Proper application of machine learning (ML) to financial industry can highly improve development of custom solutions and help in improving security. Machine learning solutions enable spotting costly errors, improving efficiency, and augmenting the decision-making processes. All the benefits of machine learning in the financial sector make this technology a true game-changer.
Nowadays, machine learning plays an important role in multiple processes and aspects of the financial systems, starting at approving loans and managing assets, and ending at assessing risks. Below, we are listing examples of how machine learning can be actively used in global financial environment.
1. Algorithmic trading
The largest financial institutions in the world need to make thousands trades daily. Human employees are simply not able to collect and analyze all the necessary data for the trading purposes. It is where MI comes in handy. Even though, the most important decisions are not being made exclusively by ML, this technology plays a highly important role in assessment of the real-time trading decisions.
2. Security
Users’ security in finance, banking, and investment scenery is undoubtedly of higher importance than in other sectors. Apart from the anomaly-detection applications, security measures such as facial or voice recognition or use of biometric data are becoming more and more popular among top-of-the-line security solutions. In many instances, these applications are backed up by machine learning algorithms.
3. Loan underwriting
Underwriting is a task in finance which proves machine learning efficiency and aptitude for the sector. The machine learning algorithms can perform automated tasks like matching data records, looking for exceptions, and, what’s particularly interesting, calculating whether an applicant qualifies for a loan or insurance. In other words, machine learning applications can predict very accurately an applicant’s ability to pay back their loan.
4. Stock market analysis
It is commonly known that stock market is influenced by many human-related factors which are not necessarily associated with the current economic situation. When that happens, machine learning is able to enhance and replicate intuition which drives human financial activity by studying trends and not obvious signals.
5. Fraud prevention
Since old-fashioned, check list-based fraud prevention systems turned out to be ineffective, new solution had to be found out. Modern fraud detection systems actively learn and calibrate new potential security threats. With use of machine learning technology, all suspicious activities and behaviors are detected and flagged to be checked by security teams.
In fact, one of the greatest responsibilities of financial companies is protection of their customers against fraudulent activity. This is where machine learning makes a case for itself. Machine learning algorithms compare each transaction with account history in order to assess whether given transaction could be fraudulent. Unusual activities which may include huge cash withdrawals or out-of-state purchases increase vigilance and may result in delay of the transaction. Once this measure is taken, human employees make final decision whether to finish the given transaction or not
6. Network Security
Safety of professional network is equally important as the protection of consumer data. All data security professionals should be able to recognize suspicious patterns that occur in their network. As this task can sometimes be challenging, it is a good idea to make use of machine learning. Pattern analysis abilities combined with big data capabilities give ML the significant advantage over other solutions.
Machine learning is used on daily basis in majority of industries, including various financial sectors. Thanks to this technology we are able to solve complex problems and secure data. Even though ML has already proved itself to be revolutionary, we believe that in the near future it will prove itself many more times. Visit https://addepto.com/machine-learning-consulting/ for more information.
