As we all know the the reach of Machine Learning and various fields in which it can be used. There is one industry which still remains a notion and is clearly not untouched by the machine learning and predictive Analytics is Finance( Fin-tech). The big giants, big four and all other well known fin-tech companies have adopted the machine learning models and AI to be used for various aspects such as to detect the Fraudulent Activities, Customer Retention and acquisition based on the analysis of the data, investment strategies for the stocks and money market, and risk management. The demand for the machine learning and AI is huge as the same goes with the healthcare and finance industry. The giant companies like Fidelity Investments, Bank of America, Jp Morgan Chase , PWC and various other fin tech companies make usage of the machine learning models to detect the fraudulent activities, any risk involved in the financing, prediction algorithms for the investment analysis.
Some of the ways that Machine Learning can be more refined and used in the finance industry are as follows :-
- Algorithmic Trading
- Loan/Insurance Underwriting
- Portfolio Management
- Fraud detection and prevention
- Customer Service
- Sentiment Analysis
- Sales/ Recommendation for the Financial Products
- Network Security
Now lets see an example in a simple way to understand exactly how the Machine learning is adopted by the Fin-tech industry to improve their efficiency and provide better services to the customer. Below is an example :-
Credit card fraud detection and Prevention is a challenge for any financial institution and is vital to a bank’s reputation and profit margin. Luckily, fraud prevention is the perfect area for Machine Learning to help. A bank has an enormous amount of data that can be analysed by ML algorithms: spending habits, location, and client behavior. Machine Learning can instantly make a conclusion if something looks suspicious and alert the cardholder. This level of precision is impossible for a human employee, because hundreds of transactions must be analysed in real-time. Moreover, an algorithm is less likely to make an error. When an anomaly is detected, the system could instantly require more information from the client or block the transaction. Banks can catch fraud as it happens by implementing ML.
Unlike a human agent, the algorithm is able to quickly weigh the transaction details against thousands of data points and make a determination whether or not the attempted activity is uncharacteristic of the account owner. And unlike non-AI software, machine learning programs learn from each action the account owner takes, and from each decision the software makes. Over time, the algorithms adjust themselves in response to changing habits on the part of the account owner.