White papers

White Paper in Quantitative Finance

Posted on 17-08-2018

Panorama of stochastic models for Real World simulations in Finance

by Dr. Sébastien de Valeriola, François Ducuroir and Wim Konings

In the recent years the need for scenarios of financial Risk Drivers simulated under Real World (“RW”) probability measures has increased, driven, among others, by new regulations (e.g. Solvency II), materially changing market conditions (triggering the need for refined Strategic Asset Allocation studies), the development of new financial products, the introduction of new actuarial techniques, etc.

In this paper, we propose an overview of the usual methodological approaches which we consider during our consulting missions when faced with modeling requirements. This paper is obviously not exhaustive and covers, high-level, 11 typical features modelers may have to include in their RW simulation engines.

We have aimed at using a language as close to plain English as possible and to illustrate our choices with practical examples and intuitive interpretations so that this paper also addresses questions from our readers less familiar with mathematical finance.

White Paper on Digital Transformation and Artificial Intelligence

Posted on 09-07-2018

Reacfin’s Strategic Digitalization Quick-Scan

With practical examples of transformation projects driven by AI and Data Science

by Francois Ducuroir, Vidushi Gupta and Aurélien Couloumy

With its 15 years’ experience in consulting for Financial Institutions and its high-end technical knowledge of algorithms and digitalization techniques, Reacfin has developed and applied a hands-on methodology to help Financial Institutions define priorities and action plans for their digital transformation in data science, artificial intelligence and robotization. Our approach focusses on capturing “Fast-Mover” advantages by leveraging on our clients strengths and competitive positioning.

In this white paper, written for general managers rather than for AI specialists, we present an overview of our approach that has 3 unique features:

  • It can be applied at company level or at specific business level only, taking into consideration a broader group strategy for digitalization.
  • It identifies pragmatic actions to be undertaken in the short term and defines a clear schedule of practical deliverables over time.
  • It defines such ‘hands-on’ digitalization strategy very rapidly and in co-development with your own teams (i.e. delivering a strategic vision and implementation roadmap that is truly owned by your staff).

We have structured our “Quick Scan” methodology so that it uses concepts which traditional financial services managers are familiar with and such that it will contribute, through rapid measurable results, to a true buy-in within the entire organization or department under scope.

White Paper on Financial Planning and Wealth Management

Posted on 03-05-2018

CASE STUDY #1: Preparing a financial diagnostic for a typical household

A Practical applications using ReacfinFinancialPlanner.com

by Wim Konings, Charlotte Thomas and François Ducuroir

In this paper we present a practical application of the Reacfin Financial Planner tool which is freely available online.We consider the case of a Belgian family of 4 people (2 adults and 2 children). Both adults have comfortable income but proportionate expenditures. As they meet their Financial Advisor for the first time, they have so far placed all of their savings in cash on a standard deposit account.

We see how using Reacfin’s Financial Planner tool, the Financial Advisor can show its clients that, applying their current saving strategy they could run the risk of having to sell their house in their old days to sustain their standard of living.

We also illustrate how our tool can be used to show that an alternative investment strategy (which would include Corporate Bonds and Equities) could help limit that risk. The tool shows how risks will increase over the short term but will be compensated over longer term by the higher expected returns of the investments.

White Paper: Competition price analysis in non-life insurance

Posted on 06-11-2017

Competition price analysis in non-life insurance

How machine learning and statistical predictive models can help!

by Annick Biver, Michaël Lecuivre and Xavier Maréchal

The competition on the non-life insurance market is as fierce as ever. This situation leads many insurers to develop more and more sophisticated pricing structures trying to implement an efficient segmentation to reach expected profitability. This makes the comparison of an insurer’s prices with its competitors’ prices more difficult and less transparent. Relevant price analysis techniques must therefore be implemented to clearly identify an insurer’s positioning on the market and take the adequate decisions in order to improve its profitability.

In order to respond to such needs, Reacfin’s team developed some methodologies and a tool that enable the user to perform many comparative analyses with the aim to assess and benchmark the prices of other market participants.  Machine learning and statistical predictive modelling combined with data visualization techniques allow to build practical indicators and help in taking decisions regarding an insurer’s positioning and strategy.

