Diving Deeper

Exploratory Data Analysis

Knowing and understanding each piece of data you have is key to producing meaningful results that have practical application. Exploratory data analysis, often the first step in data analysis and modeling, is an investigative process that gives you a feel for data sets, enabling you to see patterns, spot anomalies, test hypotheses, and check assumptions.

Part One: The Importance of Knowing What You Know. Neighbor A picks up his cup of coffee on Saturday morning, walks out the back door, turns on the spigot, and waters a bed of azaleas a crew of landscapers planted the day before. Rising with the sun, Neighbor B slips on her Muckster clogs, grabs her shovel, shears, and compost from the shed, and goes to her knees in the flower bed she tilled yesterday, preparing holes and soil so that the shrubs she plants there will thrive. Which person is the true gardener? This isn’t a trick question. Switching...

Increasing Confidence in Model Performance Monitoring Through Automation

When an information services company discovered its model monitoring process was fraught with inefficiencies and roadblocks, they needed to redesign the platform to support automation, provide confidence in results, and facilitate widespread adoption throughout the organization.

Case Study: The solution, which reduced the cost and improved the efficiency of model performance monitoring, eliminated single-person dependency to operate the process, reduced the number of analysts needed to monitor the model, and dramatically enhanced the coverage and speed of the execution cycle. A top-to-bottom audit of the new model performancemonitoring process within months of implementation resulted in no findings, giving leadership comfort in the rigor and reliability of the system and confidence to act on modeling insights that drive business.

Making Data Dynamic

The graphical representation of data translates information from a computer readable format into a dynamic story, providing users with key insights that help them understand trends, recognize patterns, identify outliers, and, ultimately, make business decisions.

Using Data Visualization to Tell Your Story We all use words to communicate. And often, in an effort to enhance the impact of our message, we rely on nonverbal gestures – hand waving and eyebrow raising, smiles and frowns. The dynamic relationship between word and gesture not only facilitates communication, but it also improves audience cognition. Research shows that gesture not only contributes “essential information to a message but also actively facilitates the cognitive formation of messages and . . . communicates unique information that is not present in the speech signal.” The same dynamic relationship exists between data and data visualization....

Reducing Risk, Lowering Cost in Credit Card Authorization Decisions

Having already invested heavily to create a robust authorization platform, the bank was prepared to increase its stake in technology with an additional $1.2M. After investigating the problem, Flying Phase provided the bank with a roadmap to save between $500K and $1M in fraud costs and credit-risk approvals by rebuilding the back-up decision logic found within the third-party processor.

Case Study: A top 10 bank that processed more than seven billion credit card transactions annually became concerned about its exposure to risk when it discovered that two million of those were decisioned with incomplete information. Having already invested heavily to create a robust authorization platform, the bank was prepared to increase its stake in technology with an additional $1.2M. After investigating the problem, Flying Phase provided the bank with a roadmap to save between $500K and $1M in fraud costs and credit-risk approvals by rebuilding the back-up decision logic found within the third-party processor. It also re-allocated the $1.2M...

Helping Financial Services Organizations Leverage Data to Gain Competitive Advantage –

A Data Management Playbook for Success – Your bank’s data can help you enhance profitability, build new products, minimize exposure to risk, and deepen customer relationships. But it has to be expertly leveraged. For that, you need the expertise of experienced data practitioners and a focus on outcomes. “Helping Financial Services Organizations Leverage Data to Gain Competitive Advantage” explains how to transform vision into action as you develop and implement a maximum impact, cost-effective data management strategy for your organization.

A Data Management Playbook for Success. The urgent need to manage data effectively is only going to intensify in the months and years ahead as financial services organizations seek to enhance profitability, build new products, minimize exposure to risk and deepen customer relationships. To help you leverage data so that your organization can achieve these outcomes and gain a competitive advantage in the market, we’ve created “Helping Financial Services Organizations Leverage Data to Gain Competitive Advantage.” This playbook begins by explaining how you can assess the current business environment and make sure your organization is ready for the challenge. It...

Make Big Data Smart Data (Part 3)

In the third and final installment of “Making Big Data Smart Data” we provide banking leaders with a practical guide that identifies key milestones, along with several best practices, as they drive toward a smart data management strategy.

Part 3: Now What? This third and final installment of “Making Big Data Smart Data” provides practical guidance to senior-level banking leaders – the head of consumer credit card, for instance – who understand the need to design and execute a data management strategy. We’ll look at the process of how we help clients put their data to work . . . making sure “all the holes line up” as they assemble a solution that enables them to leverage data and gain a competitive edge. What have we covered up to this point? For starters, vision is wonderful, but planning...

Leveraging Credit Expertise to Forecast Customer Hardship and Minimize Bank Charge-Offs

When the COVID-19 pandemic slowed the world’s economy to a crawl, many banks relied on models and forecasting to anticipate customer delinquencies and hardship and to efficiently staff its call centers.

Case Study:  The recoveries and customer support group at a Top 25 bank found itself facing a turbulent economic landscape in 2020 when the COVID-19 pandemic slowed the entire world’s economy to a crawl. When the COVID-19 pandemic slowed the world’s economy to a crawl, many banks relied on models and forecasting to anticipate customer hardship and efficiently staff its call centers. Appropriate staffing is critical for banks to effectively assist customers who have fallen behind in payments or who needed assistance with forbearance or deferral programs. This assistance helps customers get out of delinquency and avoid defaults, and helps...

Make Big Data Smart Data (Part 2)

While financial services organizations have spent significant time and money collecting data on products, customers, and transactions, they have put only fractional amounts of that information to work. Now is the time to make big data smart data. In this post, we provide 4 considerations to ready your organization.

Part 2: The Key to Becoming a Data-driven Enterprise Your competitors are working vigorously to execute a data management strategy that will enable them to dig deeper and pull out insights that enhance profitability, build new products, minimize exposure to risk, and deepen customer relationships. Data management on its own is probably not enough. It’s what you can do with data once you have a handle on it. Today’s small banks and startup FinTechs are also on the move, not hamstrung by twenty years of infrastructure accumulation. They will grow quickly because they are innovative, and they are nimble. Your...

BLOG > EXTREME AUTOMATION

The Process Behind Process Automation

From EUCs to structured, automated data, we walk you through the process, best practices and change management to ensure you’re leveraging analysts’ skillsets to the fullest.

Since its introduction in September 1987, Excel has been the go-to resource in an analyst’s toolkit due to its versatility and capacity to quickly translate complex formulas into illustrative tables and charts. Over the ensuing years, a typical financial services analyst would build elaborate, interwoven spreadsheets, simple models, assumptions and calculations, all in Excel, creating end-user computing (EUC) applications. And while these EUCs built the foundation for modern automation, generally speaking, they lacked traceability or documentation, making model adjustments challenging and presenting serious challenges for governance, risk and regulatory compliance functions.