Despite the fact that Artificial intelligence in finance is still a relatively young phenomenon, the subject is advancing rapidly. It seems as if there is some new discovery every day, whether it is a research article outlining an enhanced machine learning method or a new library for one of the most popular programming languages.
Fintech uses artificial intelligence
To begin, we’ll go through some of the important sectors of the financial industry where artificial intelligence is having the most influence and adds the most value above conventional ways.
It’s all about credit score
When it comes to the financial sector, credit scoring is a critical use of machine learning. Financial institutions, big and small, maybe found lending money to customers. It is necessary to appropriately analyze the creditworthiness of a person or a corporation in order to do so.
After conducting an interview with a person and obtaining pertinent data points, analysts used to make these kinds of choices. A prospective borrower’s eligibility for a loan may now be assessed using a more complicated set of criteria thanks to artificial intelligence. It’s done by using a wide range of criteria (such as demographics and income and savings and prior credit history) to arrive at a final score that decides whether the individual will get a loan or not. It is possible to make fair judgments using AI-based scoring systems since the bank employee’s attitude on a specific day or other circumstances do not influence the decision-making process. People with little or no credit history may be able to use this method to establish their creditworthiness and capacity to pay back the loan.
Prevention of fraud
Fraud protection is another area where machine learning may have a significant influence. Our definition of “fraud” includes anything from credit card fraud to money laundering. In recent years, e-commerce, online transactions, and third-party integrations have led to dramatic growth in the latter. In the past, companies employed hardcoded regulations created by specialists in the field to combat fraud. Fraudsters, on the other hand, maybe able to abuse the system if they learn the rules. That’s not the case with Artificial intelligence in finance and AI-based systems, which may change and adapt to new data patterns over time.
Machine learning algorithms that specialize in anomaly detection and can identify fraudulent transactions are plentiful. If a transaction-related characteristic (such as the client’s prior behavior or location or spending habits, for example) looks out of place, a warning may be sent by such an algorithm. The machine learning industry is continually striving to improve, even if many conventional approaches like logistic regression, support vector machines and decision trees can already provide respectable results.
In part, this is due to increasingly complicated algorithms that are better suited to handling massive amounts of data (both the number of observations and potential features). Deep Neural Networks thrive in fraud detection because of their ability to deal with unstructured data and discover patterns without any feature engineering. XGBoost and LightGBM, two Kaggle competition winners, are examples of this.