Top Data Science use cases in Finance Sector

geeksforgeeksThis post was originally published by at Geeks for Geeks

Data is the bread and butter of the Finance Sector. Even before data science was such a cool term, financial companies used data to draw insights and obtain a competitive edge in this industry. However, modern Data Science is changing the Finance Sector in a lot of ways. From fraud detection to risk analysis to algorithmic trading, all of these use data science to better their performance infinitely.

Top-Data-Science-Use-Cases-in-Finance-Sector

Many financial companies enhance their cost efficiency and improve their sustainability by training machine learning models using a large amount of data they obtain from their customers, markets, rivals, etc. This allows companies to predict many important parameters such as their stock market moves, customer retention schemes, etc. So now let’s see how the finance sector is using data science and its related technologies such as machine learning to improve their performance in various fields such as risk analysis, fraud detection, real-time analysis, algorithmic trading, consumer analytics, etc.

1. Risk Analysis

Risk Analysis is a primary part of the financial sector. After all, how can a company take strategic decisions and manage their trustworthiness without engaging in risk analysis? And how can a customer trade or invest in the market if they don’t have a good understanding of risk? Therefore, Risk analysis is also a critical component that data science manages in finance. This involves an excellent understanding of maths, statistics, and problem-solving. Some of the risks that companies face include the risks from the markets, shares, competitors, etc. Companies analyze the massive amount of data they generate from their financial transactions, customer interactions, etc. and train to optimize their risk scoring models and decrease their risks. Another risk that companies face is from customers and whether they are creditworthy. So companies train machine learning models on customer information and credit history to understand their creditworthiness.

2. Fraud Detection

Where there is finance, there is also a high chance of fraud! And that is why fraud detection and management are some of the most important things that data science tackles in the finance industry. The most common type of fraud practiced is credit card fraud. However, now data analytics allows financial companies to catch the anomalies that occur in credit card history and financial purchases because of credit card fraud and freeze the account to minimize their losses as much as possible. Many other machine learning algorithms can analyze any unusual patterns in trading data if they occur and catch investment fraud if it occurs. Clustering algorithms can also be used to catch out on the cluster patterns of data that seem suspect and may be an indicator of insurance-related frauds or other frauds in the financial industry. In this way, data science can be used to manage fraud which has increased more and more with the increase in the number of financial transactions in modern times.

3. Real-time Analysis

Real-time analysis is fundamental in the finance industry. Companies need to know where their money is invested at the current moment, what is the state of the market, are their investments at risk, etc. And if the data is outdated or analysis is performed after the time has elapsed, then the insights gained are useless because the state of the financial industry may have changed. Financial companies need to make market decisions based on data that is only seconds old otherwise they will lose money in this competitive market. Therefore cutting-edge data infrastructure is very important in the finance sector for performing real-time analysis.

4. Consumer Analytics

Consumers are a key part of the financial sector. After all, what would be the use of finance if there are no consumers to use that knowledge. So consumer analytics are used to understand consumer behavior using consumer data and predictive modeling. Data visualization can also be used to highlight the relevant results to the people in power. Some of the common things than financial institutions keep in mind about their consumers include the customer lifetime value which is the total money a consumer may spend on a particular business, reducing their below zero consumers which cost the company more than they are worth, etc. and they work so that the optimal consumer segments can be targeted for maximum reach and profitability.

5. Algorithmic Trading

Algorithm trading is a big part of the modern financial sector. It involves executing the trading orders keeping in mind pre-programmed trading instructions that are automatic and keep in mind changing variables such as price, timing, volume, etc. These automatic trading instructions use complex financial formulae devised by machine learning algorithms that are free of human emotions and biases and so can make markets more liquid and provide much more systematic trading opportunities. Big data also has a huge impact on this as algorithmic trading keeps in account data streams that provide the relevant information for making decisions. In theory, this should be able to generate profits at a speed and frequency that is not possible by human traders.

6. Customer Data Management

It is very important for financial companies that they have full knowledge of their customer base. This is the only way in which they can keep track of their customer needs and also work on satisfying them otherwise they will lose their customers. For this, they need customer data. Now, customer data is available in 2 common forms i.e. structured data and unstructured data. Structured data is obtained through official forms such as feedback forms, initial detail forms, etc. and is easy to main and handle. It can even be stored in a conventional relational database. However, it is the unstructured data that creates a lot of problems. Most of it is obtained through informal methods like social media posts, online feedback, email messages, etc. And guess what, most of the data is unstructured! This data is only managed using various NoSQL tools such as Hadoop and stored in NoSQL databases such as Cassandra, MongoDB, HBase, etc.

7. Personalized Services

Customers are more loyal to a company when they feel they are getting personalized service and a high amount of attention. This is no different in the financial sector. Therefore, financial companies use a lot of methods to ensure that they can offer personalized service to their customers by using their data and generating insights from this data. One example of this is chatbots. Customers can get instant support and solutions for their problems using a chatbot. When customers get such good service, chances are high that they will be more loyal to the company which will drive up sales. Many finance companies are using technology like Natural Language Processing and Speech Recognition to interact with their customers on a personal level and provide better interactivity.

It is very important to state that these are not the only fields where data science is changing the finance sector. Financial companies also use data science and machine learning for other tasks such as process automation, social media interaction, higher levels of overall security, etc. All in all, the inclusion of data science best practices in the finance industry are only leading to more revolutionary changes. This has increased efficiency, improved transparency, and also lead to much tighter security practices overall.

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This post was originally published by at Geeks for Geeks

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