Applications of Finance Build Using Arificial Intelligence Techniques

Webtunix Blog

Today, machine learning plays an important role in the sector of financial ecosystem, from approving loans, to handling assets, to measuring risks. Yet, there are some technically-savvy specialists have a precise view that how machine learning is helpful to in daily financially lives.  

Arificial Intelligence has well productive applications in finance before the advent of mobile banking apps, capable chat bots and search engines. Few industries are well suited for artificial intelligence giving high volume, accurate historical records, and measurable nature of the finance world. Furthermore, there are more uses cases of machine learning in finance, a movement preserved by more accessible computing power and more accessible machine learning tools such as Google’s Tensorflow. There are various AI based companies which provide excellent services in Finance. Let’s discuss main applications of machine learning in finance:

  1. Stock Prediction: Stock market prediction has been an essential matter in the field of finance, business and mathematics due to its potential economic gain. As a massive volume of capital is traded through the stock market, the stock-market is perceived as an outlet of peak investment. Nowadays researchers are trying to prove the predictability of the financial market. From this time, Stock Market prediction is an emerging topic for researchers. Few Data Science Companies are working on stock prediction these days. Supervised learning model techniques of machine learning are used in predicting stock price trend of a single stock, development of trading strategy logic and deep functionality of stocks to understand stock market better. Machine learning optimizes trading firms strategies thus predict movement of stock portfolio by analyzing stock candles and its mountain.
  2. Fraud Prevention: Financial service suppliers have no better concern than defending their clients against fraudulent action. Financial fraud costs Americans, alone, $50 billion annually. It is not good enough to handle clients’ accounts in old manners. With every advancement in data security, criminals have stepped up to the challenge. To protect clients’ data against progressively sophisticated threats, organizations and companies must stay one step ahead of hackers. Machine learning enables applications to prevent security breaches by out-thinking the criminals. In order for machine learning to be operative, it must be able to quickly access and digest large amounts of data. Understanding the cost of machine learning, Amazon, Microsoft, IBM, and Google are each mixing machine learning abilities into their cloud-based developer borders. As criminals become more progressive in their plans, only computer systems with access to big data and with the capability to think and learn will be able to stop them.
  3. Customer Service: Chat bots and conversational interfaces are a fast growing area of investment and customer service budget as the highest promising short-term. Companies like Kasisto are now building finance-specific chat bots to assist consumers to ask queries via chat such as “How much did I spend on shopping last year?” and “What was the balance of my personal savings account 15 days ago?” These assistants have had to be built with robust natural language processing engines as well as reams of finance-specific customer interactions.
  4. Sentiment Analysis: The stock market exchanges in response to myriad human-related factors that have nothing to do with ticker symbols, and the expectation is that machine learning will be able to copy and enhance human “intuition” of commercial activity by learning new trends and telling signals.
  5. Recommendation of financial products: A robo-advisor might recommend portfolio changes, and there are number of insurance recommendation sites this force use some degree of AI to counsel a specific car or home insurance plan. In the future, progressively personalized and calibrated apps and personal helpers may be supposed as more dependable, impartial, and dependable than in-person advisors.
  6. Portfolio Management: Five year ago, robo-advisor was unknown to everyone. Thus robo-advisors are algorithms built to regulate a financial portfolio to the goals and risk tolerance of the this process, user enter their goals, age, income, current financial assets. The advisor then spreads investments across asset classes and financial instruments in order to reach the user’s goals. Robo-advisors have increased important traction with millennial buyers who don’t want a corporal consultant to texture relaxed capitalizing, and who are fewer able to authenticate the fees paid to human advisors.

In nutshell, we can say that, there are a number of sites which host ML competitions. These competitions although not specifically targeted towards the artificial Intelligence applications in trading, can stretch good contact to quants and traders to different ML problems via participation in competitions & forums and help expand their ML knowledge. Furthermore, Machine learning techniques are pragmatic in various markets like equities, derivative, Forex, etc. Machine learning enthusiast/Quants/Traders who intend to apply machine learning techniques to trading should also have some know-how on related subjects like Programming, Basic statistics, Market microstructure, Sentiment analysis, Technical analysis etc.