Data science is one of the revolutionary topics that have changed lots of industries and businesses. These businesses and industries depend on data science, artificial intelligence, big data, and machine learning to make decisions. According to Forbes, enterprises that adopt big data raised from 17% in 2015 to 59% in 2018 with a compound annual growth rate (CAGR) of 36%. Data is a powerful tool for any enterprise, it can process all the data and use these insights to decision making, improve customer service, and develop the products and services. Also, data give the decision-maker the chance to predict the future and analyzing the risk.

1) Decision Making

Most of the finance industries make lots of decisions based on predictions. By collecting past data, decision-makers are able to use these data to build a prediction model for the future of the industry and make the decision based on the results of the model. Decision making in the finance industry provides clever decisions based on data and models instead of making the decision based on the intuitions and biases.

2) Risk Analytics

Institutions make lots of decisions and build strategies, and they consider the risk because the risk is associated with any decision in life. The risk may arise from regulations, customers, competition, and economic conditions, so it is necessary to identify the risk, monitor it, and prioritize the risks. Data science and machine learning use for this process by building models to mitigate the effect of risks and it depends on data. Examples of risk analytics in insurance companies where they assess the profiles of applicants in greater detail. Also, banking industries use risk analytics where they can verify the creditworthiness of customers.

3) Management of Customer Data

Financial institutions use and extract an enormous amount of data from different sources, so it is an essential process to store this data and benefit from it in the future. Data specialists divide data into three types, structured data that organized in a spreadsheet, so it is easily accessible and easy to use. Semi-structured data is structured data but need work to put it in a spreadsheet. Finally, unstructured data hasn’t any structure, so you need to work on it to extract what you need like social media and surveys. Artificial intelligence used to handle the unstructured data with the help of data mining and natural language processing.

4) Fraud Detection

One of the significant factors that characterize good financial institutions' services is to protect from fraud processes like credit card fraud. Fraud is one of the main concerns of any financial institution, so it needs a robust system to protect from frauds and improve security especially for the increased number of transactions in the current time.

Fortunately, data scientists use machine learning and artificial intelligence algorithms to solve this issue. Machine learning uses to identify unusual transactions to detect the fraud and then sends a message to the employees to mitigate the danger. Artificial intelligence helps the system to reveal new methods in fraud, so the system is self-teaching and updates with time.

5) Introducing Personalized Services

With the increased competition in the finance industry, financial institutions need to provide high-quality services, and new services based on customer needs to gain more customers and increase revenue. Financial institutions collect an enormous amount of data about customers and their digital experience, and they use artificial intelligence algorithms by using natural language processing and speech recognition to understand the customers’ preferences.


With the advancement of data science, machine learning, and artificial intelligence, every industry has an extraordinary opportunity to benefit from these technologies that will give business insights by collecting and analyzing data. Financial institutions have this opportunity to benefit from data to grow up and compete with others by utilizing these methodologies and algorithms.