10 Best Use Cases of Machine Learning in Finance

The demand and value of fast and high-quality financial engineering, financial analysis, and forecasting have increased over the last few years as we enter a new customer experience level. Obviously, the clear benefits of using artificial intelligence and machine learning in finance, banking, and business analytics are difficult to overestimate. The advantages have been confirmed in practice by many successful cases.

Machine learning applications in banking enable companies to automate time-consuming, mundane processes offering a far more streamlined and personalized customer experience. They also allow you to work more productively with large databases, significantly improving the quality of asset valuation, forecasting financial performance, and solving many key issues of financial security of data.

This post outlines key messages on the role of machine learning in FinTech as well as deep learning use cases in banking. Besides, the article focuses on the best effective cases of using AI and ML to optimize financial services.

The main functional destination of ML algorithms is to prominently identify work patterns and correlations among huge amounts of information, events, operations, and sequences. Thus, ML is successfully used today in process automation, security issues, customer support optimization, credit offerings, portfolio optimization, personal finance, and many other sectors. In reality, you are dealing with the work of machine learning banking use cases if you are a client of a bank, insurance service, or any FinTech company. Some experts ironically call the introduction of AI into the financial market white magic because it's almost invisible, yet it still changes the interaction of the customer and the company for the better.

For example, the leading commercial bank in Ukraine, PrivatBank, has effectively practiced a technology with chatbot assistants in web platforms and mobile applications. AI-based chatbots optimized the processing time of general queries' resolution significantly. Simultaneously, the worldwide known company PayPal invests in deep learning in security terms to improve its financial monitoring and detect frauds.

The highest level of customer support is the main marker of the quality of the financial services provided. And this is exactly the factor in which leading financial companies are fighting for leadership. ML helps organizations to improve customer experience, services, and to optimize budgets. Process automation replaces routine manual work in most cases, automates tasks, and makes their realization more productive.

Among the most striking examples of automation of processes to increase customer service level in the field of finance are the automation of paperwork, automation of call centers, and the use of chatbots.

One of the 'big four' US banks Wells Fargo, a holding company providing financial, banking, and insurance services in the USA, Canada, and Puerto Rico, constantly invests in improving customer support. It successfully uses an AI-based chatbot to communicate with users more productively and provide supportive assistance using accounts and codes.

Customer onboarding is the complete process that users go through when they act as clients of a bank or FinTech company. Onboarding experience can truly determine the customer's current relationship with the organization. To visualize the results of applying ML in the direction of client onboarding, try to look at the interface of any of the popular social networks.

Any smallest change in the start page's design or an application shortcut on your desktop, any change in the algorithm, and innovation in the optional is not just happening and not at the developers' whim. Artificial intelligence studies the patterns of users on the web, and based on the analysis of the behavior of millions of customers, changes and improvements are created.

Among the examples of successful cases in this direction is the experience of German challenger N26 Bank. Being fully digital enables customers to manage their financial lives directly from their smartphone from day one. It invites customers to open an account in minutes, right from the smartphone, manage it on-the-go, spend and set aside money in real-time. This offering makes real sense for the customer. Nowadays, previously complex processes like onboarding can happen anywhere in minutes.

As far as the number of transactions, real clients, and integrations grows, security threats come along. This is when machine learning algorithms come in handy when banks and other institutions require a special fraud detection.

Banking organizations can use it to monitor a considerable amount of transaction parameters at once for every account in real time. The algorithm examines historical payment data and analyses every cardholder's action. Such models can be highly prominent and prevent some suspicious behavior with a rather big precision.

A global payment system Payoneer provides financial services and online money transfers worldwide. Accordingly, the company's customer database is estimated in millions. Since the company is a registered MasterCard provider worldwide, transaction security would fail without ML use cases in banking.

Portfolio management is an online wealth management service that uses statistical points of the issue as well as automatized algorithms to optimize the performance of client assets. Customers fill in their financial goals, for example, to save some amount of money during a certain period of time. The robot advisor then assigns current assets to investment variants and opportunities. Portfolio management involves creating and overseeing selected investments that align with the investor's long-term financial goals and risk tolerance.

