Machine learning in finance has become more noticeable today due to the availability of substantial amounts of data and advanced computing power. Machine learning in finance is transforming the financial services industry than ever before. Leading banks and financial services companies are deploying AI technology, including machine learning (ML), to streamline their processes, optimize portfolios, decrease risk, and underwrite loans, amongst other things.
Why machine learning is suitable for finance?
So, why machine learning is widely used in finance? Is machine learning suitable for finance? There are few reasons to explain those questions by Algorithm-XLab’s article.
1. Machine learning in finance is the utilization of a variety of techniques to intelligently handle large and complex volumes of information.
2. Machine learning excels at handling large and complex volumes of data, something the finance industry has in excess of.
3. Due to the high volume of historical financial data generated in the industry, Machine learning has found many useful applications in finance.
Use Case: Fraud Detection
Let’s take a look at a real application case, one of the most popular cases using machine learning in finance. Fraud is a serious issue for financial institutions today. According to the 2019 FTC report, credit card fraud has been steadily increasing for the last five years. That's why machine learning is necessary and actively leveraged to finance industry today.
Why should we use machine learning in fraud detection?
Machine learning is ideally suited to combating fraudulent financial transactions.
Speed:
Machine learning can evaluate enormous numbers of transactions in real-time. It can scan through vast data sets, detect unusual activities, and flag them instantly. It is continuously analyzing and processing new data. Moreover, an advanced model such as neural networks autonomously updating its models to reflect the latest trends.
Scale:
Machine learning becomes more effective in incremental data set. The machine learning model can effectively distinguish the differences and similarities between multiple transactions. Once the machine learning model learns which transactions are genuine and which are fraudulent, the systems can decide and predict them when dealing with new transactions.
Efficiency:
Contrary to humans, machines can perform repetitive tasks tirelessly. Furthermore, machine learning does the dirty work of data analysis and only escalate decisions to humans when their input adds insights.
If you're interested in how fraud detection function could be built using Machine learning, check this out!