White Paper: Machine Learning applications to non-life pricing

Posted on 20-09-2017

Machine Learning applications to non-life pricing

Frequency modelling: An educational case study

by Julien Antunes Mendes, Sébastien de Valeriola, Samuel Mahy and Xavier Maréchal

Machine Learning techniques operate data to perform classification and prediction. They are becoming more and more popular, due (among other things) to the still increasing computational power of machines. These algorithms use a very limited set of assumptions, compared to “standard” statistical methods. However, on the contrary of such methods, inference is not the main goal of Machine Learning techniques: they focus on classification and prediction. This is the reason why these “innovative” techniques are considered as more “black box” solutions.

This paper presents a comparison on a simulated database between traditional statistical predictive modelling techniques (GLM and GAM), machine learning techniques (regression trees, bagging, random forests, boosting and neural networks) and penalized regression techniques (Lasso, Ridge and Elastic Net).

Mortality projections for Belgium, general population over 1950–2015, with corrections for adverse selection

Posted on 27-05-2017

Lee-Carter modelling and adaptation to insurance mortality using Reacfin’s tools Thanatos and Hygie

by Elena Atienza Y Rubio, Michel Denuit, Julien Trufin et Julien Antunes Mendes

In most industrialized countries, including Belgium, mortality at adult and old ages reveal decreasing annual death probabilities. The calculation of expected present values (for pricing or reserving) for long-term survival insurance benefits thus requires an appropriate mortality projection in order to avoid underestimation of future costs. The so-called

projected life tables include a forecast of the future trends of mortality. This paper updates previous projections based on recent mortality statistics, for the general population and for the insurance market. A discussion of the strategies to deal with the systematic longevity risk concludes the study.

A Reacfin White Paper on Worker’s Compensation Insurance

Posted on 05-02-2017

A Unified Approach for the Modeling of Rating Factors in Workers’ Compensation Insurance

by Ben Stassen, Michel Denuit, Samuel Mahy, Xavier Maréchal and Julien Trufin

Workers’ Compensation is an insurance cover whereby an employer compensates for the lost wages and medical expenses of an employee who is injured on the job. As with other types of insurance, premium rates for workers' compensation policies are calculated according to several different factors. In Belgium, current market practice consists of pricing workers’ compensation based on a hierarchical credibility model, i.e. the Jewell model. Hierarchical credibility models provide a nice hierarchical procedure in the calculation of premiums. First, we produce an expected aggregate claim amount for the whole line of business. Then, we distribute this amount over lower levels (top down approach).

In this paper, we define a new tariff structure for workers’ compensation by combining hierarchical credibility models with a GLMM approach in the final pure premium model. We start with the calculation of a basic pure premium rate in function of some explanatory variables by using a Generalized Linear Mixed Model (which is an extension of the well-known Generalized Linear Models). The advantage of such a model is that we can now fit ordinary rating factors and hierarchical rating factors at the same time. Afterwards, this basic premium rate is adapted according to the client’s history of claims data with the use the Bühlmann-Straub credibility model.

Research in Actuarial Science: Compositions of Conditional Risk Measures and Solvency Capital

Posted on 23-01-2017

Compositions of Conditional Risk Measures and Solvency Capital 

Reacfin’s consultant, Dr. Adrien Lebègue, together with our chairman, Pr. Pierre de Volder, have just published a scientific article that proposes routes considering time-consistent risk measures to determine the necessary capital of pension funds or insurance companies.

In this paper, the authors consider compositions of conditional risk measures in order to obtain time-consistent dynamic risk measures and determine the solvency capital of a life insurer selling pension liabilities or a pension fund with a single cash-flow at maturity. Pr. Devolder and Dr. Lebègue first recall the notion of conditional, dynamic and time-consistent risk measures. They link the latter with its iterated property, which gives a way to construct time-consistent dynamic risk measures from a backward iteration scheme with the composition of conditional risk measures. They then consider particular cases with the conditional version of the value at risk, tail value at risk and conditional expectation measures. Finally, the authors give an application of these measures with the determination of the solvency capital of a pension liability, which offers a fixed guaranteed rate without any intermediate cash-flow.

This paper entitled “Compositions of Conditional Risk Measures and Solvency Capital” is currently available on the site of the Multidisciplinary Digital Publishing Institute here.

Research in Actuarial Science: Minimum Protection in DC Funding Pension Plans and Margrabe Options

Posted on 19-01-2017

New Scientific Paper: Minimum Protection in DC Funding Pension Plans and Margrabe Options

In the context of their academic research, our chairman, Professor Pierre Devolder, together with Reacfin’s senior consultant Dr. Sébastien de Valeriola, recently published a scientific paper regarding the valuation of pension plan features.

