Data Analytics in Finance

Use case of data analytics in finance

Using data analytics or data science in the financial sector is becoming a necessity to keep up with the competition and make the firm secure. Nowadays as customer data is increasing day by day particularly hour by hour, we need robust data analytics tools and machine learning models to manage data and prevent financial scams. Data Science is used in Fraud detection, real-time analytics, consumer analytics, algorithmic trading, personalization services, risk analytics and many more in financial institutions.

Now let’s see some of the applications of data analytics in the finance sector:

Fraud Detection

 Fraud detection is the most common and important thing that the finance sector deals with every day. Companies should ensure security and safety for the customers but sometimes due to some leaks or loopholes, companies can bear a big financial loss. Credit Card Fraud is one of the most common types of fraud. So financial institutions are using data scientists who can use advanced machine learning models and data analytics and find the unusual patterns in real-time. By using machine learning models in the stock market one can identify the patterns in data which manipulate and further investigate that. In this way, data science is used in fraud detection.

Consumer analytics 

Consumer analytics is using data to understand consumer behavior, needs and satisfaction. Companies can gain insights into the consumer's behavior with help of real-time analytics and they can make better business strategies and improve personalization services. Data analytics is used in many financial industries (like insurance companies) to generate insights from clients’ behavior.

• Customer data management and personalisation services: To build a good relationship with customers, financial institutions need to improve their personalized services. Customers are more reliable to the company when they are getting personalized services and they feel they are getting more attention. For this they need customer data. Customer data is available mainly of two types:

            1) Structured Data

            2) Unstructured Data

Structured data can be processed and analyzed effortlessly but when it comes to unstructured data it is not as smooth as structured data. It is obtained through feedback messages, emails, online polls, social media posts, etc. This data can be managed through various data science models and big data tools. Companies are using many tools and techniques like NLP (natural language processing) Speech recognition, text analytics, data mining etc. for better communication with customers, improving their services, designing better business solutions and optimizing strategies.

Real-time and predictive analytics

In earlier times, data used to be processed and analyzed in batches (one batch at a time) but not in real-time. This method has a huge disadvantage. For example, when we take stock market data, the importance of data changes from minute to minute so the earlier method will not be helpful and accurate. Financial companies need to make decisions based on seconds-old data. So real-time analysis is most important in the finance industry. It analyses a large amount of data which is taken from discrete sources and provides the best results possible it. In the present ultra-competitive market, real-time analytics are very useful for understanding and responding to customer behavior. Financial institutions need to know what customer wants and real-time analytics makes it possible. Finance industries can keep a check on all credit scores and transactions of customers by using real-time analytics.

Algorithmic Trading

This is the most affected area by the outcomes of real-time analytics. Nowadays algorithm trading is a big part of the modern financial sector. It is a system of trading in which we use advanced mathematical tools to facilitate transaction details in decision-making. By computing complex mathematical formulas in fraction of seconds financial institutions can devise new strategies. As these models use complex formulae and machine learning models, markets are free of human biases and they will be more viscous also providing more systematic trading opportunities. Financial institutions use algorithmic trading to forecast market opportunities. With software and techniques like Hadoop, Storm, NoSQL and the most precise algorithms data scientists are making important financial decisions to stand out in this competitive sector. Big data also has revolutionized the Data Science field in a bigger way which led to a massive change in the way the industry is functioning.

Risk Analytics: 

Risk analytics and management are very important factors in the finance sector. Every financial company faces risk in one way or another. Analysing the risks or threats became essential for the companies to make strategic decisions and is called risk Analytics. It helps companies to increase security, take better business strategies and also helps in maintain trustworthiness. A data analyst with skills in statistics and problem-solving can perform the processes involved in risk analysis. Some risks that companies face are markets, shares, competitors, investors, credit cards etc. companies analyze their massive customer data from the financial transactions and customer interactions and they decrease the risk by optimizing their risk scoring models. for example, when we take the case of credit cards, the company should know the creditworthy of the customers. So, they train the machine learning models on the information of the customers and their credit history so that they can understand customers' creditworthiness also raw data consists of both structured and unstructured data which structured data can be handled easily. For unstructured data (which cannot be handled directly by any database) data science plays an important role to analyze data and also to identify, prioritize and monitoring the risks.

Conclusion: 

• Although data is an important segment in companies, one needs to know how to handle it and analyze it. Better analysis leads to better decision-making in financial industries and also increases profits. So, data science has become a very important segment in the finance sector. Today the financial data is rapidly increasing which creates a necessity for machine learning and AI tools in different aspects of the business. Accuracy in detecting fraud, reducing losses, and minimizing risks are improved with the use of data science.