Machine learning is a subset of AI technology that develops complex algorithms based on mathematical models and data training to make predictions whenever new data is supplied to it for comparison. Artificial intelligence is the ability of machines to simulate neural networks and human intelligence through machine learning courses without the use of any human intervention or explicit programming.
Though these two concepts that always go together have been around for ages, the past two decades have seen a phenomenal rise and exploitation of benefits of ML applications.
Let us explore some applications in real-life in the financial services area where they have made huge differences in customer service, fraud and risk management, and last but not least personal finance.
Examples in Customer Service:
Chatbots are the latest feature of financial services being deployed to aid and automate and reply when asked frequently asked questions, common customer service answers and requests, help in bill payments, provide information on services and products and more.
Since they work with NLP-natural language processing they understand the query and answer appropriately. But there are instances when the scenario does not fit the scripted questions and the conversation is beyond their comprehension.
ML is important to teach the chatbots in customer service to assimilate data from interactions where the AI can self-learn how to respond in the future based on the experience they gather. Obviously more the interactions, the better they get.
They are also capable of recognizing emotions like frustration, anger and so on where they can diffuse the tensions by transferring to a live customer service agent for further help or resolution. Often they up-sell products, introduce the newer services and help in transactions like making automated payments.
During the course of such interactions, they can also pick up customer behavior trends like the possibility of defaults due to cash-flows. Imagine how satisfied a customer would be when it is the due date for payment, the account is bereft of money and the chatbot work efficiently offers a different due date, a short-term loan or a customized payment plan.
That’s just a small example of the chatbot and its machine learning courses enriching the customer or user experience.
Examples in Personal Finance:
ML comes to the aid of financial institutions by specializing in the service of customers needing applications for budget management, offering guidance and highly targeted financial advice. Such apps are made for mobile devices and allow their clients to track their daily spending.
Using their innate ability to spot trends they can help with budgeting, saving and investment decisions and plans by watching and learning from the client’s spending and purchase patterns.
Ina real-life example a leading bank spotted the trend of people from a certain segment facing problems with their cash flow and using their credit cards for late-night transactions and withdrawals. By flagging such abnormal behaviour it was found that the segment faced unduly low-interest rates in their savings accounts. Based on such foresight the bank not only improved its savings rates but it also offered the segment increased credit limits to restrict defaults on payments.
ML intelligence worked very well since the bank retained its customers with such an offer and also saw an increase in its savings accounts deposits.
Examples in Fraud and Risk Management:
In the fields of risk and fraud management the daily number of transactions to be scanned, are very large and involve huge sums of money. In modern times online payments have emerged as an ideal spot for fraud perpetration. Paypal the market leaders, have employed machine learning courses specializing in risk management and fraud detection and using Big Data, complex neural networks, and deep learning capabilities. Any abnormal behavior is flagged and forms a sandboxed risk queue within milliseconds.
The cybersecurity challenges are confrontable by smart ML algorithms. The detection of phishing attacks is dependent on the algorithm being able to easily compare the original and fake sites for logos, visual images, and site components. T
hey can also detect unusual behavior once they are trained on recognizing normal patterns on a profile or account. A red flag is immediately raised and the user is asked to verify the transaction.ML is also used in risk scoring, assessing defaults in payments, automating credit scores and compliance issues, assessing loan applications and every transaction in between.
Machine learning is not restricted to any one field. However, the applications can get very complex and extend far beyond these few examples. ML helps in better security, increasing operational efficiency and delivering better customer service or user experience.
If you would like to learn more, then do the machine learning courses at the Imarticus Learning Institute where technologies of tomorrow are taught and skilled for today.