Big Data in Banking: Advantages, Challenges and Applications

Over the past 20 years, Big Data technologies have already radically changed the business of foreign companies and forced some market players to get fabulous profits, while others forced them to abandon their business and accept an offer to merge with more technically advanced and successful competitors.

A well-known example is a business selling mobile smartphones from Apple. Some 10-12 years ago, the company was considered a potential bankruptcy, but current ideas, bold design, and popular products allowed Apple to become a leader in the industry.

But what is the merit of Big Data Analytics? Making the phone is only a small part of Apple’s success. Interaction with other systems. business and the use of modern models based on big data to predict the behavior of not only the market but also your real and potential client. Big data has already been confirmed by official references many times. large domestic, including financial, organizations.

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Three components of the term

Three components of the term

As of June 2017, the expert did not make a clear definition of the term Big Data in the Russian official glossaries. Most likely, this is due to the lack of a common understanding of the technological functions of Big Data among the persons responsible for drafting our regulatory documents in the country.

Someone thinks that big data is a situation when a certain system has more than 100 GB of data for processing, or more than 500 GB, or more than 1 TB – whoever likes what. It is impossible for ordinary people to imagine that big data is data that can be processed for a long time in MS Excel or in any relational database.

Big data is data that is difficult to read and redundantly stored on a computer and requires an entire server or two, or a rack of servers. The position of the management staff of domestic companies boils down to the fact that big data is any data that is processed within their organization.

Skeptics are generally not inclined to define a Big Data terminal and consider it to be another marketing ploy that marketers have come up with in the West to sell their products more to technically illiterate consumers in Russia. In the theoretical studies known to me, the term “Big Data” is understood by the authors as a series of approaches, tools, and methods for processing structured and unstructured data of considerable volume and variety.

From the standpoint of a practitioner, I defined the term Big Data for myself as a combination and unification (in mathematical terms) of three mandatory components:

  • in the data queue of a large or another amount of information – it does not matter, since there is never too much and the analytical model and hypotheses can be built with a certain degree of probability;
  • Then the definition of the term Big Data is necessary to use technologies for providing online access and data processing since even with the presence of high-quality analysts, it is quite problematic to rewire terabytes of information in manual mode;
  • the third component in Big Data is analytics, namely: compilation (programming) of data models, scenarios and processing, building prototypes, modeling features, and patterns.

Only with these three components, their implementation in the internal and external processes of the organization, can we start talking about the use of big data. Read more info here:

Consumer spending models

Consumer spending models

Banks have direct access to a large amount of historical data on the structure of clients’ expenses. They know how much money was transferred to you in payroll for a given month, how much went into your savings account, how much your utility providers went to, and so on.

This provides a broad basis for further analysis. By applying filters such as holiday seasons and macroeconomic conditions, bank workers can understand whether a client’s salary is growing steadily and whether spending remains adequate. It is one of the cornerstones for risk assessment, loan quality check, mortgage risk assessment, and cross-selling of multiple financial products such as insurance.

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