7 Key Applications of Machine Learning in Finance Industry
The world of finance still has a long way to go in terms of machine learning adoption, the industry has transformed significantly with the rise of technological advancement.
Technological innovation is transforming the face of various sectors across the world and the financial sector is no exception. Among the various technological changes happening across the world, machine learning promises a lot of benefits in the financial sector. Though the world of finance still has a long way to go in terms of machine learning adoption, the industry has transformed significantly with the rise of technology. Many financial industries have already started using high-level innovations like Artificial Intelligence (AI) to improve their business.
Before we understand the applications of machine learning in finance, let’s find out what exactly is machine learning. In simple terms, machine learning refers to the idea that computer programs can learn on their own and adapt to changes based on their experience. This creates a lot of new opportunities for companies in the financial sector in the fields of customer service, data security, risk mitigation, data interpretation, etc. Let’s check out some of the key applications of machine learning in the financial sector that have great potential to expand in the future.
1. Customer Service
Customer service is the backbone of any service industry. When it comes to finance, customers expect fast solutions to their problems. Moreover, customers also demand accurate information about various products. This is one of the segments where machine learning is expected to have a major impact in the future. Many companies already have AI chatbots to resolve certain queries from consumers. However, these chatbots have very limited functions now as they are programmed only to follow a specific set of instructions. With machine learning, these chatbots can evolve and provide a better interactive experience for customers.
2. Insurance Underwriting
Insurance companies take various factors into account before deciding whether to provide coverage to an individual. Some of these factors include age, income, credit score, health condition, location, etc. Moreover, most insurance companies set premiums to individuals based on these risk factors. The sheer volume of these data points makes the underwriting process overwhelming to do on a manual level. Machine learning uses historical patterns to understand risks and executes underwriting tasks in an accurate manner. Most importantly, the risk predictions made with the help of machine learning can help companies improve their profits significantly.
3. Fraud Prevention
Various industries in the finance sector have long been the target of frauds. Frauds are very common when it comes to credit cards and bank accounts. Millions of people use these services on an everyday basis, and it is pretty much impossible to prevent fraud on a manual level. The complex algorithms used by machine learning can analyse patterns and detect frauds. Historical patterns used by machine learning can help determine whether a particular transaction is of a suspicious nature. This way, it is possible to prevent fraud on a real-time basis rather than wait for the crime to happen.
4. Digital Assistants
People have already started using digital assistants in the form of Apple’s Siri and Amazon’s Alexa. However, the potential for digital assistants is very significant in the financial sector. This technology uses speech recognition to help customers access the right services. With machine learning, programs can adapt to the specific needs of customers and help them with their preferences. Many technology experts believe that machine learning and artificial intelligence have the potential to make digital assistants provide assistance similar to that of a human being.
5. Process Automation
There are many industries in the financial sector that have repetitive tasks and intense manual work. Machine learning can automate these tasks and improve productivity in a great way. Automation tools for fintech companies such as paperless approval loans generate a massive volume of data and process them every day. By adding a layer of machine learning to this process, the efficiency of these operations can be improved. Moreover, process automation can improve customer experiences and minimise costs for various companies.
6. Investment Predictions
Many experts believe that the investment banking industry is likely to witness a major change with the introduction of machine learning. Traditional forms of market analysis are not very effective when it comes to predicting future market changes and potential risks. Machine learning uses existing data to come up with investment predictions for the future. Hence, fund managers who use machine learning can have an edge other fund managers in terms of coming up with market changes.
Marketing is one of the major areas of finance that is likely to witness a significant change in the future. A successful marketing campaign typically involves making predictions based on the past behaviour of consumers. Financial companies have plenty of data about various segments of customers in the market. With machine learning, it is now possible to track the purchases and spending patterns of customers. This helps companies develop a marketing campaign that could target these consumers in a much efficient manner.
Machine learning is not entirely a new concept. However, its potential in the financial sector is yet to be fully utilised. Most of the applicants listed above are almost impossible to perform on a manual level. Machine learning plays a major role in making data effective in various operations. Most industries in the financial sector have already understood the potential of machine learning experts, and they are trying to implement it on a large scale. In the future, it is likely that machine learning will disrupt various industries in the financial sector and bring a massive change.
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