Technology is not just an industry but an application that benefits all industries! After all, new technologies like Machine Learning and Data Science can provide enhancements and new innovations in many different industries like the Finance Industry. Who would think that machine learning can have any role to play in finance? After all, finance just covers your banking or insurance or even any share trading but what is the relevance of ML here? Well, as it turns out, Machine Learning actually has many different benefits for FinTech.
Many financial companies can enhance their performance and cost-efficiency while improving their sustainability by training machine learning models using a large amount of data that is available from customers, markets, rivals, etc. This is how technologies like Machine Learning and Data Science can be combined with financial institutions to obtain great results. So let’s understand the meaning of FinTech companies the different roles that Machine Learning plays in enhancing these companies.
What is FinTech?
FinTech is an innovative achievement of the 21st century that is combined using 2 words i.e. Financial Technology. This is the combination of finance and technology that merges various tech services into financial services. There are many different types of FinTech companies in multiple sectors such as Banking, Insurance, Trading, Lending, and Credit, etc. These FinTech companies can reach out to a large number of customers than previously possible using the innovations in technology and provide them secure and easy transactions. Basically, FinTech has digitized data and finance where customers don’t have to leave the privacy and comfort of their home to avail of these services.
And emerging technologies such as machine learning, data science, etc. have made these FinTech companies more diverse and easy to access for their customers. They provide more applications in various domains like financial trading, forecasting, fraud prevention, customer support, etc. So let’s see these benefits that Machine Learning and Data Science provide to FinTech.
Benefits of Machine Learning in FinTech
1. Financial Trend Forecasting
Machine Learning algorithms play a very important part in forecasting financial trends. FinTech companies can use ML algorithm to predict market risk, identify future financial opportunities, reduce fraud, etc. Companies can train their machine learning models on huge amounts of data such as financial interactions, loan repayments, company stocks, customer interactions, etc. This ensures that they can predict future trends relating to lending, insurance, stocks, etc. Companies can also use these ML algorithms in early warning systems that can predict risk scenarios, financial anomalies, changes in portfolios, etc. Another application ML is forecasting consumer trends for Fintech companies. Here consumer analytics are used to understand consumer behavior using consumer data and predictive modeling.
2. Algorithmic Trading
Algorithmic trading is becoming more and more popular these days. In fact, around 70 percent of all the daily trading worldwide is algorithmic trading which is an application of Machine Learning. But what is algorithmic reading and how is it different from normal trading? Algorithmic trading involves executing the trading orders keeping in mind pre-programmed trading instructions that are created using machine learning algorithms in conjugation with financial formulae. There are no human emotions or preconceived notions involved in algorithmic trading because the algorithm is automatic and keeps in mind changing market variables such as price, timing, volume, etc. Another advantage of algorithmic trading is that humans don’t need to monitor the market consistently whereas this is a must in manual trading. All these factors combine to create much larger profits from algorithmic trading that is not possible by human traders.
3. Advanced Customer Support
Machine Learning is also very valuable in providing advanced customer support to all their clients. Now, there is no need for customers to stand in long lines just to have some basic queries answered. A big example of advanced customer support is chatbots. These chatbots can provide instant support and solutions for their problems. And this saves time for both FinTech companies and their customers when agents are not required to solve basic problems. For example, the bank of America has its chatbot Erica that can provide balance sheets, past transactions, investment portfolio details, etc. to customers with no hassle. Another facet of advanced customer support is a personalized experience for customers. This is very difficult to achieve manually because a FinTech company may have thousands of customers but machine learning makes it easier. Machine learning algorithms can analyze the best history, transactions, etc. of a customer and predict what services they would like or give preemptive advice and suggestions.
4. Fraud Prevention and Detection
There is a high chance of fraud in FinTech companies. This is especially true with the increases in technical innovations as there are more opportunities to swindle the tech and do fraudulent transactions. In such cases, an ML-based approach to fraud detection and prevention is extremely important along with the traditional methods. The ML-based approach manages real-time processing and automatic detection of the anomalies using ML algorithms. The most common examples of this are credit card fraud and investment fraud. FinTech companies can catch the anomalies in credit card history and financial purchases using ML algorithms and freeze the account to minimize their losses as much as possible. There are also machine learning algorithms that can analyze any unusual patterns in trading data if they occur and catch investment fraud if it occurs.
5. Advanced Underwriting Services
Underwriting services are those where large FinTech companies guarantee payment for financial losses and accept the risk of paying this payment. This can happen in the case of insurance but FinTech companies need a complete risk assessment before deciding if it’s worth it to provide underwriting. This process of risk assessment can be a little complicated as applicants may hide details about their past financial history. Machine Learning algorithms are a much better option for risk assessment than just using manual methods. These algorithms can analyze the data from financial transactions, past credit history, etc. for a particular customer and understand whether the customer is worth providing underwriting services to. Natural Language algorithms can also go through the social media sites of the customers to better understand if they are trustworthy or not.
Conclusion
There are a lot of benefits that machine learning can provide to FinTech companies and we have only touched the basics in this article. In fact, ML can be used to improve every fact of service ranging from operations, security, marketing, customer experience, sales, forecasting, etc. And since this is still a developing technology, there are no limits to how finance and technology can intermingle and create much better experiences for their customers in the future.
