Why machine learning is suitable for the financial industry?

The financial markets of different nations have been the most early adopters if Machine Learning (ML) technology. Different investors, stakeholders and people have been able to spot the patterns within the financial and stock markets since the early 1980s. The success of machine learning technology within the financial market would depend on the efficient infrastructure, collection of suitable datasets and application of proper form of algorithms (Patel 260). With the latest forms of developments, machine learning have been able to several insights within the industry of financial services. The prediction of the different events based within the stock market is considered to be an important matter of concern due to the long term effects based on potential financial gains. Machine learning algorithms based within the financial sector would be able to enhance the quality of work within the financial sector.  

With the advanced methods of computing systems and the vast spread of the internet platforms, the stock markets have become much more accessible to the general public and also to the strategic investors. The machine learning technology could be defined as a subset of data science that employs different the use of statistical models for drawing different insights and making of predictions (Hagenau, Liebmann and Neumann 690). The data scientists would be able to train the models based on machine learning within the existing form of datasets. They would thus be able to apply the trained models within the real-life situations.

With the inclusion of more data within the computing systems, the results would be much more accurate. Enormous data sets would be a common form within the use in financial services industry. The financial sector deals with petabytes of data based on several kinds of transactions, transfers of money, customers, bills and many others. Hence, in this sector the machine learning technology would be the most suitable approach. With the evolvement of technical approaches, the algorithms have become much more open-sourced (Cavalcante et al. 201). It would thus be a difficult scenario to exclude the machine learning approaches within the models of financial sector.

Despite the different forms of challenges, many of the financial companies have taken the advantage of the ML technology. There are a variety of reasons based on the needs of machine learning technology within the financial industry. These reasons include:

• Increased form of revenues based on better productivity and enhance mode of user experiences.
• Reduced costs of operations based on better processes of automation.
• Reinforced form of security and better compliance.

There are a broad range of open-source based machine learning algorithms and several tools that would be able to meet with the growing demands of financial data (Delen et al. 1156). The established form of financial services provider based companies would have substantial amount of funds that these companies would be able to spend on the computing hardware. The machine learning technology is composed to enhance the aspects of financial ecosystem based on the larger volumes of historical data and quantitative nature of financial domain.

Applications of machine learning in the financial sector

The different forms of machine learning applications within the financial sector are:

• Automation of Processes– This could be defined as the most primary applications based on machine learning within the financial sector. This high level of technology would be able to replace the manual work of workers, increase the level of productivity and automate the repetitive tasks (Einav and Levin 19). Machine learning technology would be highly helpful for optimizing costs, scaling of services and improving the customer experiences. With the help of efficient form of algorithms, the machine learning algorithms would be able to manage the financial services in an automated manner that would be helpful for the customers and easy processing of transactions.
• Enhancement of Security Levels and Features – The different threats to security in the financial sector are increasing with the high level of transactions, greater number of users and integrations of third party users. The use of machine learning algorithms would be excellent approach base on the detection of frauds. The banks would be able to make use of the high level of machine learning technology based on monitoring the thousands of transactions in real time scenario (Dinh 1591, 1601). The algorithm is able to examine each of the action performed by the cardholder. This would also assess any kind of attempted activity. Financial monitoring could be considered as another form of security use cases based on machine learning technology that is primarily used in finances (Velamuri et al. 1340004). The different data scientists could be able to train the system in order to detect larger kind of micropayments and even flag the money laundering techniques.
• Credit scoring and Underwriting – The use of different forms of machine learning techniques would perfectly fit within the underwriting tasks that would be common in insurance and finance. The data scientists are able to train different kinds of models based on thousands of customer profiles. These would be possible with the hundreds of data entries based on each customer (Byanjankar, Heikkilä and Mezei). A well-trained machine learning system could be able to perform underwriting and tasks based on credit-scoring within real-time environments. Such kind of scoring engines would be helpful for human employees in order to work in a faster and accurate process.
• Algorithmic Trading – in algorithmic trading, the use of machine learning techniques would prove to be helpful in order to make the better decisions based on training. A mathematical model based on machine learning would be able to monitor the news and results of trade based on real-time scenario. This model would be able to detect patterns, which would be forcing the prices of stock to fluctuate (Hu et al. 549). The use of machine learning algorithms would be able to analyse the thousands of data sources in a simultaneous manner.
• Robo-advisory– The robotic advisors are considered to be most commonplace within the financial domain. There are two forms of major applications based on machine learning within the advisory domain of financial sector. Portfolio management could be considered as an online wealth management service that makes use of statistics and algorithms in order to allocate, optimize and manage the assets of the clients (Velamuri et al. 1340004). The robotic advisory would also be able to provide different kinds of recommendations based on financial products. Different kinds of online insurance services require robo-advisors for recommending personalized services for providing plans of insurance (Moro, Cortez and Rita 1321). Customers mainly choose robo-advisors over financial advisors due to their lower fees along with calibrated and personalized recommendations.

Conclusion 

Based on the discussion from the above report, it could be concluded that the use of machine learning approaches within the financial sector would be efficient for processing different kinds of tasks. Current forms of machine learning techniques would mainly be able to process the large level of transactions. Most of the projects based on machine learning would mainly be helpful for dealing with several kind of issues that have been addressed previously. Different kinds of tech giants such as Google, Microsoft, Amazon and IBM are able to sell machine learning software that would be used within the financial sector. This report also focuses on the use cases of machine learning algorithm within the financial sector. The machine learning algorithms should also be able to focus on the security mechanisms within the different kinds of transactions. Hence, the use of machine learning systems would be helpful for improved performance within the financial sector.

References

Byanjankar, Ajay, Markku Heikkilä, and Jozsef Mezei. “Predicting credit risk in peer-to-peer lending: A neural network approach.” Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 2015.

Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211.

Delen, Dursun, et al. “A comparative analysis of machine learning systems for measuring the impact of knowledge management practices.” Decision Support Systems 54.2 (2013): 1150-1160.

Dinh, Hoang T., et al. “A survey of mobile cloud computing: architecture, applications, and approaches.” Wireless communications and mobile computing 13.18 (2013): 1587-1611.

Einav, Liran, and Jonathan Levin. “The data revolution and economic analysis.” Innovation Policy and the Economy 14.1 (2014): 1-24.

Hagenau, Michael, Michael Liebmann, and Dirk Neumann. “Automated news reading: Stock price prediction based on financial news using context-capturing features.” Decision Support Systems 55.3 (2013): 685-697.

Hu, Yong, et al. “Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review.” Applied Soft Computing 36 (2015): 534-551.

Moro, Sérgio, Paulo Cortez, and Paulo Rita. “Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation.” Expert Systems with Applications 42.3 (2015): 1314-1324.

Patel, Jigar, et al. “Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques.” Expert Systems with Applications 42.1 (2015): 259-268.

Velamuri, Vivek K., et al. “Product service systems as a driver for business model innovation: lessons learned from the manufacturing industry.” International Journal of Innovation Management 17.01 (2013): 1340004.