Banks get data visualization boost with SAS Visual Analytics



As the banking industry generates more and more data, reporting layers that are easier to understand, navigate, and digest are increasingly in demand by end users. Since 2012, SAS has been aggressively developing the visual layer of analytics, and banks can now benefit from a more intuitive visual interface that does not require a high level of technical skill to use. Users can generate reports on the fly with in-memory technology, sourcing large quantities of data and leveraging the browser/client-server architecture on PCs or mobile devices.

SAS’s visual interface is expected to proliferate and, in the longer term, become standard across SAS’s dedicated banking solutions. With this move, SAS has become an important vendor to consider: it offers not only statistical tools and model building capabilities, but also the visual interface layer that is needed to “translate” data from machine language to human language.

SAS’s existing analytical platforms get an advanced visualization layer

Launched in 2012, SAS Visual Analytics is now one of the vendor’s focus areas, receiving a significant research and development boost. In essence, the solution provides highly intuitive and fast data visualization capabilities. The move is a significant change for the vendor, which has traditionally developed analytics that are directed at technically proficient users who can handle statistical tools and model building.

SAS’s solution was designed from the ground up to perform high-speed analytical calculations in-memory, going far beyond traditional query and reporting capabilities. It offers the ability to visually explore large data sets quickly and efficiently on a PC using a web browser or on a mobile device via a hybrid app or web browser. The solution is based on the SAS LASR Analytic Server, which greatly accelerates analytic computations. Once data is loaded onto the server, users can interactively explore it to gain insights and discover patterns and trends for further analysis.

This approach democratizes access to information, as the range of users can be extended beyond the tech-savvy. The solution has a high level of automation and presumed configuration, so features such as on-the-fly forecasting, auto-charting, and drag-and-drop capabilities are provided via a user-friendly interface and can be used immediately, without the need to develop SQL code.

SAS’s solution is effectively a visualization layer that can be targeted at a broader range of users on a broader array of devices. Many banks use SAS’s modeling tools and face the challenge of integrating them with a separate business intelligence (BI) layer; this solution can help them to overcome this challenge. Even though the market for BI and analytics seems to be mature, and a number of such tools are widely deployed, integrating systems is a challenge for many banks that have, over time, built custom and often complex reporting tools, and many projects fail to achieve expected results. Further “merging” of BI and analytics can facilitate the enhancement of reporting capabilities. Additionally, the visual layer is expected to proliferate and become standard across SAS’s banking software portfolio, including the areas dedicated to Fraud and Financial Crime, Risk and Capital Management, and Customer Insight.

Realtime capabilities and ease of use will widen the adoption of data insight tools

BI and analytics are already popular in the banking world, although typically implemented to deal with separate issues. For many banks, the goal now is to build an automated intelligence solution – to automate as much as possible the manual work that is done by business analysts and to optimize the level of reporting output – making reports shorter and more informative and intuitive. Banks still face many data-related challenges, such as the inability to create on-demand access and the lack of consistency, trust, relevance, and completeness. Realtime access is very difficult to achieve, and typically, business analysts have access today to yesterday’s data. In implementing BI and analytics tools, a major challenge is the lack of education and training; many vendors mainly focus on the software implementation and then they disappear. There is a huge gap in training and a reluctance to learn how to use such tools – most employees will avoid them and many simply hate working with data. The widespread implementation of BI tools in a bank is currently highly unlikely to reach the bank’s expected level of adoption.

The visual analytics approach may be a game-changer for banks that need to overcome these challenges, especially those that want to gain an advantage with Big Data technologies and leverage a wide array of data sources for better understanding and decision-making. Cosmos Bank in Taiwan is one that has implemented SAS Visual Analytics for its customer intelligence and risk management areas, effectively deploying a Big Data solution with instant access to large stores of information and data. The solution has reduced report preparation time, and it provides bank executives with instant access, via a PC or a tablet, to the latest insights based on the latest data. It also provides the ability to analyze data instantly, which may be critical, and this requires greater risk controls to be put in place. Report users can now run their own analyses and discovery without requiring extensive IT or business analyst support, while end users can generate and share their own reports, dashboards, and visualizations simply by clicking on or drilling down into items of interest, for example to break down cash-card issuance by branches or representatives and gain insights that help improve sales effectiveness.

According to representatives of Cosmos, this is an “era of visualization,” and the goal for the institution is to “provide ranking officers and board members with eye-catching tables and charts that help them quickly grasp the meaning of the data provided and make informed decisions.” Better insight of the data also makes it easier for the bank to understand customer behavior and avoid making difficult business decisions such as whether or not to engage in price competition, instead focusing on business that makes sense from a risk-return perspective.




Jaroslaw Knapik, Senior Analyst, Financial Services Technology


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