Count Us In on More Data Analytics

Corios transitions from pandemic to post-recession data analytics storytelling

What a weird several years we’ve all experienced. The pandemic presented all businesses with unique circumstances to overcome. It is possible the biggest challenge was staying the course in data analytics transformation efforts while pushing all employees into the digital landscape full time. Even as we managed to keep Corios working through COVID, we chose to stand up to adversity and consider our options beyond simply riding out the turmoil.

Would we be satisfied with the pre-pandemic status quo? Or would we instead choose to mature and grow our management analytics business?

When 2022 arrived, we chose evolution of the Corios Way. We retained our core team throughout the pandemic, so, instead of a rebuild, our expansion is underway with purpose and a recommitment to our storytelling vision.

We are still here, simplifying the complex in data strategy and humanizing the mechanical in analytics for high business value. And there is so much more to do.

Way beyond Portland: Living values in hybrid mode

The pandemic put a spotlight on heavy culture shifts for a lot of firms. Work from home, remote connectivity and the cloud were not yet widely adopted or operational. Our own dedication to the cloud for us and our clients – in SOC2- and PCI-compliant management controls, made for a smooth shift to Work-From-Wherever.

Now, as some of the “old ways” of office culture are coming back, not only are we celebrating team events in person in our Portland HQ, but we are also adding work from work locations in new markets. In October we opened a new office in Denver, where Austin Barber leads our Credit Risk and Compliance practice. In 2023, we will continue to explore where in the U.S. we can put down more roots as we expand the team and serve new clients.

Simplifying access to Corios data analytics solutions

Speaking of Corios practices, delivery of our service and solution capabilities is now restructured into three areas that better reflect the way our clients seek out data and analytics project support. After five years mostly dedicated to the Analytics Modernization practice, I have turned over that responsibility to Tallack Graser. In addition to the Credit Risk and Compliance practice, we formalized analytics roadmap and operational analytics (think marketing, customer value) into a single strategic support offering for clients.

On the energy side, Corios VP John Willey’s knowledge and leadership highlights the natural structuring of a Utility Analytics practice serving electric utilities. With a sound solution-approach in Corios Lightning, created based on the energy needs of California, we are well-poised to meet the unique circumstances driving analytics for electricity suppliers anywhere in the U.S.

Each practice focuses on our domain expertise, application of analytics solutions and account management in the business areas where we are strongest. This establishes a platform for company growth that anticipates the data and analytics demands our clients will face in the future.

What’s not changing? The Corios Way – our approach for how we help clients migrate functional, secure, and compliant workloads to the cloud and open source environments. More than just a process, we demystify the complexity in a fundamentally different solution to tackling these giant tasks.

From human brand to data transformation solution

We anchor our data and analytics transformation expertise and leadership with four software-based solutions created under the Corios Rosetta brand banner. These provide an instrumental foundation for our clients as well as our delivery team to reduce time to market, reduce cost and improve operational risk for analytics cloud migration and modernization.

Our next motion for Rosetta is investments that make the solutions more accessible through partners like Amazon Web Services (AWS). Giving companies an entrée into the scope of their analytics migration is the natural extension of our commitment to keeping the task as transparent as possible. And making the task more human – for both broad modernization initiatives as well as focused and near mission critical efforts like the ones John continues to lead in the utilities sector.

Beyond the technology and resource benefits, the more important outcome our clients experience is unifying different cultural threads previously fragmented and splintered across their organization. The way clients and the Corios team engage using Rosetta helps reconfirm analytics is a team sport, played by real people, not algorithms.

Real people are at the core of building our own team consistently and prudently. So even as we are in growth mode, we are carefully adding the right new people.

Notable analytics talent is coming out of the pandemic woodwork

As we close out 2022 our team has already grown. We have expanded our project resources with two energetic and focused associates already hard at work on several client projects. Steven Maxwell and Ria Kim bring diverse backgrounds and work experiences to the Corios team and yet both fit easily into the weave of the company culture.

Key to company trajectory, we also added two new team members focused on growing our brand and market value. Big goals for the future and our solutions demand greater attention on our go-to-market strategy and building stronger relationships with both customers and partners alike. Adding Amelia Johnson-Lewis and Jason Kempson in marketing and sales gives us the first coordinated team approach and is already showing us that scaling a boutique firm is more than do-able.

