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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...
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.
Beyond our credit-meets-technology expertise, part of what differentiates Flying Phase from our competitors is how we attack clients’ problems. Yes, the results stand on their own, but the way in which we get there – iteratively and with a focus on incremental value – plays a huge role in shaping the end result.
At Flying Phase, we take a different approach to tackling our clients' problems. Beyond our credit-meets-technology expertise, part of what differentiates Flying Phase from our competitors is how we attack clients’ problems. Yes, the results stand on their own, but the way in which we get there – iteratively and with a focus on incremental value – plays a huge role in shaping the end result. Whether in internal projects, our recruiting and marketing efforts, or our client engagements, Flying Phase brings an Agile approach to everything we do. Though born in software development, Agile has deep applicability across all...
Understanding polling and election modeling: How the behind data science and models that were seemingly off-target in 2016 are different in 2020.
Understanding polling and election modeling: How the behind data science and models that were seemingly off-target in 2016 are different in 2020. With less than a week until Election Day, key polls are pointing in the Democrats’ direction. Former Vice President Joe Biden leads in nearly every key battleground state, and pollsters and pundits say his victory is likely. For many Democrats, however, it feels like repeat territory, a PTSD of sorts from 2016, recalling pundits describing the race as “Hillary’s to lose” and assigning high chances to her victory. But by late Election Night, Donald Trump had claimed the...
Highly regulated industries can better leverage machine learning tools (and create trust in their findings) by developing monitoring systems that are holistic, insightful and actionable.
Summary Solution: Highly regulated industries can better leverage machine learning tools (and create trust in their findings) by developing monitoring systems that are holistic, insightful and actionable. Over the past decade, we’ve seen an explosion in the field of machine learning. Tech giants, online retailers, and social media platforms all have adopted machine learning strategies to evaluate data in ways that human beings and traditional modeling frameworks cannot. These new machine learning models provide a better fit for non-linear relationships, uncover unique insights and are better equipped to pick up on the nuances of a shifting environment. It’s no surprise,...
Summary Solution: Organizations often shortcut internal tech products because they don’t provide direct value to the customer. This post explores where and how to invest your time and efforts. Read a book or article on the topic of building a great product, and the process seems simple enough: Identify a customer base with an underserved […]
Summary Solution: Organizations often shortcut internal tech products because they don’t provide direct value to the customer. This post explores where and how to invest your time and efforts. Read a book or article on the topic of building a great product, and the process seems simple enough: Identify a customer base with an underserved need, present a value proposition, slap on some consumer-driven features and iterate to success. Even when we calibrate product development for an internal audience within an organization, the formula presents itself as manageable: Build an enterprise solution that solves for real problems and stop wasting...
Automated machine learning solutions drive better modeling results and a more controlled process in loss forecasting.
Case Study: Automated machine learning solutions drive better modeling results and a more controlled process in loss forecasting. A Top 10 bank relied on traditional statistics to forecast credit losses and make decisions based on those insights. Their processes were labor-intensive and complex, and as a result did not deliver insights to leadership quickly enough to drive decision-making. We reimagined this process to create a simplified platform based on extreme automation and machine learning to deliver more accurate results in half the time. We gave leadership the insights and confidence to take action. Case Study: Superior Forecasting Through Machine Learning
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.
