Big Data solutions in banking and financial services pt. 2

In the previous article, we touched upon the topic of Big Data solutions in banking with a benefit for the client. Now, we want to look into the solutions based on Big Data specifically for banks, and to show that they are also capable of changing business processes and internal organization for the better. Innovations don’t stand still and working with data becomes a widespread practice in world organizations and the banking sector. Central banks of Europe also start using Big Data more actively on a daily basis. Management specifically pays attention to analytics and statistics, viewing them as key factors in the work of their banking systems.

So, what Big Data solutions should you consider if you are thinking about business process optimization in your bank?

1. Data structurization

It’s thought that solutions based on Big Data are in many ways used for analysis and management of client data. Still, you shouldn’t also forget that a problem may occur, which can impede the aforementioned processes - the issue of collecting all that data. A few fragmented sources, slow processing of data (which leads to low productivity) - these are the obstacles that may occur in the way of many banks. In this case, the proposed solution becomes the creation of a data warehouse.

“Guaranteeing the safekeeping of data and its unity, the warehouse allows to consolidate all the available information in one place and provide the aligned collection methodology.”

 

An additional solution can also be the automatic aggregation of data, which together with a data warehouse can significantly accelerate the report-making processes and improve the quality of the information provided for the analysis. Substantial optimization of collection, transformation, and aggregation processes will have a positive impact on the decision-making, with reports incoming on a daily basis for the executives to act promptly and timely for the further development of the company. The workload of the staff will be reduced as well, who will no longer need to collect the data manually and prepare reports on their own.

2. Corporate data warehouse optimization

Big Data solutions can often overlap with the construction or transformation of the existing data warehouses. Constantly growing volumes of incoming information, the need for their storage, and, at the same time, the rational use of funds - these are the main problems that a bank may face and may need to urgently address. In order to prevent such a situation, it’s necessary to carefully approach the choice of a data warehouse, understanding the advantages of that or this option and being able to evaluate the willingness to pay for a license or to make a choice in favor of an open-source solution.

“At the same time, open-source is in no way inferior to licensed solutions: it is able to provide high speeds for recording and reading data and provide additional benefits by duplicating information on different servers.”

 

Such actions, in turn, guarantee protection against data loss.

3. Preparation of analytics and reporting

The next step after data collection should be their analysis and reports preparation. There are several types of Big Data-based analytics that allow you to look deeper into the work of enterprises and draw up a strategy for several years ahead. Descriptive and diagnostic analytics give an understanding of the current position of a company or a bank. Predictive and prescriptive analytics tell management where to go next and what development paths they should choose in the future.

“Competent analysis and making conclusions through data processing give banks the opportunity to look into the future and build forecasts beforehand.”

 

4. Improved quality of the received data and forecasting

The more competently the data is structured, the better its quality will be. The use of various automation technologies, artificial intelligence, machine learning, data cleaning from duplication, and formatting errors, leads to a significant increase in the quality of the source data, allowing to generate reports and statistical graphs based on them. The quality of the latter, in turn, directly affects the quality of decisions made by management, making them more thoughtful and justified.

5. Risk management

In addition to building strategies for further development, Big Data can also warn about potential threats and unwanted financial transactions like investing in unprofitable business.

“Risk management is of particular importance in the field of investment banking, where, based on the information collected, the ratio of risk and reward for investing in a particular business or initiative is calculated, as well as the benefit from a potential merger or acquisition.”

 

The provision of loans also falls within the scope of risk management. Before offering a loan to current or new customers, Big Data will allow you to assess credit risks and predict the term and probability of repayment. Another possibility of using Big Data for successful business activities is the assessment of operational risks. Conclusions based on the information received will help management to identify areas of risk in the bank's activities and prevent any issues from occurring in time.

Invento Labs has rich experience in working with Big Data. More information about our solutions you can see in our cases.

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