The consumer no longer wants to be one of many. The customer expects financial companies not only to recognize suspicious card transactions themselves, but also to anticipate their spending and offer a convenient card or service at just the right moment. All of this is the result of big data analytics.
What Is Big Data
The term Big Data refers to a group of technologies and methods that analyze and process a variety of data, both structured and unstructured, in order to produce new knowledge.
As a defining characteristic of Big Data are noted “three V” – volume, velocity, variety.
volume – in the sense of the physical volume,
velocity – in the sense of both speed of increment and necessity of high speed processing and obtaining results,
variety – in the sense of simultaneous processing of different types of structured and unstructured data.
In reality, only very large companies have big data in the true sense of the term, since even a few terabytes of accumulated statistics simply are not what Big Data is. A terabyte relational database is a highload-DB, not Big Data. The difference between these terms is the ability to build flexible queries. Ordinary relational databases are suitable for sufficiently fast and single-type queries, while the load on the complex and flexibly constructed queries simply exceeds all reasonable limits. That said, Big Data analysis techniques are quite applicable to data that are not originally large, moreover, analytics can be useful in many projects.
Why Do We Need Big Data?
Big data helps analyze the current state of the business, build predictions and automate routine processes. There are special technologies you can use to work with them that allow you to quickly process vast amounts of information and extract value from them.
What Methods Are Used to Work With Big Data
The goal of machine learning is to predict the result from the input data.
It requires an algorithm, signs of the necessary patterns and, in fact, the data itself to be processed. What can you get out of it? E-mail spam filtering, forecasting the behavior of clients or customers, and so on.
Sentiment in Big Data is a person’s attitude toward a particular brand, product, service, fact or event. Analysis of such attitudes is based on extracting specific feedback, comments, notes, etc. from a large flow of information.
At the output of this method you can get, for example, an excellent monitoring of the company’s or product’s reputation.
Social Network Analysis
The information for analysis in this method is the profiles of clients/partners/employees in social networks and the expected result is the portrait data of the entire target audience or its individual members and their connections with each other.
This is essentially a classification that is based on statistical data. For example, it can be used to automatically assign documents to a certain category. In other words, it is sorting, only in very large volumes.
The method is based on random selection, combination and modeling, essentially like natural genetics. Only they do it all with data. Reminds me of password matching. This method allows, for example, to schedule doctors for the emergency departments of hospitals.
How Does Big Data Work
In terms of technology, big data is artificial intelligence(AI)-based software algorithms that process vast amounts of chaotic information, structure it and make it understandable.
Why Big Data in Finance
Big Data technologies are especially in demand in finance. They can be used to prevent fraud, manage risk and improve customer service.
What Big Data Can Help You
- Detecting basic transaction channels (ATM withdrawals, credit debit card payments);
- Separating customers into segments according to their profiles;
- Cross-selling products based on customer segmentation;
- Fraud management and prevention;
- Risk assessment, security compliance and reporting to the regulator;
- Analyzing and responding to customer feedback.
Big Data presents new opportunities for the financial industry and others. Big Data technologies can take a company to a whole new level. Of course, not every company can afford its own specialist, that’s why many turn to data science companies which have professionals who can help and guarantee high quality and meeting deadlines.