Machine Learning & Finance is it the perfect blend?

Machine learning and the financial service industry might be the perfect match. The success of these models is based on the consumption of large datasets using appropriate algorithms. Because of the nature of data in financial companies, these technologies are a great fit for not only performing basic functions but also developing inroads in financial services. Let’s take a deeper look at machine learning and financial services.

 Important definitions

Machine learning is a kind of data science. With statistical models, ML can make predictions or draw insights based on the data fed. Because of data models set, they can learn and make adjustments without being programmed to. This constant evolutional growth makes ML models great for real-life analyses.

These ML models need only be trained with already existent data and then once they are considered ‘well trained’ they are then applied to real-life situations.

Why finance is perfect for machine learning

ML models are developed to provide results based on their training. They are often retrained and kept up to date to maintain their sharpness and efficiency. These models improve as you continue to feed them more data. For this reason, ML models are perfect for financial services. The field of finance has huge datasets based on various activities, ideal instances to use Machine Learning Models. 

Some promising machine learning applications

Machine learning has the potential to automate processes and reduce automation costs, increase revenue due to increased productivity and improved user experience, and improve compliance and reinforced security. Take a look at the 5 applications of ML in finance:

Process automation     

  Automation is the basic goal for many AI or computer-based systems. The idea is to take advantage of these technologies to replace manual tasks and automate repetitive ones. This, therefore, increases productivity and cuts down on the time spent doing basic tasks. With such tasks automated, the company can focus on improving overall customer experience and other services. Some instances of process automation might be call center or paperwork automation or even gamifying training so you can eliminate the redundancy or waste that comes with using their employees as trainers.

Security

Nothing trumps security in the matter of finances. Because of the trust basis, both consumer and service provider wants to make sure their finances are safeguarded. However, the issue of security is becoming more and more volatile with increasing users, transactions and other elements. ML algorithms are great when it comes to detecting fraud and may be instrumental in maintaining secure environments even with the influx in transactions or uses. Such systems monitor actions taken by the consumer and spot any fraudulent behavior. They usually prompt for additional identification in light of suspicious behavior to validate any transactions.

In addition to reporting any odd activity, these systems can be used to monitor finances and detect any large payments that are indicative of illegal activities like money laundering. 

Underwriting and credit scoring

With customer profiles and numerous data from each customer, ML algorithms have the capacity to develop underwriting and credit scoring in real life situations. The machines rely on historical data and use these entries in scoring. Such scores are useful to human employees and help work faster and accurately. Apart from historical data, these machines can utilize data from other sources including telecommunication companies.

Algorithmic trading

Machine learning algorithms can monitor trades and establish patterns in real time markets or in the news. With such capacity, these machines can make proactive decisions based on their established patterns. This may be useful in making better trading decisions based on predictions. Because of the limitation of how much data humans can consume and analyze simultaneously, these machines have a predictive edge over human traders.

They may come in handy when human traders need an advantage over the market average and translate these minimal advantages into profits.

Robot advisory

Machine learning may come in handy by applying the algorithms to manage assets. With the right statistics, ML functions as an advisor in portfolio management. With relevant data from the user, the Robo advisor can allocate the assets investment opportunities based on the goals, risks, and preferences. Robo advisors may also be useful in providing recommendations for financial products. Again, using the individuals’ data, they can recommend personalized plans to fit the calibrations set. Robo advisors are additionally way less costly than human advisors.

Evidently, these applications of machine learning models in finance have the potential to greatly affect the finance service industry. Considering how elaborate each case works, we plan on doing a weekly analysis of each of these applications to fully understand how insightful ML models could be in finance based on these 5 methods.

Final word

Evolution of technology has brought with it models such as machine learning algorithms that have the potential to significantly change the financial services industry. Although some financial services are not prepared for these technologies, ML models are quickly becoming adaptable in various financial service sectors. While this is just the beginning, it might be hard to picture a financial services world without them.