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By Nadia Zaifulizan.

Machine Learning has been one of the biggest game-changer in the financial world. As more and more companies adopt Machine Learning technologies to prepare themselves for the market demands in the coming years, we explore the top 5 Machine Learning functions in the financial sector.

1.Trading and Stock exchange predictions

Machine Learning has been the main engine behind many stock exchange predictions. Based on the collection of previous years’ stock data, Machine Learning can forecast the stock’s expected performance for the following month or year. It can also predict the chances that a corporation’s stock has for recovering losses.

2.Finance audit and monitoring

Expense reports are submitted by departments for the company to monitor the costs incurred in their common business operations. This is especially relevant for expenses of client-related business travels covered by the company. Companies like JPMorgan Chase & Co. recently applied the machine learning audit, to ensure that data from their travel and expense reports are aligned with their policies. Using Machine Learning, the reports are processed and analyzed for a more efficient and transparent expense monitoring.

3.Security and Fraud detection

One of the major concerns of banking and corporate finance is security and fraud detection. Machine Learning solutions assist financial services institutions by identifying possible fraud risks from transactions and alerting the institution. Extensive training of a Machine Learning model enables easier identification of features associated with potential fraudulent transactions.  This enables easier fraud prediction and detection.

4.Process Automation

Some financial processes can now be automated to save time and reduce errors. This includes the review of commercial credit agreements, user support services, and customer queries. Some corporation use the Machine Learning engine to directly review agreements, while others use automated chatbots and emails which streamline the response process. Although the mechanism of automation seems simple, the addition of Machine Learning in these automated processes ensures that the interactions received by the machine gradually improve the automated processes and responses.

5.Credit scoring and Banking marketing

Banks have many opportunities to obtain data from its customers and investors. Underwriting and credit scoring requires large number of data history to determine and identify their payment behavior. This helps the bank in their operational decision-making process. Using data, banks can also apply Machine Learning models to create targeted upselling and cross selling campaigns, based on the customers’ demography, socio-economic profile, and predicted preferences. The Machine Learning model effectively form campaigns and automatically markets the bank’s services in the most preferred form by the targeted customers.

There are many more applications of Machine Learning in the financial sector. Although the main purpose is to increase efficiency and reducing errors, finance workers can also benefit from the reduced menial workload and focus more on work that requires their expertise.

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