ARTIFICIAL INTELLIGENCE IN B2B
Fintech is the use of technology in the financial industry. Whether handling financial products and services for individuals or corporations, the fintech industry depends heavily upon vast amounts of both quantitative and qualitative data. Some of the greatest challenges in fintech are accurately predicting how qualitative data will transform over time, and also how qualitative information will manifest in qualitative distinctions. This makes AI and fintech uniquely suited towards each other.
Artificial intelligence is used in financial services such as wealth management to better leverage the potential insight available through big data while reducing the labor required for the analysis. AI fintech also includes more general customer service applications that help individuals and companies make better financial and business decisions. Through the use of specialized fintech machine learning programs, financial firms can have a stronger understanding of market forces and help their customers maximize the positive impacts of their decisions, while also ensuring that they maximize the profitability of all their own customer and client relationships. Some examples of Artificial intelligence machine learning applications in fintech are below.
AI Machine Learning and Fintech
Natural language processing (NLP) is a form of AI that allows for the proper interpretation and effective production of human language. Financial firms can use specialized NLP programs to empower financial coach chatbots (aka virtual digital assistants) to answer specific questions from business leaders about their investments, their valuation, and the potential risks and rewards offered by different changes to their asset profiles. Financial coaches can also use artificial intelligence speech recognition to allow for audible conversations. Whether in text or in speech, virtual digital assistant chatbots for financial coaching can be beneficial for businesses to understand how to optimize their asset allocation and debts for greater productivity, profitability, and long-term growth.
Automated Contract Analysis
Fintech firms offer automated contract review and execution. This involves natural language processing along with machine learning governing how to look for common contract terms and identify any potential issues. While such analysis may be used sparingly for unique contracts, many contracts between businesses fall under relatively stable and routine patterns. Using AI to review and confirm the soundness of standard contracts based upon standards set by the business will reduce the amount of time spent on clerical tasks, and may even enhance the capacity to bring questionable clauses to the attention of business managers. JP Morgan was able to free around 360,000 hours spent annually on contract analysis through the use of fintech machine learning.
Financial Product Analysis
Fintech machine learning can also be used to analyze various financial assets such as stocks, bonds, hedge funds, private equity, real estate, options, futures, and others. Such assets have valuations that could fluctuate quite substantially over time, necessitating careful attention by buyers and sellers to maximize their returns on investment. AI fintech machine learning can be used to analyze changes in valuation in relationship to broader market variables and make predictions as to where prices may go in the future, to grant businesses better insights into their asset portfolio that may lead to more profitable management. Machine learning is quite suited for these purposes as artificial intelligence is made stronger as the amount of information increases, and information from financial markets is abundant. By predicting changes to asset valuations, AI can help fintech businesses improve their relationships with their clients and ensure that they take the strongest positions in the management of their finances, strengthening long-term relationships and helping establish a public reputation for effectiveness and reliability that will facilitate the acquisition of new clients.
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