Nearly a year ago, Hector Rubalcava was hired into the role of director of finance at OCC with the goal of advancing the finance unit using industry best practices converted to best fit approaches. “After some turnover in the past year, we’re starting fresh with a lot of new people,” he said.
And where did he get those industry best practices? “I have been applying the learnings from the Certified Corporate FP&A Practitioner (FPAC) program in so many different ways, angles and styles,” said Rubalcava. As a member of AFP’s FP&A Advisory Council, Hector is able to gain a clear understanding of the needs and challenges other FP&A professionals are dealing with, and he is then able to take their advice and insight and apply it to his own organization. “If you were to ask me for a list of everything I've been able to roll out, it's a very long one with many success stories,” he said.
Rubalcava has been working to increase the data capabilities of the finance team at OCC and explains his path and success in this conversation.
Building a relationship with the “data scientists”
When Rubalcava arrived at OCC, the finance department was not fully leveraging OCC’s data science capabilities. Hector connected with a data scientist working within the Financial Risk Oversight team under the Corporate Risk Management division, Masoud Valizadeh. Valizadeh was someone who was able to identify, connect, and get buy-in and trust in the data as he provides continuous research, modeling, and programming for the needs of the overall business.
Forging the relationship was a little burdensome at first. It meant new projects and more work for both Rubalcava and Valizadeh. They started with small efforts, including a project to build an innovative and interactive dashboard. As they got to know one another, Rubalcava shared how he was trying to revamp his own department and how he could use the data scientist’s “intelligence and insights to create more effective data modeling and predictive analytics that would aid our units resulting in actionable insights for decision-makers.”
In the end, it was a great connection that turned into a win-win situation filled with continuous learnings and opportunities. “We were able to implement best practices from each field that converted into value creation for the organization,” said Rubalcava. Working together, they were able to address the needs of their executive team and build a tool that forged data science with the FP&A perspective, providing a comprehensive assessment. “We built a very tight relationship that became the platform for additional creative projects,” said Rubalcava.
Finance continues to face ongoing challenges within the world of data science. However, Rubalcava sees this as an opportunity: “The great news is that these obstacles can become valuable opportunities for those who are willing to disrupt the norm; build a culture of data analytics; explore existing resources; learn new tools, methods and techniques; and completely adopt continuous improvement as well as a learning mindset.”
Data science is NOT data analysis
“Some people say that data science and data analytics are basically the same thing. That’s not correct,” said Rubalcava. “There is a different skill set, scope and overall goal for each field.” Narrowing down the universe of disparate data into action can be thought of as a funnel. It starts with a business question or challenge to guide the effort.
Generally speaking, data science starts at the top of the funnel, pulling together big, disparate data sets, cleaning them up, and trying to discover new insights. One applies advanced statistics, data modeling, and heavy engineering using languages like Python or R, and iterates on the results with the finance or business user.
Data analytics is mid-to bottom of the funnel, further refining, interpreting and applying the data to business cases. “The key to the last stop is to craft compelling stories with a focus on influencing decision-making practices.” The relevant skills are to understand the data lifecycle, interpret statistics, use BI tools and apply some programming skills like SQL. This team also has the option to convert this process from a one-off exercise to a routine application.
Example 1: More detail delivered more often increases agility
One major strategic project Rubalcava and Valizadeh collaborated on was a tool to project OCC’s Clearing Fee while targeting a specified operating margin. The end-to-end process includes three main steps. The first step is leveraging historical daily volume data and using mathematical modeling to project future average daily and aggregated volume. A Holt-Winters decomposition was used to capture potential seasonality of data. In addition to the expected volume, the methodology provides volumes associated with different confidence levels — projected volume decreases as the confidence level increases.
As the second step, the volume associated with different confidence levels and other inputs (e.g., future expenses, future non-clearing revenue, underspent amount) is used to project the required Clearing Fee to target a specified operating margin.
The third step is putting the data to work by bringing it to the business the data produced by prior steps and visualizing it using a business intelligence tool. Several interactive dashboards were designed with input parameters allowing various Clearing Fee outputs at different confidence levels. Prior to the deployment of this enhanced tool, it was all done manually and without agility. Within the volume projection approach, a confidence level matrix displays different aggregated volumes for different confidence levels.
According to Paul Jasper, executive director of financial risk oversight at OCC, “The entire process can be recomputed quarterly for the firm to recommend a Clearing Fee adjustment.”
“It was a very successful project and a great company win,” said Rubalcava.
Example 2: Deeper insights
“Volume is everything here at OCC,” said Rubalcava, and this idea became the basis for another collaboration effort. However, volume is broken down into three core groups, Equity Options, Non-Equity Options and Futures.
Because the volumes in the last couple of years have been anomalous and Finance had the need to better project several driver-based volume line items, Rubalcava wanted dashboards that would break down the mix of option trades into various categories. For example, OCC sees a lot of trading activity in the retail sector as individuals hedge their positions against volatility. There is a different fee structure for trades of large contract quantities, or capped transactions, versus trades of small contract quantities, or uncapped transactions. Rubalcava wanted to be able to dig and slice the data into more detail so he could ultimately visualize some of those analytics, examine them, reconcile the fluctuations and be more comfortable with the outlook.
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Another important subject to track is the nature of options trading. At OCC, there are statistical metrics that are leveraged to monitor potential changes. Historically, there is a strong positive correlation between transaction and contracts volume at OCC. A large change in these values (linear, polynomial or non-conditional correlations) can be an indicator of a change in the nature of the market. As an example, these values changed during the pandemic. In addition to growth in contracts volume, OCC saw a strong rally in the transaction volume during that time. Because transaction volume had a higher growth factor compared to the contract volume, the contracts per trade ratio decreased during that time. This effect was later explained by an increase in the number of retail traders during the pandemic.
Success builds on success
“One of FP&A’s strategies at OCC is to solidify a sustainable relationship with our data scientist (Masoud Valizadeh) to leverage tools, intelligence, creativeness and ultimately design automated dashboards and enhanced reporting analytics for faster decision-making. I have the pleasure to elevate our Finance unit as a trusted business partner and along with Masoud, we have pivoted to requests from Senior Management and deployed two innovative and impactful solutions,” said Rubalcava. While performing this work, Rubalcava and Valizadeh were able to actively collaborate and display their company’s value and principles. They were able to enhance the firm’s balanced scorecard, foster a data-driven culture, amplify Finance’s value proposition, and build capabilities within their functions by pushing for change.
Required skills for FP&A
Data analytics is key for finance teams and required for success; “I want my team to have programming and modeling experience,” said Rubalcava. Interestingly, he finds that data skills align with personality traits. Rubalcava gives top marks to people who are curious, passionate and creative because “they can play around with data, tools and systems and explore new insights while having fun.”
Recalling that data projects begin with a business question or a major challenge, Rubalcava also looks for employees who can build relationships within the business, introduce themselves to their internal stakeholders, and take the time to understand their overall needs and challenges. They are the source and the end users of the data projects!
“I don't see a time where everything will be perfect and I'm done,” he said. “There’s always room to improve from a reporting, processing, relationship management, partnership and learning perspective. We need to ask ourselves: How do we empower ourselves and our team in such a way that we can continuously grow? How do we automate the routine, decommission non-value-added activity, and reallocate that time to analysis? And how do we have more fun? We're spending a substantial amount of our day, and our life, at work. So, we need to figure out what elements we need to add to ensure we keep disrupting, transforming and more importantly evolving to address the many ongoing internal as well as external demands.”