www.fuzzylogix.com

High Performance Analytics for Big Data

The retail banking world is characterized by data, data and more data. Account data, transaction data, clickstream data, vendor data and credit bureau data are all captured and stored, processed and aggregated, reported and modeled. IT resources have grown to meet basic customer service and regulatory requirements, and now must add to that the responsibility of mining, marketing and monetizing their data. And it’s not just big data. It’s diverse data that is scattered across systems, stored in different formats, isolated by varying account identifiers and analyzed by different business units using various rules, tools and techniques to carve up the data.

As big data gets bigger, the rewards of having a unified architecture, simpler processes, faster processing and smarter analytics grow greater. Conversely, the penalties of having disparate systems, escalating data complexity and outmoded technology spread across dueling fiefdoms also increase. Fuzzy Logix’ DB Lytix and FIN Lytix solutions can tame big data challenges by moving analytics out of a specialized server environment and into the data warehouse, allowing banks to keep their data and their analytics in one place. The result is exceptionally fast performance—up to 100X faster than conventional analytics—actionable insights in real time and a simplified systems architecture that can help banks win big in today’s challenging operating environment.

Fuzzy Logix DB Lytix™ In-Database Analytics Solutions for Retail Banks

BLAZING ANALYTICS, GAME-CHANGING BUSINESS OUTCOMES

INDUSTRY CHALLENGESThin Margins made thinner by Basel III capital requirements that increase funding costs while reducing return on assets (ROA)

Complex Reporting Requirements from industry regulators that include cross-enterprise reporting and stress test modeling, which tie up analytic and reporting talent and increase costs

Risk Management in an industry where exposure grows more time-consuming and complex each year

THE BENEFITS OF FUZZY LOGIXIt’s easy to use. The industry-standard SQL interface can be integrated with leading ODBC-compliant analytical applications and visualization tools, so you don’t need to develop new in-house skills.

It’s highly efficient. The Fuzzy Logix analytics execute in parallel inside your existing database system, minimizing data movement.

It’s proven to be effective. Fuzzy Logix includes a rich library of proven analytical algorithms to deliver accurate insights that help you grow your bottom line.

www.fuzzylogix.com

High Performance Analytics for Big Data

INDUSTRY USE CASES: FOUR REASONS TO GET FUZZYRetail banks collect an enormous amount of data that can be used to improve customer service, regulatory reporting and marketing campaigns. But monetizing that data isn’t easy. The sheer volume and complexity of banking data requires significant investments in hardware, software and specialized skills.

Simply throwing more dollars at the problem will not solve it. Instead, banks must integrate and simplify their data architecture, take advantage of high-performance processing, enhance the effectiveness of the data analysts and put the power of marketing analytics in the hands of more people. The following use cases illustrate how Fuzzy Logix can help banks do just that.

USE CASE #1: Marketing Analytics

Segmentation and targeting, forecasting and wallet share analysis are among the tried-and-true analytic techniques used by retail banks. Marketing analytics at a bank are often handled by a specialized group in the business, with highly skilled data scientists and expensive analytics software running in a dedicated environment where data can be moved to and from the data warehouse. This method produces extended wait times as data moves back and forth between the warehouse and analytics environments, making it difficult to get timely, actionable insights. Further limitations occur as banks try to summarize their results quickly, perform multiple iterations of analyses or test alternative analytic methods.

With Fuzzy Logix, banks can accelerate their time to insight while dramatically reducing their costs. Fuzzy Logix puts key modeling tools such as linear and logistic regression, time-series forecasting, clustering, support vector machine, neural networks, ARIMA and financial derivatives right in the data warehouse. Fuzzy Logix is designed to exploit the massive parallelism of the data warehouse system and eliminate the need to move data between separate storage and analytic environments. The result is faster data analysis; as much a 100X faster than conventional analytic methods. And because Fuzzy Logix’ functions are invoked through traditional SQL, analytics can be run by business analysts instead of expensive data scientists.

USE CASE #2: Name Matching

Name and account matching are key regulatory and data management activities at every bank; they’re critical for compliance, exposure analysis and customer service. Sorting through endless variations in naming conventions, spelling and data capture errors can pose a major processing challenge, however. Most solutions available in today’s market struggle to meet the demands of matching very large volumes of names with a high degree of accuracy. The Fuzzy Logix Name Matching solution is specifically designed to fully balance the demands of matching large volumes of data with a high degree of accuracy, helping banks unlock the revenue potential of their data. Name-matching algorithms in the Fuzzy Logix library include Jaro-Winkler, Levenshtein and others. Matching criteria are highly customizable with Fuzzy Logix, giving banks the flexibility they need to tailor solutions to their specific business challenges.

www.fuzzylogix.com

High Performance Analytics for Big Data

USE CASE #3: Reporting

When using data mining to support business reporting, there is often a tremendous amount of data movement between the analytics server and the data warehouse. To support this, banks need to buy more servers and more software to support both environments. In addition, banks must duplicate their data and store it in separate systems, which can create compliance and data governance issues down the road. With Fuzzy Logix Reporting, all of the data mining queries, calculations and subsequent reporting are performed within the data warehouse environment rather than splitting the process across multiple platforms. This centralization of data results in better data governance and a “single version of the truth” in banking data.

USE CASE #4: Risk Modeling

Credit models are an integral component of bank risk management, helping to identify appropriate pricing and credit limits as well as value at risk (VaR). Market risk assessment is key to portfolio management and trading. The common practice among banks entails first moving a sample of data to a dedicated analytics server (or servers) for credit and market risk modeling, and then loading the results into yet another environment for reporting. This is a sub-optimal solution, with process lags that mean insights are not timely enough to act upon.

Using Fuzzy Logix vastly reduces the processing time and can be used for a variety of models including regression, simulation, time series, equities, fixed income and VaR. Because of the speed and scalability of in-database analytics, sampling is not necessary. Real-time analysis ensures insights are fresh, which translates into greater accuracy and improved profits.

We accelerate analytics. We are a growing team of data scientists, engineers and marketers that live to discover interesting patterns in super large data sets in record time, and with a purpose: to make this science available to any business function within an organization, in an easy to apply manner. We are all about making analytics on big data pervasive, real time, and approachable. Visit us at www.fuzzylogix.com.

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