The quantitative nature of the financial domain and massive availability of data sets is well-suited for ML to enhance numerous aspects of the financial industries.
FREMONT, CA: Machine learning (ML) and its use cases are generally attributed to collecting suitable datasets, building efficient infrastructures, and applying the right algorithms. However, of late, ML is digging deep inroads in the financial service industry too. Let’s take a glimpse on how financial companies can implement ML solutions.
With technology shaping every nook and corners of everyday life, it’s difficult to imagine the future of financial services without the incorporation of ML. However, most financial services companies are hesitant to gain real value from technology for the following reasons:
•An unrealistic expectation from machine language and its importance to the organizations.
• Expensive R&D in machine learning
• Lack of DS/ML engineers in another major limitation.
• Financial institutions are not responsive enough to update data infrastructure in an agile way.
Purpose of ML in Finance
Several financial companies are interested in ML driven applications. Here are the reasons why they are so keen to invest in such a technology that poses them with many challenges:
•Inexpensive operational costs due to process automation
• High revenues, owing to enhanced user experience and better productivity.
• Reinforced security and improved compliance.
Moreover, several open-source ML algorithms and tools complement financial data. The quantitative nature of the financial domain and massive availability of data sets is well-suited for ML to enhance numerous aspects of the financial industries. The cumulative effect of such advantages is forcing the financial institutions to spend on ML.
Applications of ML in Finance
Process automation is aimed at eliminating the redundant manual work with automated systems to increase productivity and to engage the human resources toward productive tasks and business goals.
The automation use cases of ML in finance include call-center automation, Chatbots, Employee training, and Paperwork automation.
With ever-growing users, transactions, and third-party integrations, there has been a parallel increase in threats in finance too. With the incorporation of ML, it is getting easier to detect frauds. The algorithm analyzes each action of a cardholder and compares it against the trends to evaluate whether the attempted activity is a characteristic of the particular user.
ML enables better decision making with the help of its algorithmic trading. A mathematical model analyzes real-time news and trade results and seeks out the patterns that can influence the stock prices to rise. It can also proactively hold, sell, or buy stocks as per the predictions.