Oreum Industries is a data science consultancy from Jonathan Sedar in collaboration with a worldwide network of expert developers and leaders. We focus on the insurance industry, helping underwriters, insurtechs and corporates to learn from data. We operate remotely & globally, with a client base spanning USA, UK, Europe and the Middle East.

Our technical bread and butter is advanced Bayesian statistical modeling, to create an edge in pricing & reserving throughout general & life insurance. This is essential to quantify uncertainty and support underwriter-led real-world decision-making in low data environments.

As opportunity allows, we host a handful of technical resources here. In each case at least a Technical Overview is public, and in some cases we've made the code available to read, or even fully open-source. Naturally we can't open everything, so where code is closed, please contact info@oreum.io for commercial interest - we'd be more than happy to discuss in detail.

These are the result of a concise exploration and implementation of specific tools & techniques relevant to our modeling work. They often contain novel model architectures, describe theory and practice at a deep level, and use Bayesian inference and a Bayesian workflow, specifically using the pymc & arviz ecosystem.

• Oreum Copula Copula Regression Modeling. This provides an extremely valuable method to estimate Expected Loss Costs when Claims Frequency & Severity are correlated in low data environments. Demonstrated 32 percentage-point improvement in accuracy vs non-copula model. Publicly Readable Code, Commercial License

• Oreum Survival Survival Regression Modeling. This provides an extremely valuable suite of model architectures to estimate time-to-event via right-censored Accelerated Failure Time (AFT) and semi-parametric (CoxPH) models. Publicly Readable Code, Commercial License

• PYMC Examples: GLM-ordinal-features Full Bayesian workflow example on how to handle ordinal data in exogenous (predictor) covariates in pymc. Public Code, Open Source License

• PYMC Examples: GLM-missing-values-in-covariates Full Bayesian workflow example on how to handle and impute missing data in exogenous (predictor) covariates in pymc. Public Code, Open Source License

These are the result of a deep-dive investigation into a particular dataset, either to learn about that data or to fully demonstrate a suite of methods to tackle a new challenge.

• Oreum Case Study: Lung Survival regression of a lung cancer mortality study with conventional full-period observations. Uses a novel Accelerated Failure Time architecture that handles right-censoring, missing data, ordinal covariates and a linear regression submodel. Public Overview Slides, Commercial License

• Oreum Case Study: ONS Survival regression of a multi-year England & Wales ONS aggregated deaths-only dataset. This is a very compromised dataset but interesting to tackle, and requires a highly novel modified AFT architecture to allow for right-truncation (not right-censoring). Public Overview Slides, Commercial License

• Oreum Wholelife Simulator Simulate the cash value of WholeLife Insurance Policies using a parameterised survival curve including financial activities (investments, drawdowns). Publicly Readable Code, Commercial License

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