In Belgium a recent law change allows employers to choose between two different types of guarantees to offer to their affiliates. In this paper, the authors address the question arising naturally: which of the two guarantees is the best one? In order to answer that question, they set up a stochastic model and use financial pricing tools to compare the methods. More specifically, they link the pension liabilities to a portfolio of financial assets and compute the price of exchange options through the Margrabe formula.

This paper entitled “Minimum Protection in DC Funding Pension Plans and Margrabe Options” is currently available on the site of the Multidisciplinary Digital Publishing Institute here

AML: Market practices and upcoming risk solutions

Posted on 31-10-2016

Risk-Based Approach & examples of Machine Learning Application

By François Ducuroir and Maciej Sterzynski

On 27 and 28th of October 2016, the Union of Arab Banks held, in Beirut, its annual forum for heads of AML/CFT* compliance units.
At this occasion, Reacfin’s managing partners, François Ducuroir and Maciej Sterzynski, presented our experience with regard to current market practices and upcoming solutions in this matter.

Among other we discussed:

  • practical aspects of idesigning and deploying Risk-Based solutions for Anti-Money Laundering purposes, and
  • hands-on examples for using Machine Learning algorithms (i.e. Artificial Intelligence techniques) to identify relevant variables for an efficient identification of supiscious Money Laundering or Terrorism Financing behaviors.

Interested readers will find here the slides of this presentation:

Reacfin White Paper - Prospective Life Tables

Posted on 17-09-2016

An introduction to time‐dependent mortality models

by Julien Antunes Mendes and Christophe Pochet


Life expectancy at birth among early humans was likely to be about 20 to 30 years. It rose to between 40 and 45 by the middle of the 19th century. Rapid improvements began at the end of the 19th century, so that by the middle of the 20th century it was approximately 60 to 65 years. At the beginning of the 21st century, life expectancy at birth reached about 70 years. Two trends dominated the mortality decline:

The first half of the 20th century saw significant improvements in the mortality of infants and children (and their mothers).

Since the middle of the 20th century, gains in life expectancy have been mainly due to medical factors that have reduced mortality among older persons (reductions in deaths due to the “big three” killers – cardiovascular disease, cancer and strokes).

More in the pdf document.

A reacfin White Paper on Mortality

Posted on 17-09-2016

Adverse selection in lifetables models, Methods & applications

By Aurélie Miller and Julien Antunes Mendes

In many different applications (Solvency Capital Requirement, Best Estimate of Liabilities computation, Economic Balance Sheet statement, insurance products pricing, profit analysis…), insurance companies use lifetables to model the probability of death or survival.

Adverse selection is the fact that, depending on the features of the insurance products underwritten, policyholders could present different behavioral or health characteristics. This could lead to observe different death probabilities between policyholders of the same age among the different products. For Valuation and Risk Management purposes, as this difference could be large, it should therefore be accounted for.

The lifetables will thus be different for some segments of insurance policyholders and will be different from the general population mortality (of a country for example). By using adapted life tables, the insurance company can take into account the adverse selection.

Calibration of such tables will be based on a trade-off between appropriateness (specific features of the policyholders adequately captured) and accuracy (sufficient volume of data to obtain reliable parameters).

More in the pdf document.

A Reacfin White Paper on Life Insurance

Posted on 07-07-2016

Lapse rate models in life insurance and a practical method to foresee Interest Rates dependencies

Lapse rate modelling is an important topic for life insurers. Changes in such lapse rate can potentially lead to material losses or to liquidity problems. Yet lapses prove difficult to model because they can be influenced by large number of parameters including the policyholder’s behavioral characteristics, the product’s specificities or the financial markets and macro-economic environment.

Specifically the modeling of interest rate dependency proves both critical (as lapses could be driven by increasing interest rates) and difficult to calibrate (historical data offer for instance limited information as rates decreased almost continuously over the recent decades). In this white paper, our consultants Julie Zians, Aurélie Miller and François Ducuroir review market practices in the Belgian market with regard to the modeling of lapses. They further propose a pragmatic way to model and calibrate the interest rate dependency of lapse rates.

A Reacfin White Paper on Artificial Intelligence applications to Finance

Posted on 22-06-2016

Introduction to Machine Learning techniques used in the financial industry and a practical case study

By Arnaud Deltour and François Ducuroir

Machine Learning (“ML”) techniques, a sub-field of Artificial Intelligence, become increasingly popular within the financial industry to tackle issues involving large amounts of data.

In this paper we aim at providing the reader with a basic introduction to key ML concepts and techniques, explain how such approaches differ from the more traditional statistical analysis approach and illustrate this theoretical presentation with some simple yet very practical application within the financial industry.