The Song Remains the Same: stay sharp, serve with purpose and multiply value

I see the market and companies of all sizes fully grasping the shift from nice-to-have into need-to-have for their data and analytics strategy. And with any luck, quickening the pace of data access and analytics insight will improve how companies compete as well as how we deal with uncertainties like pandemics and recessions more quickly.

If we have learned anything from the past three years it is to not make projections, but rather ground deeper into our values to meet the opportunities we unveil. As we look forward into 2023 and beyond, we see data and imagine all the untold narratives just waiting to be revealed. Somewhere between harnessing the data and reaping the benefits of better decision making are the people key to translating analytics into action.

So, count us in because we are ready to play on. Oh, and I meant to ask:

What’s your story?

Eating the elephant, one iteration at a time

Estimating Value at Risk for an IFRS17 Insurance Portfolio via Monte Carlo Simulation using SAS Viya

Supporting IFRS17 portfolio cash flow modeling and simulation

Corios has been busy lately supporting our client’s actuaries who are implementing the IFRS17 standard on their set of insurance portfolios. The purpose for this engagement is to better estimate the Value at Risk (VaR) on their portfolios’ liability for remaining coverage (LRC) and liability for incurred claims (LIC). LIC in turn includes both the liability for claims reported but not fully reserved, and for those claims incurred but not yet reported. The approach we and our client are following uses a cash flow projection analysis that is conceptually similar to the way our banking clients model future cash flows for secured and unsecured lending portfolios.

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Five D’s of Analytic Model Deployment

Moving your models from the lab to the field for business impact

The Challenges: Business Adoption of Analytic Models

In order to increase business adoption of analytic models, there is a great deal of work that must occur in addition to model development, and it extends well beyond the model development team.

  • First, businesses need to establish connections between model scores and business decisions. This connection usually takes place outside the analytics team building the model.
  • Second, the data structures and systems used by model developers for building models are often different from the one that will be used for implementing in production. Adaptation of the model asset into production should incorporate these differences.
  • Third, businesses must be able to easily interpret, assess, and catalogue the model scores and the changes in scores over time on an ongoing basis.
  • Fourth, to deploy and execute these models in a production information technology environment and in the field requires diligence, planning, design, execution, and quality assurance practices that are not commonly adopted by model developers.

The Five Ds of Model Deployment

The purpose of this chapter is to provide a set of best practices for analytic model deployment, organized into five phases that we’ve nicknamed the “Five Ds.” They are,

  1. Develop: Developing and packaging models
  2. Decisions: Tying operational business decisions to model scores
  3. Data: Operationalizing analytic model deployment in a specific data architecture
  4. Delta: Monitoring the workflow and numeric performance of analytic models in the field
  5. Deploy: Implementing analytic models via a software development life cycle

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Model governance checks: Stability, Peformance and Calibration

Benchmarks for CCAR and IFRS17 practitioners

Some enterprises build a formal model governance practice in order to comply with industry standards such as CCAR (banking industry) or IFRS17 (insurance industry). Others know that building a sound predictive model governance discipline is a great idea to improve the quality of your business decisions. Here are some well-tested practices for ensuring three pillars of model governance: Stability, Performance and Calibration.

  1. Stability: Can I rely on the process that generates my enterprise data as stable and believeable?
  2. Performance: Can I predict the difference between good and bad outcomes, or between high and low losses?
  3. Calibration: Can I make those predictions accurately?

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Test and learn: a virtuous cycle

Continuous evaluation and improvement of models

Turning your predictive analytics practices into a thriving model factory requires closing the loop from model creation, to performance monitoring, to problem and opportunity identification and subsequent model improvement, in a formal and disciplined way. This includes continuous measurement of each model, and creating a virtuous cycle of model improvement through portfolio management of your models.

Continuous measurement

Continuous measurement of model performance is about drawing insights from model performance, and identifying opportunities for model enhancement. You can find these opportunities by stratifying the customer population to isolate pools of customers who behave differently with respect to the predicted outcome; tuning the model for specific pools of risks that contribute to the predictive outcome in different ways; and adding new predictive drivers to the model to help address customer behavior not being explained effectively by the current model. Read More