The use of machine learning in the field of fintech

Being an active player in the market, our company PayOnline , which specializes in the organization of payments on websites and in mobile applications, cannot but note that today the sphere of financial services is undergoing fundamental changes. This is facilitated by the arms race that has unfolded in recent decades in such areas as big data analytics, neural networks, evolutionary algorithms, expert systems and machine learning. These technologies allowed to process considerably large volumes of various data not only faster, but also more efficiently.

Nowadays, financial services companies have gained new opportunities through machine learning, which is a series of processes that give computers the ability to make assumptions based on known properties derived from training data. The most important thing in machine learning is data. Computers analyze new information and compare it with already existing data in order to find patterns, similarities and differences. At the same time, their ability to more accurately and efficiently analyze data, classify information and make assumptions is constantly being improved, which makes it possible to make better decisions based on data.

Many startups turned the fintech ecosystem upside down due to the introduction of machine learning as one of their key technologies. Companies use various machine learning algorithms to solve problems that can be divided into several categories. Let's look at some of the examples of using machine learning for the benefit of such companies.

1. Credit scoring

Increasingly, companies operating in the field of credit use machine learning to predict the creditworthiness of customers, as well as to build models of credit risks. Such companies include Kabbage, Inc. , financing a small business through a lending platform, LendUp remote microlending service and the recognized leader in the financial technology industry Lending Club . In particular, the Kabbage team specializes in developing new generation machine learning and analytics algorithms for building credit risk models and analyzing an existing portfolio. Among the set of machine learning algorithms for determining the borrower's credit rating, the following are used: multilayer perceptron, logistic regression, support vector machine, as well as AdaBoost (or Adaptive Boosting) classifier amplification algorithm and vector quantization during training.
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2. Decision Making

Financial calculations and decision making can be carried out through machine learning algorithms that allow computers to more efficiently and quickly process data and make decisions regarding credit, insurance, fraud protection, etc. Machine learning models are widely used by companies such as Affirm , BillGuard and ZestFinance . The latter managed to find a new approach to traditional problems thanks to machine learning and the analysis of large data arrays. The company analyzes thousands of potential credit variables — from financial information to technology utilization — to better assess factors such as the potential for fraud, the risk of default and the likelihood of long-term customer relationships. As a result, an enterprise can make more “correct” decisions on granting loans, which leads to an increase in the availability of loans for borrowers and a higher percentage of their repayment.

3. Retrieving Information

It's time to talk about a variety of information retrieval, the purpose of which is to automatically obtain structured data when processing unstructured or semi-structured information. As a rule, this concerns work with web content, that is, articles, publications in social networks and various documents. For example, AlphaSense , a specialized search engine for financial companies, uses natural language processing algorithms and complex machine learning algorithms.

Thanks to powerful proprietary algorithms, Dataminr is able to instantly analyze Twitter’s postings and other data from social networks and web sources and convert it into useful information that can be applied in practice. The company targets customers from finance and news, as well as public sector and corporate security. By processing 500 million tweets each day, Dataminr algorithms are able to find relevant information about new uptrends of interest to clients and the latest hot news for minutes and even hours before the moment when they actually become one.

4. Fraud Protection and User Identity Management

According to the results of IBM research, each year fraud causes damage to the financial industry, equal to about $ 80 billion. Machine learning provides more effective methods of detecting fraud. Thanks to the solutions created, it is possible to analyze transaction history to build a model that could recognize fraudulent actions. In addition, machine learning technologies are also used by techtech companies for the development of biometric user authentication systems. The startup EyeVerify has developed a technology using machine learning algorithms that allows the use of trendy “selfies” to secure their financial operations. Their flagship product, Eyeprint ID, is software that identifies the user by drawing veins on the whites of the eyes and other microscopic features of the eye.



Feedzai , a company specializing in data processing and analysis, uses machine learning and large data sets to improve the security of enterprise information. Their machine learning models recognize fraud 30% faster than using more traditional fraud detection methods.

5. Algorithmic trading strategies

Machine learning is used to create highly efficient algorithmic strategies for trading. The main form of algorithmic trading is high-frequency trading, in which special algorithms and trading robots are involved for the quick trading of securities. Machine learning provides powerful tools for studying market patterns. Thanks to predictive modeling, programming, and machine learning algorithms, KFL Capital Management Ltd. , an investment fund manager, has become an expert in predicting market behavior changes based on financial data. Binatix, a trading company, implements state-of-the-art machine learning algorithms that help to detect patterns that give an advantage in investing.

Along with the development of other technologies, machine learning introduces significant changes in the FINTECH industry, offering effective solutions for data analysis and decision making. Machine learning algorithms are used for many tasks in various areas of financial technology - from lending to enhancing the security of financial transactions, and they can be directed both to individual clients and corporations.

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