As the world advanced into the era of big data, the need for its storage also grew. Storage became the primary concern and challenge for the enterprise industries until 2010. The main focus then was to build solutions and frameworks to store data. Now when Hadoop and other frameworks successfully solved the problem of storage, the focus has shifted to the processing of this data.
Data Science is the secret sauce here. All the ideas one sees in Hollywood sci-fi movies can be turned into reality by Data Science. It is the future of Artificial Intelligence; therefore, it is essential to understand what Data Science is and how it can add value to businesses.
Data Science is a blend of various algorithms, tools, and machine learning principles to discover hidden patterns from the raw data. Data Science is primarily used to make predictions and decisions, making use of predictive causal analytics, prescriptive analytics (predictive plus decision science), and machine learning.
Finance has always been about data, and matter-of-factly, finance and data science go collectively. Finance has been using it long before the term data science was devised. Just like how banks have been automating risk analytics, finance industries have also used data science for this task.
Finance industries understand data as a fundamental fuel and commodity. It transforms raw data into a meaningful product and makes use of it to draw insights for better functioning of the industry. Finance is the hub of data, and financial institutions were among the pioneers and earliest users of data analytics. Data Science widely used in areas like customer management, risk analytics, algorithmic trading, and fraud detection.
Why data science is used in Finance
Financial industries need to automate risk analytics to implement strategic decisions for the company. With the use of machine learning, they monitor, prioritize, and identify the risks — these machine learning algorithms model sustainability and improve cost efficiency through training on the enormously available customer data.
Similarly, financial institutions make use of machine learning for predictive analytics. It allows the companies to predict their stock market moves and customer lifetime value.
Remarkable Data Science Applications that are reforming the Finance Industry –
1. Risk Analytics – This is one of the critical areas of data science. With Risk analytics and management, the company can take strategic decisions, increase trustworthiness and security. Risk management is an interdisciplinary field, and it is essential to know mathematics, problem-solving, and statistics.
Most companies face various types of risks, which originate from credits, markets, competitors, etc. The major steps towards managing risks are identifying it, prioritizing, and monitoring the risks. There is a massive availability of data and financial institutions train on this type of data to increase risk scoring models and optimize their costs.
2. Consumer Analytics – One significant task of financial institutions is Consumer personification. With the assistance of real-time analytics, data scientists can take insights from consumer behavior and can make business decisions that are appropriate.
Financial institutions like insurance companies utilize consumer analytics to measure the customer lifetime value, increase their cross-sales as well as reduce the below zero customers for optimizing the losses.
3. Fraud Detection – Fraud is a significant concern for financial institutions. The dangers of fraud have however, been increased with an increase in the number of transactions. However, with the growth in analytical tools and big data, it is now possible for financial institutions to keep track of fraud. One of the most widely practiced scams in financial institutions is credit card fraud, and this type of fraud can easily be detected as a result of the improvements in algorithms that have improved the accuracy of anomaly detection.
Additionally, these detections alert the companies about anomalies in financial purchases, prompting them to block the account to minimize the losses.
Various machine learning tools can also quickly notice strange patterns in trading data and then call the financial institutions’ attention to investigate it further. There are other insurance-related frauds that banks face, but with the use of various clustering algorithms, companies can separate and cluster patterns of data that appear highly suspicious.
4. Customer Data Management – Financial Institutions need data. Big data has revolutionized how financial institutions operate. The variety and volume of data are contributed through social media and a large number of transactions.
The data can be in two forms –
– Structured data
– Unstructured data
While the structured data is much easier to handle, it is the unstructured data that causes a lot of problems. This unstructured data can be dealt with with several NoSQL tools and can be processed with the help of MapReduce.
5. Algorithmic Trading – Algorithmic Trading is an essential part of financial institutions. In algorithmic trading, there are lightning speed computations and complex mathematical formulas that help the financial companies to devise new trading strategies. Big Data has had a significant impact on algorithmic trading, and data science has become its most essential feature.
The data present in the algorithmic trading is made up of massive data streams and involves a model that describes and measures the underlying data streams. The analytical engine aims to make predictions for the future market by having a better understanding of the massive datasets.
6. Offering Personalized Services – Financial Institutions are responsible for the provision of personalized services to their customers. Financial Institutions make use of different techniques to dissect customer information and create insights about their interactions. Moreover, financial institutions are relying on natural language and processing and speech recognition software for the provision of improved interactivity to its users.
With the data that is provided back by the users, financial institutions can take actionable insights into their customer needs, which would result to an increase in profit. This would help the institutions to improve their strategies and offer better services to their customers.
The current drift in the financial industry is to lead the way to more sound and revolutionary models finding their way in. The best solutions to the serious problems in the field of trading and finance would lead to more transparency, innovations, tighter risk management, and increased efficiency.
About the Author
Mayur Rele is a cloud automation expert and cybersecurity leader that has a wide experience in overseeing global technology, cloud infrastructure, and security in healthcare, e-commerce, and technology companies. Mayur graduated with an M.S. in Computer and Telecommunications Engineering from Stevens Institute of Technology and is an active IEEE researcher and contributor.
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