With the burgeoning growth of electronic financial transactions and their relative anonymity, sophisticated fraud schemes have exploded in the last 5 years, as reported by the FBI and leading publications such as the Wall Street Journal, Forbes and the New York Times. As reported by CNBC, over 16 Billion dollars was lost to identity theft fraud alone. However, savvy CFOs are using Machine Learning to detect these fraud schemes and thwart these criminals.
For example, California’s Franchise Tax Board (FTB) has leveraged machine learning to apprehend fraudulent tax preparers. Another company, VSP, has identified crooked vision care providers who were submitting inflated or outright false claims by using machine learning.
So, what is machine learning? To put it simply, it is a subset of data science that uses statistical models to draw insights and make predictions. The magic about machine learning solutions is that they learn from experience without being explicitly programme. Machine learning excels in digesting large amounts of data and learning from that data in how to carry out a specific task, such as distinguishing fraudulent financial transactions from authentic transactions.
Machine learning is ideally suitable in finance, because of its ability to utilize a variety of techniques to intelligently handle large and complex volumes of information, something the finance industry has in excess of!
Due to the high volume of historical financial data generated in the industry, machine learning has found many useful applications in finance. The technology has come to play an integral role in many phases of the financial ecosystem, from assessing risk and detecting fraud, to approving loans and carrying out credit scores, to managing assets. Some of the current applications of machine learning in finance are:
- Fraud Detection
- Risk Management
- Document Analysis
- Money-Laundering Prevention
- Chatbots, etc.
