Diving Deeper

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...

Make Big Data Smart Data (Part 1)

Enhance profitability, build new products, minimize exposure to risk, and deepen customer relationships by transforming your bank from a legacy organization into an information-based, data-driven enterprise.

Part 1: What’s the Forecast? “Regarding the weather, so much is said about it, but so little is done.” That familiar quip comes to mind as I think about data management in today’s financial services environment. An industry statistic that has received about as much play as the comment regarding weather is startling: while 90 percent of global data was created in the past five years, only 1 percent of it has been analyzed. The rest? It’s siloed into databases and file systems, hopefully secure, holding the secrets to critical insights that could translate into more profitable products, better customer...

Leveraging Data Infrastructure and Governance to Speed Model Development and Execution

Architected data platforms provide a foundation for automated modeling and forecasting to reduce cycle times and ensure well-managed data inputs.

Case Study: Architected data platforms provide a foundation for automated modeling and forecasting to reduce cycle times and ensure well-managed data inputs. The model development team at a Top 10 bank was responsible for forecasting on a $100 billion loan portfolio, but development efforts were often stalled by the complexity in data, and created compliance issues in the process. We implemented a data platform for providing authoritative and high-quality data to streamline the model development process and ensure the bank stays compliant and well-managed. Case Study: Leveraging Data Infrastructure and Governance to Speed Model Development and Execution

Agile is in Everything We Do

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...

Consumer Loan Defaults: Should Banks Continue to Hold Elevated Reserves?

Allowing for loan losses during COVID: Banks prepared for delinquencies during COVID uncertainty, but the expected wave of losses has not come. Will it?

Allowing for loan losses during COVID: Banks prepared for delinquencies during COVID uncertainty, but the expected wave of losses has not come. Will it? As states locked down, retail businesses were shuttered, and office workers who still had jobs learned to work from their kitchen tables via Zoom, credit analysts in the banking sector rushed to project the swell of consumer defaults on the horizon that would be driven by the COVID-19 pandemic. The reality is that, nine months later, chargeoff rates are stable and delinquency rates are down more than 15%. If the anticipated defaults never materialize, the result would...

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.