The Present and Future State of Machine Learning in Finance
If you are of opinion that seeing, grasping and hearing is the most coveted quality of only human beings, then probably you are not very familiar with machine learning yet.
Yes, we don’t deny that you would have come across the term machine learning but you are still confused about its working and application. The interesting thing is that computers are also “gaining the ability” to see, learn and grasp just like us. And this happens through machine learning.
Machine learning which is a subset of artificial intelligence has assisted in handling complicated issues even in natural language processing. Of late, neural networks have turned out to be the most dynamic methods for learning tasks.
The Financial industry has also been Impacted by the Machine Learning Revolution.
Why Regard Machine Learning in Finance?
There are certain challenges for applying machine learning in finance. However, several financial companies have started to take advantage of machine learning. Some of the reasons for which financial services’ personnel should take machine learning seriously are:
- There is the advantage of reduced operational costs due to the process automation.
- There is a rise in revenue because of better productivity and rich user experiences
Current Application of Machine Learning in Finance
One of the prominent challenges that financial institutions encounter is safeguarding clients from fraudulent activities. Machine learning offers data security by surpassing the brain of the fraudsters. It astutely and sharply looks into every transaction, huge cash withdrawals etc,. Whenever needed it also prompts alerts.
It also has the ability to raise the number of steps to carry out an operation so that there is a delay in the transaction until the humans take a decision. When there is doubt the transaction is generally declined.
While the earlier financial fraud detection systems relied too much on a complex set of rules, modern fraud detection proactively learns and tackles new security threats. For example, with the help of machine learning, systems can find out unusual activities (anomalies) and flag them to the security team.
Offering Assistance in Trading Services
Investors gain from machine learning because they can carry out sound investment forecasts. The Role of a broker is reduced to some degree because the investors themselves can place the order and eventually sell it.
Taking Control of the Risk
Machine learning is reputed for evaluating huge chunks of data. Hence it assists in preventing fraud investors from getting loans. It verifies the financial status of the applicant and checks whether he/she has a lot of accounts. Thus a huge burden is taken away from investment managers and bankers because without the help of machine learning they would not have checked such small details.
Nowadays Robo advisors are creating a revolution in the financial industry. They have eliminated the need for humans by giving the optimum advice to the customer.
Users just have to key in their goals and the robo advisor will offer a list of basic finance and investment questions depending on their age and present assets, investment-risk tolerance, long-term objectives etc. The Robo advisor will customize a distinct investment for the person entering the details.
Going beyond passwords, there is enhanced security in the form of speech recognition and facial recognition.
Several companies are encountering the dilemma of investing a lot on augmenting information security. This is because they experience prominent issues that threaten their ability to develop. In this regard, machine learning can be applied to assist in enhancing organizational security.
For example, companies can leverage machine learning for the purpose of minimizing data breach cases by handling identify authentication and password authorization.
Algorithmic trading encompasses the application of complex AI systems to carry out very swift trading decisions. It is believed that both deep learning and machine learning are taking up a prominent role in gauging trading decisions in the practical scenario.
Borrowers want a hassle-free loan processing which would credit the amount to their bank account in a swift manner. Machine learning offers a high technology power to financial institutions to make lending swift.
Future of Machine Learning in Finance
Conversational mediums and chat bots are quickly gaining momentum in venture investment and customer service. These agents have to be formed with powerful natural language processing engines besides finance-related customer interactions.
These chatbots which answer customers’ questions by means of a conversation maintain details of customer’s financial transactions. The chat experience is not much of a practice today in banking. However, it can turn out to be a good option for several people in the future.
You may be familiar with the terms discussed above but the sentimental analysis is a relatively new field. When you ponder over AI you would not think about sentiment analysis. However, for the uninitiated, there’s a total domain of research applying AI to comprehend emotional responses to news, movies, restaurants etc.
Otherwise called as emotion AI, sentiment analysis assesses news from written text to understand and judge reactions. Now there is a belief that machine learning will be able to recreate and develop human intuition of financial activity by finding out new trends and giving signals.
Recommendations of Financial Products
In the future, drastically personalized and adjusted apps and personal assistants may be seen as reliable ones. For example, Amazon and Netflix nowadays recommend books and movies with the same intuition of a living human expert. In the same way, conversations with financial assistants will give similar assistance financial products.
If you are interested in diving into the essentials of machine learning, then it’s time to Enroll with Softlogic, the established software training institute.