How AI is Learning the Language of Finance?:
Artificial intelligence (AI) is about to bring big changes and benefits, especially for the financial sector. There is speculation in the media that with the arrival of AI machines they will replace humans and have nothing to do. The fact is, however, that AI will not automate most financial professions, but rather complement human intelligence.
Google recently launched the People + AI Research Initiative (PAIR) to further explore these interactions with support from MIT researchers. The financial sector has also understood what power and potential AI can offer. A 2016 Ernst & Young report identified 5,000 FinTech companies, many of which are introducing AI to the banking world.
Easily accessible computing power and machine learning tools have fueled machine learning cases in the financial sector. Artificial intelligence is particularly relevant in the areas of customer service, trading, post-trade operations such as reconciliations, transaction reporting, tax operations, and corporate risk management. Therefore, we can confidently expect that 34% of decision-making in the banking sector will be generated by machines and 66% by human judgment in the near future.
Natural language processing (NLP) is a subfield of artificial intelligence and is a particularly strong example of complementary intelligence applications. NLP is developing systems that can read and understand the languages spoken by people. The financial sector is a knowledge-intensive industry and much of it requires the reading and understanding of large amounts of information that is only partially structured. However, NLP makes the processes much easier, faster, and more accurate with less effort than humans could.
Machine learning is revolutionizing processes in financial institutions and testing some of the old business models. Wealth managers use deep learning solutions for long-term value investments, advisors are being replaced by chatbots that cover up to 95% of all queries, and companies like PayPal can reduce fraud losses across the industry if they research largely with artificial neural scanning. network.
Some fast-growing personal finance programs already have machine learning, and many large financial services companies are experimenting and testing.
Personal financial management in machine learning is still in its infancy, but the potential is great: replacing bank employees or financial advisors with algorithm-based chatbots; fully automated investment services that can move your money to global markets in seconds from financial news; or the ability to instantly access loans, based only on the data you have already shared online, without realizing it.
The term “Robo Advisor”, which was not heard five years ago, is now a well-known and common word in the financial world. Robo Advisors (companies like Betterment, Wealthfront, and others) are algorithms created to align a financial portfolio with a user’s risk and tolerance goals. Users enter their goals (for example, retire at age 65 with a savings of $ 250,000), age, income, and current assets. The advisor (or ‘allocator’) allocates investments between asset classes and financial instruments to achieve the user’s objectives. Therefore, the system adapts to the user’s changing goals and real-time changes in the market, always aiming to find the best solution for the user’s original goals. Robo advisors have become more prominent among Generation Y consumers who are comfortable investing without a physical advisor and who are less able to ratify commissions paid to human advisors.
Algorithmic trading (also called “Automated Trading Systems”) originated in the 1970s and used complex artificial intelligence systems to make extremely fast trading decisions. Algorithmic systems often execute thousands or millions of trades per day, which is why the term “high-frequency trading” (HFT) is used, which is considered a subset of algorithmic trading. While most hedge funds and financial institutions do not disclose their AI approach to trading, machine learning and deep learning are believed to play a more important role in calibrating real-time trading decisions.
Today the internet has become simpler and more accessible and very valuable business data is stored online. It poses a threat and is a perfect scenario for data security risk. Previously, financial fraud detection systems were governed by a complex and robust set of rules, but modern fraud detection actively learns and adapts to new potential (or actual) security threats. This is where machine learning comes in handy in the financial world when it comes to detecting fraud. Machine learning detects and reports unique activities or behavior (“anomalies”) to security personnel. With the increasing forms of security breaches today, true “learning” systems will be a necessity in the near future.
Taking out loans/insurance
Underwriting is the perfect job for machine learning in finance and maybe the only job where machine learning threatens people. Especially in large companies (major banks and publicly-traded insurance companies), machine learning algorithms can be trained on millions of sample consumer data and financial or insurance loan results. Algorithms can detect and assess underlying trends and also detect trends that could affect loans and insurance in the future (are younger people in a certain condition affected by road accidents? Increasing the number of norms among a specific population) Demographics in the last 15 years? ?) These results, until now reserved for larger companies with available resources, can be of huge benefit to a company and change its profits.