How Data Evaluation Technology Can Provide a Competitive Advantage for Financial Institutes

• The increasing significance of data and information gathering for financial institutes

• Innovative technologies lead to a rapid acceleration in the processing of large amounts of data

• Advantages especially in the fields of risk and fraud management

• Data analysis as the basis for the personalization of the product spectrum

• Potential of increasing customer satisfaction and loyalty through an individual, information-based relationship

The amount of electronically stored data is growing rapidly. Between 2010 and 2012, the amount of data stored worldwide increased by more than half to a total of 2.8 zettabyte. Between 1999 and 2003, the annual yearly growth of stored data was around 30%. This creates a challenge for actors in terms of data processing and evaluation, particularly for information gathering purposes. The difficulty lies in processing the amounts of data to generate information and knowledge from it. Effectively using the existing structured and unstructured data can provide companies with a decided information and knowledge advantage, and with it, a decided competitive edge. This is becoming increasingly important for financial institutes as well, which can use the potential within data evaluation to benefit their business.

The actors’ goal must be to carefully evaluate the existing, diverse data so that the generated information advantage leads to a significant improvement in established processes on the one hand, and, on the other, to new opportunities for action and new business models. Until now, this was problematic due to the immense amounts of data, particularly in terms of speed, as the data could only be processed and evaluated at a limited speed. Modern technologies such as SAP HANA and Oracle Exalytics provide a solution for improving the speed of analysis thanks to the use of in-memory databases.

Due to better access speed, in-memory technology uses the RAM (random access memory) instead of the hard drive to (temporarily) store data, which enables significant performance advantages in comparison to regular database technologies. At the moment, such solutions are gaining in importance, particularly within the analytical field.

Figure: Acceleration of processing times through in-memory Technologies

Risk/liquidity management is a central function of banks that could profit from the greatly improved analysis speeds. Nowadays, risk evaluations usually require several hours and are not conducted throughout the day, but undertaken once a day in the context of end-of-day processing. By accelerating individual transactions on the one hand, and increasing the number of transactions on the other, noticeably faster and, as a result, more precise results can be achieved. The required risk buffer of the institutes can thus be measured more exactly and efficiently, which leads to a liquidity advantage. This increased speed also plays a role within the field of fraud detection. In-memory databases make it possible to search through extremely large volumes of data with rules and predictive algorithms for evidence of fraud practically in real time, and thereby lead to a direct increase in efficiency in fraud detection and fraud prevention.

The use of precise data analysis and information gathering is also helpful with regard to the expansion of online and mobile banking channels. Online and mobile banking channels have greatly gained in significance over the last few years and are now a fixed part of financial institutes’ multi-channel and omni-channel strategies. The volume of online business has grown by almost 50% in the last two years, in contrast to the declining traditional modes of business.

Within the framework of online and mobile banking, the provision of information plays a large role with regard to two aspects in particular: For banks, it is important to evaluate and use the data gained from customers in order to build up an individual customer relationship. At the same time, it is important for the customer that his/her financial institute function as a partner who prepares information in such a way that information overkill is avoided.

Online banking customers use manifold possibilities for finding information and no longer blindly depend on the products offered by banks. They are more flexible and more willing to switch. It is therefore necessary for financial institutes, particularly in the field of online and mobile services, to retain customers through the creation of a personal relationship. Although the number of interactions with customers has increased via mobile channels, the personalization of the relationship has lately been in decline.

The institutes must therefore aim to personalize their products and to provide their clients with a tangible added value. Data mining plays a role here too. The data gathered within the framework of mobile banking can be used to analyze customer behavior and customer needs. According to a Sybase survey, nearly 60% of respondents were of the opinion that institutes are currently not using this option to its fullest extent. This despite the fact that the possible added value of such analysis – not only in terms of customer loyalty, which is difficult to measure – is enormous. Individual providers claim that the provision of personalized offers has led to increases in revenue of up to 12% per customer.

In addition to personalized offers, banks can formulate general information services tailored to the needs of their customers. Despite the wealth of information and actively requested product choices, many customers are overwhelmed by banking products, particularly when it comes to complex financial services and insurance products, and require additional advice. One promising differentiation characteristic is the option of personal communication, such as via online chat. Through direct, timely advice, the number of product purchases completed online can be massively increased. A detailed client profile can also be of great help in this situation, as bank employees can tailor their advice for the client without personally knowing him or her.

The possible profit from sensible processing of already existing data as well as the effective gathering of new information can contribute to competitive advantages in different business areas. Financial services businesses should be aware of the value of their data. At the same time, a balance must be kept between gaining an advantage and maintaining data security and integrity.

In order to use the existing potential of their data, it is vital that financial institutes create the prerequisites in terms of system technology and business organization to allow efficient data usage. Outdated legacy systems must be modernized through the standardization of interfaces in order to create a flexible and future-proof IT landscape that enables the integration of modern technologies. The accompanying standardization of processes can lead to a significant reduction in process costs. Simultaneously, financial institutes must concentrate more than ever before on the growing mobile business sector.

Without a doubt, this technological change within comprehensive data evaluation brings with it a number of challenges for financial institutes. While this topic remains in the distant future for many institutes, some will begin to use the new technologies, particularly with the aim of optimizing and expanding their business.

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