How Machine Learning is changing Financial Services in 2020

An Introduction to Machine Learning for Finance.

In the modern world, businesses should pay close attention to the technology to be competitive on the market. Sure, there are some industries that don’t require constant improvement and can do just as well with legacy technologies, but that is definitely not true about companies that provide financial services. In this article, you will learn how is machine learning used in finance and what this particular technology can bring to elevate the businesses. 

Machine Learning (ML) is a subdivision of Artificial Intelligence and the subset of Data Science, which can make conclusions and predictions according to data. To get the results ML uses algorithms to draw conclusions and learn without being heavily programmed to do so. With the right set of data and proficient experts for support, Machine Learning can do wonders. Here is how the most common machine learning applications for finance look like: 

According to research by IIoT World, Finance is placed fifth in the list of the industries where AI/ML projects are doing good after the implementation. What is all the buzz is about and does really Machine Learning in Financial Services is as good as advertised? There is definitely value behind the hype and I will explain why.

Why you need Machine Learning in Financial Services?

Those companies that had already embraced the technology already know, that you can easily point out three main reasons ML improved their business:

Improved security
ML allows monitoring a significant number of transactions in real-time - hundreds or even thousand, which is crucial for financial organizations. The system with this technology can not only detect suspicious activity but take action on it, far more quickly and precisely than humans ever can. AI and ML solutions can deal with the fraud that happens in Financial Services, that’s a fact, but the effectiveness of that solutions largely depends on the expertise of the developers and data, available for processing. 

Cost savings
By helping employees to get rid of routine tasks, ML provides better automation for the entire organization as a result. The big amount of paperwork can be replaced easily and the chatbots, for example, can answer basic client requests 24/7. 

Increased revenue
Boost in productivity as well as providing better customer experiences leads to the revenue increase. Not only ML-powered organization has a better brand image but also has and additional resources (that were freed up by technology) to provide new services or focus on new business opportunities.

Why Fraud Detection is the most interesting among other machine learning applications in finance?

Dealing with money, security is the main priority of financial organizations. Criminals are also becoming smarter, and they are constantly improving their fraudulent schemes. That’s the main reason why Fraud Detection is so important for Finance. Security solutions are the priority for financial organizations, and they are willing to focus their AI/ML budgets on fraud detection, as illustrated here:

The difference between traditional methods for Fraud Detection and ML-powered solutions are dramatic and can make a big impact in real-world scenarios. If you are still hanging to your existing and not “smart” security solution, consider this:

Detecting suspicious activity automatically and alerting on it in real-time is a treasure for any financial organization because this can benefit both clients and companies. With ML-based Fraud Detection solution is way harder for criminals to use companies for illegal cash flow, as any suspicious activity will be alerted on immediately. As the simplest example, the ML solution can detect and alert on duplicate transactions right on the spot. Whether it is a mistake or some fraudulent activity - notification will be sent as the second transaction happens. In the bigger picture, such an issue can cost an enormous amount of money. 

The prices for Fraud Detection solution powered by Machine Learning may vary because problems and the scale are different, as well as the methods to deal with the challenges. Every case is unique and requires a custom approach. However, it is certain that the ML solution requires an accurate dataset, functionality of rule-based engines, and most importantly - proficient data science experts. It is usual for financial companies to partner with third-party data science experts to handle the Fraud Detection project. 

Top 5 of other prominent Machine Learning use cases in Finance in 2020

While the security is that important, there are few other usages of Machine Learning for Finance worth mentioning: 

Sentiment and News Analysis
Natural Language Processing (NLP) technology uses Machine Learning for understanding text. How can you benefit from this? Algorithms can be trained to search for and interpret the meaning of news, social media posts, and any other data sources. This is already a reality - even trends, emerging in the financial industry, can be processed automatically to give you the latest insights on the market situation or maybe your brand image according to the information on the web. 

Loan and Insurance Underwriting
Speaking of AI there had been a lot of talk about computers replacing humans. In most cases, it’s not true, but this is definitely the area were smart algorithms can do the job better. This is especially true for big corporations with large databases and the ability to hire data scientists. Machine Learning solutions can be adjusted to analyze historical information from the past and make calculated decisions. 

Algorithmic Trading
The first attempts to create automated systems for trading had appeared almost 50 years ago! In 2020, Machine Learning algorithms play an important part in fine-tuning trading decisions for financial organizations. Traders and hedge funds keep their AI aces in their sleeves, so we can’t get accurate information about the current state of ML approaches for trading. However, we can say that they use it as much as possible because they are always for winning strategies.

Customer Service
Chatbots seem to be everywhere now and Finance is not an exception. Wells Fargo was one of the pioneering banks, introducing AI-powered chatbot for their Facebook Messenger, simplifying the access to the client accounts and basic functions. Now it is possible for a chatbot to expand that functionality, giving a client update on saving status, the balance, or the amount of savings on a specific date. 

Robo-advisory
Have you ever heard of this term? This financial service is now a common thing. The portfolio of the client can now be managed in the form of an online service that uses ML algorithms to manage wealth. Let's say, a user wants to quit his job when he’s 40, he has a certain amount of money which he dares to invest in IT. All he has to do is to put in that data, set goals, and ML algorithms will provide him the best financial suggestions and opportunities available at the moment. 

“Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we'll augment our intelligence.”  Ginni Rometty, Executive Chairman of IBM

Conclusion

Machine Learning for Finance has a bright future ahead, as the opportunities to implement the technology will only grow. Gartner predicted that this year only 15% of customer interaction with the organization will be human-to-human, while the majority of interactions will be taken over by AI. Machine Learning had already proven itself in Fraud Detection and Security but definitely improve in the Predictions, Automation, and Customer Services areas. Will all forecasts come true? Time will only tell. But what we can already see is that Machine Learning is an essential part of Financial Services in 2020.