Compliance Analytics Gaps: How to Fix Bad History with Novel Data Bounding

Close Gaps in Compliance Analytics with Data Bounding
by Austin Barber on November 30, 2022

It feels like with each passing year the stakes rise for predicting and managing risk. Does the universe need to keep dishing us market, environmental and health crises when regulated industries are amid massive digital transformation and analytic platform modernizations?

The insurance industry is facing down the compliance deadline for the FASB’s Long Duration Targeted Improvements (LDTI). Public carriers should be on the cusp of final transition to running new models developed over the past two years.

Similarly, most financial institutions should have their Credit Expected Credit Loss (CECL) compliance transitions in the rearview mirror. However, many credit unions have yet to reach key milestones toward implementation of the new credit standard.

And for private and smaller entities just now converting new roadmaps and processes into trial implementations, what are the lessons learned from their larger counterparts to help smooth the process?

Timing, business considerations and the scope of modernization may vary among companies impacted by new regulatory standards, but there is one unavoidable similarity: all will surface analytics gaps. Deadlines can’t be missed, but issues must be addressed. All that’s left is determining how and when.

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Count Us In on More Data Analytics

Corios transitions from pandemic to post-recession data analytics storytelling
by Robin Way on November 15, 2022

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.

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Eating the elephant, one iteration at a time

Estimating Value at Risk for an IFRS17 Insurance Portfolio via Monte Carlo Simulation using SAS Viya
by Robin Way on September 9, 2021

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
by Robin Way on September 5, 2021

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
by Robin Way on September 4, 2021

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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