Application of Machine Learning in Finance Sector
How Machine Learning Is Used In Finance
Peoples who have studied Finance in business school might know why Finance is important and how it is affecting the globe. But the question here is what the use of Finance in Machine Learning and how we can take out the best of it?
Everyone knows Machine Learning is a branch of Artificial Intelligence, with the use of algorithms computer performance become better while performing some task. Basically, the computer learns from its experience without being programmed again and again or any human interaction.
There is some Use of Machine Learning in finance
Security threats in banks/finance are increasing as with the growing numbers of accounts, transaction and third-party integrations. And as being a safeguard Machine Learning algorithms detecting the frauds/ threats. Banks can track every transaction parameters by using this technology. Machine learning Algorithm inspects each action of the users. Machine Learning Algorithm needs only a few seconds to assess transaction. The speed helps to prevent frauds in real-time.
Managing the risk
Machine Learning can analyze the huge chunk of data; it helps in preventing fraud investors from securing loans. T checks whether the user has multiple accounts and his financial status. This basically has to be done by the investment anger and bankers, but it is not possible for them to check such minute details and only covered the static portion of the application.
There are two major applications of machine learning in the advisory domain.
Portfolio management: - it is an online wealth management service that uses algorithms and statistics to manage, allocate and optimize clients assets. Based on their risk preference and desired goals robot-advisor allocates the current assets across investment opportunities.
Recommendation of financial products: - to recommend personalized insurance plan to the particular user many online insurance services use robot-advisors. It saves money, as well as its personalized, that is why customer goes for robot-advisor rather than financial advisors.
The financial organization reduces their customer supports workload by having the bot to answer the FAQ and other queries. The bot is able to handle millions of queries and it doesn’t need any rest throughout the year. Therefore Chatbots provide a good opportunity for small companies to reduce expenses and help in the growth of revenue of the company.
Machine learning can analyze the big amount of data, whether it would be customer financial status or market changes, upcoming trends etc. Machine learning algorithms use the historical data of the company like balance sheet, profit and loss statements etc., and can predict the future of the company.
A mathematical model monitors the news and trade results in real time and forecasts the stock price to go up and down. Machine Learning Algorithm can analyze data sources simultaneously, something that human traders cannot possibly achieve.
Many Finance organization has already being used Machine Learning and become more commonplace with time. To succeed, finance leaders need to be thoughtful and methodical about their approach to adoption. According to Tucker, these technologies provide the most value only when they are in the context of a larger strategy. Automation should not simply be about alleviating costs. Instead, business leaders should think about using these resources in light of a future vision.
“Technology by itself, it’s like having a hammer that sits on the shelf. And you can use it really bad or you cannot use it at all, but if you actually have a plan to build a house, the results can be phenomenal,” says Tucker.