to their current challenges. For example, NLP (Natural language processing), uses computers to read and interpret vast amounts of text, enabling us to incorporate textual data in multiple languages from a variety of sources. One of the more obvious applications of NLP is gauging sentiment in the text—does the tone in the news article or research report published on a company positive or negative? This goes on to affect the price of the company’s market share.
ML models reduce operational costs and automate repetitive tasks, thereby increasing productivity—and therefore revenues. These models also better enable compliance and reinforce security thanks to process automation. As a result, machine learning models enable companies to optimize costs, improve customer experiences, and scale up services.
As a result of developing technologies, however, financial security threats have also increased and become more sophisticated. The growing number of transactions, users, and third-party integrations also provide more opportunity for security threats. But machine learning algorithms are excellent at detecting fraud and, as the name implies, the models continually learn to be better.
Machine learning algorithms also fit perfectly with the underwriting tasks that are so common in finance and insurance. Data scientists teach ML systems to do clerical tasks and to generate credit scores. Human workers can then move onto their next tasks faster and with more accuracy.
Lately, robo-advisors have become commonplace in the financial domain. Customers choose robo-advisors over personal financial advisors due to lower fees and personalized recommendations. The two main applications of machine learning in the advisory domain are:
Through its acquisition of Neurensic, Trading Technologies now has an AI platform that identifies complex trading patterns on a massive scale across multiple markets in real time, providing clients with a continuous compliance risk assessment.
GreenKey Technologies uses speech recognition and NLP to handle conversions, financial data, and notes about market trends and insights.
Kavout uses AI to recommend top stocks and forecast prices. Its Kai platform delivers a stock-ranking rating, referred to as its “K Score.”
PipeCandy eCommerce Leads & Insights for Fulfillment tracks company data and company shipping details: shipping volume, which companies they use to ship products, whether they ship internationally or not, and so on. You can find company leads easily with this dataset with filter capabilities.
Wikiroutes Transit Data provides public transport information—routes, stop points, and more—via crowd-sourcing. The data is constantly updated and can be easily converted and integrated into your own software system.
Wikiroute’s Transit Data is used by individuals, private companies, and government agencies of all types and sizes.
TrackStar’s Predictive Credit Technology uses fifteen years of financial dispute data to create predictive models of future borrowing potential. With this data and AI technology, your bank or other lending company can mitigate the risk of fraud, improve existing customer relations, and reduce your operating costs.