We also illustrate our point with a case-study: a practical application of decision trees to build predictive LGD models in loan books management.
We wrap-up this paper with a high-level comparison between traditional statistical inference methods and ML techniques.


Valuing Belgian EB insurance reform proposition

Posted on 05-01-2016

A Reacfin White Paper in Complementary Pensions

Assessing the economic value of the Belgian Employee Benefits insurances under the recent reform for private complementary pensions (under insurance form): An approach based on financial structuring techniques.

In Belgium, national social negotiators recently agreed to reform the Minimum Guaranteed Rate (MGR) prevailing for Employee Benefits (“EB”) insurance contracts.

In this article we use structuring techniques typically used by investment banks to perform a high-level impact assessment of such proposal. We solely focus on contracts of the type “Branch 21” (i.e. Belgian Life policies with minimum rates guaranteed by the insurers themselves and profit sharing features).

We aim at didactically explaining the values of the different building blocks of the product by not only providing a step-by-step decomposition approach but also relating our findings with the current and historically observed market conditions.


Reacfin White Paper in Quantitative Finance

Posted on 25-06-2015

Modeling negative interest rates with Free Boundaries SABR Approaches for model assessment and validation

by Dr. Sebastien de Valeriola, Wim Konings and François Ducuroir

Confronted with the current low-rates environment, many fixed income derivatives market professionals (investment banks, market makers, hedge funds, etc.) are reviewing possible solutions to adapt their derivatives pricers. It is particularly the case of those models assuming stochastic volatilities under SABR processes. A recent article published by the quant teams of Numerix  proposes innovative solutions to go beyond the usual trivial approaches or “rates shifting” solutions.

In this white paper we summarize our understanding of the article and propose an initial review of the main points of attention one should consider in the context of model assessment and its validation.

Reacfin White Paper on Retail Banking

Posted on 07-04-2015

Retail deposit modelling - Application to Belgian Saving accounts


by Dr. Sébastien de Valeriola and Jean-François Raman

Savings deposits play a major role in today’s economy. They are one of the most important sources of funding for European banks. For this reason, banks want to assess the stability and monitor the evolution of their deposits.


In this paper, we present a multivariate model for the savings deposit volume. It allows assessing elasticities to savings products and forecasting the deposit volume over specified time horizons. We consider here the case of the regulated Belgian market.

Deposits replicating portfolios approach: a Case Study

Posted on 18-06-2014

a Reacfin White Paper on Retail Banking  

by Wim Konings and François Ducuroir

Non-maturing liabilities (such as saving accounts and current accounts) are a major source of funding for many retail banks and a much envied source of liquidity for their Commercial- or Investment-banking peers. Yet characterizing, quantifying and ultimately hedging the interest rate risks of such instruments has, for long, proven quite challenging.

In this paper we illustrate how a replicating portfolio approach can offer a pragmatic solution to address this point. In addition a practical case study is presented based on the average base rate on Belgian regulated saving accounts. The results suggest that historically some banks making over-simplistic assumptions may have ended-up not only making materially less margins but also have increased the volatility of such margin and hence their related interest-rate risks.

New White Paper: Deferred Taxes under Solvency II

Posted on 05-06-2014

by Aurélie Miller and Vincent Thibaut 

With the Omnibus II agreement finally reached, the entry in force of Solvency II is now set for the first of January 2016. Nevertheless, much uncertainty remains as to many practical aspects impacting more or less significantly the solvency position of (re)insurers. 

One of the most critical and material items that requires further definition or guidance, is definitely deferred taxes. Therefore, for the time being, it appears essential for any (re)insurance undertaker to have a clear view and understanding of the texts in force. 

This paper intends to provide that insight, and offers a phased methodology in order to value taxes consistently in the Solvency II economic balance sheet.


Deferred taxes arise because there are differences between the value ascribed to an asset or a liability for tax purposes, and its value in accordance to the Solvency II principles. 

In Belgium, the tax is calculated on the net taxable profit, which is determined with Belgian accounting principles (valuation at historical cost) and specific local fiscal rules. Those valuation principles are very different from Solvency II valuation principles. Two Belgian fiscal rules will have their importance when valuating deferred taxes: 

  • There is (almost) no tax on realized gains on equities (under some conditions) 
  • There is no time limit for the recoverability of loss carry-forward

More in the pdf document

Our multifunctional advanced Economic Scenario Generator (ESG) framework

Posted on 10-11-2013