Disruptive technologies that emerged recently are driving a major increase in the number of financial transaction devices. Even more, their number is growing in proportion to the users’ capabilities. On the other hand, more reliable information is required to correctly assess customer needs for individual products. That leads to an increase in the amount of data that requires the high-quality collection, structuring, and analysis.
It is predicted that the average annual growth rate of predictive analytics in the banking sector until 2026 will not fall below 22%. In this article, DICEUS experts examine the tasks of big data in banking, possible related issues, and ways to implement efficient big data use strategies.
If earlier bank employees knew customers by sight, the situation has changed radically today. These are fundamentally new service models. Services are offered and provided remotely. As a result, their number grows, whereas paper media slows down processes, and it becomes more difficult for banks to win the favor of customers.
The task is solved by the total collection of digital personal data. A user electronic portrait is formed — the information is collected from all available sources. The analysis of user behavior also generates additional amounts of data, but online monitoring is indispensable.
Big data is an ever-growing volume of information of varying structures and formats. Some of its major sources may include:
Conventional computer systems are not trained to work with such a variety of data sources, and they can’t cope with them appropriately.
A traditional bank has always been associated with painstaking work with financial securities. However, new technologies have changed the way bank employees work. In recent years, convenient, personalized, and secure solutions have emerged that have never been native to the industry. First of all, these are AI and ML, which are developing at a frantic pace today in various areas.
The use of big data in banking makes it possible to improve service quality and stimulate customer flow. That leads to the emergence of new products that better meet current requirements. In turn, this drives demand, sales, and retention.
The analysis provides development prospects by improving decision-making and responding to requests. While appreciating convenience, customers are more likely to use mobile apps. In turn, all this requires the processing of larger amounts of information online. Transactions are accelerated and simplified, so there will inevitably be more of them. As a result, the entire industry is modernizing, becoming more profitable, and diving deeper into the latest innovations.
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One of the most promising areas today is the in-depth study of user data. Actually, this is a kind of correspondence dialogue with a client, which allows identifying their requests and, on their basis, providing recommendations and services.
For this, AI-based applications are used; they provide recommendations for reducing costs, preserving savings, and investing. For example, a well-structured notification system works selectively, making it easier for users, helping them pay for services on time, avoiding erroneous payments, etc.
Tracking transactions in real time helps you identify habits, optimize performance and predict profit growth, patterns in consumer behavior, and offer services at the right time. This increases conversion rates.
A modern user should receive an answer to any of their questions around the clock. Robots help in this matter. Requests from gadgets are processed as quickly as if the client was directly in the department. Moreover, full-on virtual banks are already working perfectly, having abandoned the usual branches with cash desks and other inherent attributes.
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It just so happened, but it is an indisputable fact. Due to the specifics of social networks, today’s customers are more willing to share confidential information. Perhaps their attitude towards this issue will not change in the near future. This means that its value will increase. Just one good analysis of mobile app or social media activity can replace costly and lengthy surveys.
Comprehensive and in-depth analysis allows you to evaluate receipts to accounts and draw conclusions about the standard level of income/expenses, which sources of income are more stable, and through which channels the client prefers to conduct transactions.
Understanding the nuances of the user environment allows you to draw conclusions about possible risks and prevent fraud. This makes it easier to make informed decisions, including on the possibility of extending loans and assessing the associated risks. In addition, it helps to cope with compliance checks, audits, reporting and reduces overhead costs.
To summarize, banks are using new technologies for the following tasks:
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As is the case with everything new and complex, the use of big data in the banking sector can have certain problems defined below.
The banking sector innovates too quickly. As a result, most of the existing systems are unable to cope with the growing workload. In collecting, storing, and analyzing data, outdated infrastructure becomes an obstacle that jeopardizes system stability and the implementation of advanced analytics tools.
The use of systems that are not designed for big data creates risks. The security system must guarantee the sturdy protection of incoming user information.
When processing a large number of different types of data, there is a possibility of encountering certain technical difficulties. Implementation of big data is in the interests of any financial institution, but some things require the high professionalism of the staff. Therefore, banks should first consider upgrading their existing infrastructure before embarking on a big data strategy.
Inaccurate or incomplete data can interfere with results. Banks must analyze and consolidate existing data before it enters the system. So, data quality management should be a priority.
The industry is governed by strict regulatory requirements such as the Fundamental Trading Book Review (FRTB), for instance. Those tend to be scrupulous about privacy, access to user data, and speed of reporting. This can significantly slow down the transition to new technologies; however, there is no other way.
To use big data, a financial institution must be mature enough, both from a business and IT perspective. Artificially, without the direct need and the existing infrastructure, this is impossible.
DICEUS provides high-quality outsourcing services for handling big data in the banking industry, considering your existing infrastructure, functional and non-functional requirements, project risks, and other risks that may impact the outcomes.
Our experts will consult you on seamlessly transforming your data, considering all opportunities as price, operating costs, efficiency, loyalty, and many more. We can help you do the following.
We are dedicated to delivering high-quality results. Our experience will help you improve security, make reliable predictions, facilitate secure data sharing, and increase customer satisfaction. Our experts will advise you on the best practices and approaches to implementing big data technology. They will also provide you with a step-by-step strategy for your project.
Big data in the banking industry solutions will enhance security through natural language processing, voice recognition, and machine learning. Our support team operates on social media, so they respond quickly to requests and generate valuable data to identify your strengths and weaknesses.
You will be able to anticipate desires, improve personalization, achieve increased customer loyalty, as well as your own competitiveness.
Experts in big data software will advise you, design, develop and test custom software based on your business goals.
We take a holistic approach to developing big data products in the banking sector according to specifications. We specialize in advanced analytics solutions. Our focus is on data warehouses, 360-degree customer view systems, data mining, and business intelligence reporting.
The support includes regular checks and reviews to ensure the solution is working correctly and delivering the expected business value to your company. Based on the results of maintenance, you receive reports with predefined indicators and recommendations for improvements.
The timing and cost of the project depend on the following factors:
We will be happy to learn about your project goals, so feel free to contact us for a free 30-minute strategy session. If you need an effective strategy for using big data in banking, our big data expert will join the call.