Almost every company in the financial technology sector has already started using AI to save time, reduce costs, and add value. storing and accessing financial information combined with the maturation of tech capabilities are all in place to accelerate the digital transformation of accounting and finance. Actually, when machines take over repetitive, time-consuming and redundant tasks, it will free human finance professionals to do higher level and more lucrative analysis.
Financial services companies can use AI to detect brand sentiment from social media and text data, measure it, and transform it into actionable advice. Sentiment analysis assists with advanced classification of textual data (e.g., for compliance). These would be relatively novel applications of artificial intelligence, particularly in the arena of finance.
Investing: Offering investment insights
In the wealth management arena, B2C robo-advisors augment portfolio management and rebalancing decisions made by humans, often analyzing a person’s portfolio, risk tolerance, and previous investment decisions to offer advice. Companie’s intelligence-grade database provides traders with information on market trends around the globe. Also, other companies use AI to track account activity and help financial advisors customize the guidance they give investors.
Trading: Trading Better with algorithms
AI can help manage and augment rules and trading decisions, helping process the data and creating the algorithms managing trading rules. Some companies use algorithms to conduct trades autonomously, and the others, relies on AI robo-traders for high-frequency trading, to boost profits.
Lending: AI for credit lending
Machine Learning is a game-changing technology for lenders, lowering compliance and regulatory costs and helping with robust credit scoring and lending applications. some companies advance in AI derived from genomics and particle physics to provide lenders with nonlinear, dynamic models of credit risk which radically outperform traditional approaches. This can supplement young adults’ and self-employed professionals’ often thin credit history.
A study by the research firm Javelin Strategy found that false positives—legitimate transactions that are wrongly rejected, due to suspected fraud—account for $118 billions of dollars in annual losses for retailers, not to mention lost customers, who will often abandon the issuer of the erroneous decline. Machine Learning algorithms, Decision Intelligence technology analyze various data points to identify fraudulent transactions that human analysts might miss, while improving real-time approval accuracy and reducing false declines. Using Machine Learning to spot unusual patterns and improve general regulatory compliance workflows helps financial organizations be more efficient and accurate in their processes.
Image Recognition in FinTech
Recent advances in deep learning have increased image recognition accuracy to levels that surpass that of humans. Some companies’ services automatically authenticate consumer identity documents, and Onfido’s platform plugs into various publicly available databases to give employers quick identity verification and background checks for things like driving and criminal records. Banks can use AI technology to stay in compliance and identify fraud.
Artificial intelligence helps financial services companies make money by enhancing the accuracy of trading and by making wealth management more efficient.