Top 10 Machine Learning Applications in Finance 2021

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Today, machine learning (ML) is changing every area of life and business like never before. And the financial industry is no exception. Consumers of digital services expect high-quality service, security and convenience. As a result, many leading banks and financial service companies are implementing machine learning technology with the help of machine learning consulting сompanies to optimize processes, portfolios, minimize risks, and, most importantly, improve customer service quality.

Financial companies use a variety of machine learning applications that fall into many subcategories. Let’s focus on the current top 10 machine learning applications in the financial industry.

The role of machine learning technology in finance

Today, machine learning has come to play an integral role in many parts of the financial ecosystem, from loan approval to credit ratings, asset management and risk assessment. Machine learning is well known for its predictions and obtaining accurate results.

Machine learning algorithms are developed to study data, processes and methods to find new insights.

Here are some key reasons why machine learning should be used in the financial industry:

* Improve productivity and usability.

* Increases income

* Reduce operational costs due to process automation

* Ensures the security of transactions

Moreover, a machine learning solution can help financial companies and businesses see the future more clearly, helping to reduce the number of forecasting errors.

Machine learning applications in finance

1.   Process Automation

Machine learning technologies enable financial companies to eliminate manual labor and automate routine tasks. Chatbots, for example, help companies to improve the quality of their customer service. Additionally, document management automation allows them to reduce costs and expand the scope of their services. Machine learning examines data, interprets behavior, and identifies patterns that improve the customer support system’s operations.

2.   Improved Customer Service

Financial companies are increasingly using virtual assistants or chatbots that answer customer questions, effectively process claims and complaints. Machine learning-based chatbots can quickly answer customer requests due to reliable natural language processing mechanisms and the ability to learn from previous interactions. These chatbots can adapt to each customer and change their behavior.

Moreover, virtual assistants can monitor customer accounts, help them save or increase their money, and provide personalized offers for them.

3.   Fraud Detection

Trying to manually detect fraud cases with hundreds of millions of bank accounts and credit cards is simply impossible. Financial companies can only expect to fight fraud by using machine learning algorithms to identify unusual transactions. In addition to detecting fraud with high accuracy, machine learning-based technology can also detect suspicious account behavior and prevent fraud in real-time.

4.   Algorithmic Trading (AT) and High-frequency trading (HFT)

Algorithmic trading (AT) is a key area of application of machine learning in the global financial market. By constantly monitoring trading results and news in real time, machine learning-based solutions and models help trading companies to make more accurate trading decisions.

Furthermore, machine learning plays a significant role in high-frequency trading (HFT). High-frequency trading is a complicated algorithmic trading strategy in which a high number of orders are completed in a couple of seconds. Large players can benefit from a small pricing differential that exists for a fraction of a second thanks to machine algorithms.

5.   Risk Management

Machine learning methods minimize risks and increase the security of banks, thanks to the analysis of a huge amount of data. After that, the various data collected by machine learning are a source of useful information for better decision-making.  In addition, machine learning algorithms can easily predict customers who are at risk of defaulting on their loans, allowing companies to change the terms and conditions for each client.

6.   Credit Ranking

Machine learning programs and algorithms analyze all available information about a potential borrower, including credit history, to determine the client’s reliability. As a result, banks may provide loans with more confidence and safety.  Chinese banks, for example, use not only machine learning algorithms but also advanced facial microexpression recognition technology. This approach enables them to determine whether clients are lying about their financial situation when applying for loans. As a result of identifying potential fraudsters, banks have already reduced their losses on unpaid loans by 60%.

7.   Financial Consulting

Surely there is an application for budget management on your phone. Today, there are a large number of such applications and they are all based on machine learning. Machine learning algorithms not only allow customers to track their expenses on a daily basis with these apps but also help them analyze this data to identify the structure of their costs. Moreover, machine learning algorithms determine areas in which customers can save money.

8.   Predictive Market Insights

Machine learning algorithms are also multifunctional tools for predicting and analyzing future financial trends. Due to machine learning, financial institutions can respond to market changes in advance and more flexibly than using traditional methods. It is also worth adding that machine learning used for predictive market information allows financial institutions to predict financial anomalies and portfolio changes.
To improve the accuracy of forecasts, some companies use a combination of several machine learning algorithms, which often leads to increased efficiency and productivity.

9.   Marketing Strategy

Machine learning in finance is used to improve marketing campaigns. Machine learning algorithms allow companies to develop a more effective advertising strategy for the clients by studying the activity in social networks, reviews, and recommendations, mobile applications usage. Therefore, using this technology, financial institutions and banks have the opportunity to prepare a successful and promising marketing strategy that will increase profits.

10.  Customer Churn Prediction

Machine learning solutions for customer churn prediction are used in many industries, such as e-commerce, mobile games, telecommunications, including finance and many others. Customer churn prediction allows not only to maintain the activity and loyalty of customers, but also provides an additional opportunity for cross-sales of retained customers.

How to implement machine learning solutions: in-house or outsourcing?

You can implement machine learning solutions with your team of ML specialists or ask another company to build them for you. It’s not an easy task! So, what is the most effective way for companies to effectively begin ML projects? Both have advantages and disadvantages.

In-house ML development

In-house development involves having your team with the necessary IT skills and abilities. Having your team means better customization, ownership of intellectual property, independence, and many other advantages. However, in some situations, this can have serious drawbacks. For example, there is a lack of experience, since some ML solutions may require hiring new specialists. Another disadvantage is the higher cost of hiring your developers than hiring developers from a machine learning consulting company.

ML outsourcing development

On the other hand, outsourcing is a growing trend which is used by many companies. Buying ML solutions, you have an opportunity to work with real experts. Moreover, machine learning consulting companies usually have access to demand-driven data. This data can be highly beneficial to your project, but it is very hard to collect it yourself. Also, as mentioned earlier, outsourcing helps to save money.

Key takeaways

To sum up, machine learning today affects various aspects of the financial ecosystem, from risk management, fraud detection in the financial sector, credit ranking, improved customer service, and much more.

Every company and bank that uses machine learning algorithms has the opportunity to predict and analyze future trends in the financial industry in advance. Thus, it directly improves business processes, affects the efficiency of the company and employees, and also increases profitability.

An author bio:

Edwin Lisowski_author

Edwin Lisowski is an experienced information management consultant with a demonstrated history of working in the information technology and services industry. CTO in Addepto consulting company. Skilled in Data Warehousing, AI Consulting, Business Intelligence, Big Data Integration, and Advanced Analytics.

Reference: 12 use cases of AI and Machine Learning in Finance. URL: