AI is the new age revolution that we are seeing in all kinds of industries. The industry of finance is not any different and, it is seeing a rise in the use of AI and ML for its services.
AI is an abbreviation for Artificial Intelligence, which refers to the automation of the actions done by humans, which commonly requires human discernment and intelligence.
ML is an abbreviation for Machine Learning. Machine learning is a branch of AI that uses statistical models to make predictions. It is a subset of data science that gives the ability to learn and improve from the experiences without them being programmed.
It’s the 21st century and it is all about data. Data without benefit is a cost more than it is a help. Data storage is available but is not for free and, it has a cost to it. Machine learning enables us to gain that from the data.
Now, let’s talk about ML in the finance sector. Machine learning is a vital part of the financial services provided by finance companies. It is applicable in the sectors of managing assets, risk management, credit score rating, underwriting, and many more financial services. Machine learning is often used in detecting and preventing fraud, trading, portfolio management and, financial advisory services to the investors.
Large banks, insurance companies, and fintech are now employing AI/Ml in practically everything they do. There is a visible reduction in the reduction of costs and risks.
AI offers a lot when it comes to automation and OCR is one of them. Optical Character Recognition(OCR) helps in increasing the efficiency of mundane and time-consuming tasks that were typically handled by employees. Digitizing documents, processing forms, or extracting relevant information from documents are examples of OCR.
Machine learning proves to be accurate when large volumes of data are being fed to the system. These large volumes of data are processed and delivered much faster than the otherwise manual method.
Though ML has the benefit of handling data with efficacy, the drawback lies in the history of the data. The history of data about cancer would be still relevant to us but, the financial market is an ever-changing atmosphere and hence the history of data might or might not be relevant to us anymore. Now that we have seen both sides of the coin, let’s move forward to see the uses.
With that let us see some of the applications of ML in finance, namely:
Algorithm trading refers to the use of algorithms to make trade decisions and also risk management decisions. This application of ML is vital as it makes accurate predictions and decisions which are not affected by emotions. We humans somehow fail at this factor, which now is being taken care of by ML.
Fraud detection and prevention systems were outdated for the new fraudsters. So now the ML is being used which processes large data sets and identifies any unique activity and then flags them for further investigation. It works by comparing the data points like account history, IP address, location, etc. like a jigsaw puzzle is being solved.
Robo-advisors are the applications built by ML, that provide investors with financial advice. These applications also help in establishing a strong financial portfolio, according to the investors’ goals and risk appetite. They make for a cheaper portfolio manager than the humans.
The loan/insurance underwriting is being simplified through machine learning. ML can process large databases and make quick decisions on underwriting and credit scoring, which in turn saves the companies’ time and financial resources.
The AI-powered chatboxes are also an example of the ML in finance. They have made the process of customer servicing so fast and cheaper.
Artificial Intelligence has automated processes and has drastically reduced the cost of serving customers. It has also made financing extremely convenient to avail.
According to Forbes, 70% of finance firms are using ML to predict cash flows, adjust credit scores, detecting frauds. According to the latest Economic Intelligence Unit adoption study, 54% of financial services organisations with 5000+ employees have adopted AI/ML.
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