One of the world's largest investment management firms, BlackRock Investment Company, offers Aladdin, an operating system created and adapted for investment managers' needs. The company claims that Aladdin can use machine learning in FinTech to provide investment managers in financial institutions with risk analytics and portfolio management software tools to make more informed investment decisions and operate more efficiently.

Credit risk is the economic loss that emanates from a counterparty's failure to fulfill its contractual obligations, or from the increased risk of default during the term of the transaction. The increased complexity of assessing credit risk has opened the door to deep learning in finance. This is evident in the growing credit default swap market where there are many uncertain elements involving determining both the likelihood of an event of credit default and estimating the cost in case a default takes place.

The other side of ML implementation in risk management is the term of security. Citi's and Trade Solution Group has entered into a strategic partnership with Feedzai, an artificial intelligence leader for real-time risk management across banking and commerce.

Citi integrated Feedzai's transaction monitoring platform powered by advanced technology into its own proprietary services and platforms to provide clients with enhanced control and risk management for payments transactions. The ML solution compares all the possible data points in current and preceding transactions to detect suspicious transactions with compliance concerns.

Customer churn forecasting is one of the most popular big data use cases in business. It lies in detecting customers who can cancel the regular subscription. The scope of implementation of the methods is enormous — from sales funnels in commercial mailings to tailoring various loyalty programs for customers.
Any large telecommunications company or mobile operator can be cited as an example of machine learning's practical application in predicting customer churn. This category includes almost any business that sells subscriptions. Video streaming giant Netflix had a total net income of over 1.86 billion U.S. dollars in 2019, whilst the company's annual revenue reached 20.15 billion U.S. dollars. The number of Netflix's streaming subscribers worldwide has continued to grow in recent years, reaching 167 million in the fourth quarter of 2019.

Asset management for digital assets or distributed industrial assets are applications where voluminous data about the assets is already being recorded, making them ripe for automation through AI.

Asset and wealth management firms are exploring the potential AI solutions for improving their investment decisions and making use of their troves of historical data. In fact, approximately 13.5% of the AI vendors in banking are for wealth and asset management solutions.

Predictions of stock market fluctuations are often underestimated in the trading sector and even considered pseudoscientific. Some old school traders still think so too and study tons of stock charts with the Japanese candlesticks every day.

Though, businesses today have an opportunity to make estimated guesses and informed forecasts based on the information we have in the present and the past regarding any stock. An estimated guess from past movements and patterns in stock price is called stock technical analysis, and it is used to predict a stock's price direction. At the same time, the most prominent technique involves the use of artificial neural networks and algorithms.

Financial machine learning helps to solve tasks with the winning trading decisions in the algorithmic trading sphere. A mathematical model monitors the latest updates of the market information and trade results in real time. A special algorithm was created in order to detect patterns that can impact the dynamic of stock prices in their increasing or decreasing course. It can then act proactively to sell, hold, or buy stocks, using factual information related to forecasts.

Machine learning algorithms can analyze tons of data sources and market conditions simultaneously. And, understandably, human traders cannot possibly achieve it physically, because of the huge amount of information.

The so-called credit scoring system assesses a person's creditworthiness and credit risks, based on numerical statistical methods. This technology is often used in fast lending for small amounts, when registering consumer express loans in real stores by credit companies, in the business of mobile operators or insurance companies.

Scoring is the assignment of points by filling out a certain questionnaire developed by credit risk assessors. Based on the results of the points gained, the system automatically decides to approve or refuse to issue a loan.

The data for scoring systems is obtained from the probabilities of loan repayment by individual groups of borrowers, which was received from the analysis of the credit history of thousands of people. It's believed that there is a correlation between certain social characteristics of a particular client, including having children, marital status, the level of education, and the conscientiousness of the borrower.

For example, one of the largest credit history bureaus in the United States with more than a century of its history, Equifax, successfully implements machine learning and FinTech technology. They use it in credit scoring to provide credit ratings and demographics to businesses and offer commercial credit monitoring and fraud prevention services directly to consumers.

The list of our successful cases also includes solutions for insurance companies, customer support services of commercial banks, as well as solutions for online educational programs for brokers. Realize the benefits of outsourcing your current business challenges, and we will be happy to help turn your ideas into brilliant ML solutions!