Machine learning has been redefining how even the basics of operational tasks are done across industries. The financial industry is no different. While some of the applications of machine learning in finance are clearly visible to us - like mobile banking apps and chatbots, the technology is now being gradually used for drawing out accurate historical data of customers and predicting their future needs as well.
The current state of machine learning in finance
According to Bloomberg, only 16% of financial firms have incorporated any kind of machine learning into their investment strategies. On the other hand, the remaining are looking for ways to learn how to use machine learning in finance for fueling their investment strategies.
Although, you can see the adoption of machine learning in finance, increasing by the day. Right from approving loads to managing their client assets, calculating risks and more, the finance companies are training their employees to develop technical skills that will make their day to day tasks simpler.
Common applications of machine learning in finance
1. Portfolio management
Companies are using machine learning to calibrate a financial portfolio of their users. This includes their goals and risk tolerance, apart from other data, to predict and recommend the right investments for them.
For instance, if a user enters their age, income and current financial status, these machine learning fueled advisors will segment them into a portfolio. This enables the in-house marketing and sales teams to reach out to them with suggestions on where and how to invest for maximum savings.
Considering the amount of company data being stored online, data security is a huge challenge. The finance industry made use complex set of rules to detect fraud. But today, it goes beyond a basic checklist of risk factors. The systems powered by machine learning, are actively calibrating new security threats.
Using machine learning in finance, the systems can easily detect unusual activities or user behaviours, flagging them for the security teams.
3. High frequency algorithmic trading
Trading has become an investment option for many users today. With machine learning, financial firms can set up automated trading systems that use complex AI algorithms to enable users to make fast trading decisions.
Machine learning actively calibrates real time market data and predicts the future for a user, making it easy for the companies to often make thousands or millions of trades in a day. According to industry statistics, nearly 73% of everyday trading is done by machines! Learn more on algorithmic trading strategies with Experfy's online course.
4. Insurance and loan underwriting
Machine learning algorithms can be trained on thousands of aspects of consumer data. This includes basic demographics like age, job, marital status, etc to their monthly spends, financial assets, current investments and more.
The system can analyse the trends for each user to detect what might lead to them looking for a lender or an insurance in the near future. For instance, if the algorithm notices that you have purchased a new car after your last one was destroyed in a car accident, it will automatically categorise you as someone who is looking for car insurance now. This doesn’t just help financial firms in reaching their potential customers at the right time, but also ensures that the messaging is right.
As of today, the underwriting is a process that is manual in nature. Employees make use of the data provided to them by a customer to analyse and predict their needs, based only on their inputs. This often results in wrong prediction or not being able to understand the consumer’s need on a 1:1 basis.
The future of machine learning in finance
1. Efficient customer service
Businesses are now putting customer engagement on priority. The active interaction with their customers, doesn’t just improve their retention rates, but also helps them understand their changing concerns and needs. The perfect example of which is the AI executive that interacts with a customer almost ‘instantly’ - chatbots.
Customers can simply type in questions like ‘how much money did they spend in the last week’, ‘what their current balance is’ or ‘what investments should they be making’. The algorithm will then not just pull in their existing data to present to them, but also recommend the right investment options to them based on their trends.
2. Consumer security
Data security in finance is of utmost importance - to both the company and the consumer. With just about all your information being uploaded online, it is an increasing challenge to keep your usernames, passwords and security questions safe.
With machine learning, firms will able to develop security features like facial recognition, voice recognition or using biometric data to access financial accounts.
3. Wealth management
Portfolio management is already a used application of machine learning in finance. But the data still goes through manual resources to be able to recommend investments or trades to users. So the turnaround time often depends on the available human resources. But machine learning is already starting to address the issue.
Using machine learning, companies can create robo advisors. These are AI executives for the company that have studied a user’s goals, understood his saving and purchase trends, and are now able to ‘recommend’ investments or trades to them.
What’s more? The instant gratification in this case keeps the customers actively engaged with the company and increases the conversion rates at the same time.
4. Consumer sentiment analysis
Machine learning can help finance companies understand social media usage for every consumer, analyse current spend trends, keep track of new and upcoming trends and also predict the changes. This lets companies understand how the stock prices are going to change in the future and prepare for the upcoming trades. Since the stock market moves in response to human related factors, it is important for businesses to learn continually from the financial activity of users. Sentiment analysis can also completement current information on economic and financial developments.
When it comes to finance, companies still need to secure their bottom line and offer the best of services to their customers. With the rise of fintech companies, we are all set to see more applications of machine learning in finance.
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