Trends in Banking 2017 and onwards

The changing nature of the relationship between a retail bank and its customers is examined, particularly with respect to new financial concepts, debt and regulation. The traditional image of a bank is portrayed as a physical building a classical Doric portico. This image conveys concepts of service, soundness, strength, stability and security ("five-S"). That "five-S" concept is changing, and the evidence for changes that affect customers directly is considered. A fundamental legal problem associated with those changes is highlighted: a bank is no longer solely responsible for the safeguard of customer monies. A solution to this problem is proposed: banks should be jointly liable with perpetrators of criminal activity in the event of frauds as an encouragement to recognise and mitigate fraud.Comment: Proceedings 29th SASE Conference, Lyon France, June-July 201

Correlations in Operational Risk Stress testing: use and abuse

Correlations between operational risk loss severity, frequency and economic factors have been used as a de facto tool to assess economic and regulatory capital since 1990. We demonstrate, using data from a single retail bank, that such correlations do not apply universally, and that projections of capital requirements are subject to wide error margins. Some correlations can be explained in terms of data trends. Given worldwide regulatory requirements to assess the resilience of financial institutions to economic shocks, an alternative to using correlations that makes use of economic data is proposed. The proposal is consistent with a much broader interpretation of capital allocation than has applied to date. Evidence that the Covid-19 pandemic had minimal effect on operational risk losses in 2020 is presented and the effect of model risk is emphasized. Our results show that the existence or otherwise of significant correlations depends on the regression model used, whether data series show trends, the time window concerned, geographical location and the type of financial institution

A complexity framework for consensus and conflict

Consensus and conflict are modelled in the context of interacting pairs of agents who may have very diverging sentiments regarding some particular issue. Simulations using the model display characteristics of complexity. Agents are modelled using Beta probability density functions whose parameters determine the agent's opinion and resistance to change after an interaction, and a third independent parameter that determines the agent's influence. Interactions among groups of agents with both aligned and opposing sentiments are simulated. The results indicate that in most cases a form of consensus is reached eventually, but for opposed agents, it is not possible to tell which agents that consensus will favour. Proofs of convergence are given in the cases where the initial state is one of consensus, and when it is one of conflict

Is a Reputation Time Series White Noise?

The plots of some reputation time series superficially resemble plots of white noise. This raises the question of whether or not the analysis of sentiment to produce a reputation index actually generates nothing more than noise. The question is answered by using the Box-Ljung statistical test to establish that the reputation time series considered in this analysis cannot be viewed as white noise. This result is supported by applying a new test based on cross-correlations of reputation time series with white noise time series

Reputation auto-correlation: Implications for simulation

Previous work has established that the distribution of daily reputation scores is best modelled by a bi-partite pair of exponential distributions. Simulations developed from that distributional model did not account for autocorrelations in the data. We now extend the bi-partite model in two ways. Candidate auto-correlation methods are assessed in order to incorporate the auto-correlation structure of the data in a simulation. Negative reputational shocks are then modelled using a chi-square distribution, so that they can then adequately model runs of successive days of either positive or negative sentiment. Auto-correlation goodness-of-fit tests show that the optimal auto-correlation model uses the fitted auto-regression components of the original data, and that goodness-of-fit can be improved by inflating them by about 1%. This optimised model is successful in at least 88% of simulations where auto-correlation in the original data does not extend beyond 10 lags. In other cases (mainly due to severe reputational shock), 80% success can be expected. Examples of shock simulations for large corporate organisations are shown, and the implications for reputational analysis are discussed

Reputation risk contagion

The effects of the reputation of any single member of a group of agents on all the others in the group are calculated by modeling the spread of reputation contagion in a DeGroot network. The reputation of individual agents is measured by compiling a reputation index for each agent over an extended period. Transition probabilities within the network are assessed by considering extreme reputational events using a Bayesian approach. The results indicate that consensus is reached quickly, and influential agents can be easily identified. Agents in the network with a very positive reputation serve to mitigate the negative reputation of other agents in the network. Approximately 10–15% of the reputation of any agent in the network is attributable to network effects; positive reputations are deflated and negative reputations are inflated. The network effect on the sales of any single agent can be estimated once the reputation score has been translated to sale

Reputation risk: measured

Two principal results for reputation risk are established. First, reputation risk can be measured in terms of a single index, arising from a data mining process directed at the opinions in a complex multi-agent network. Second, the results of the measurement process, gathered over an extended period, can be expressed directly in monetary terms by finding a correlation between the daily changes in the index and in sales. Stressed periods are modelled by calculating value-at-risk using a ‘loss-distribution/ scenario’ approach, as for operational risk capital. The short-term effect of reputation risk events on sales and profits can be significant in absolute terms, but is small as a percentage of total sales. Negative reputation has a more significant impact than positive reputatio

Computer algebra techniques in object-oriented mathematical modelling.

This thesis proposes a rigorous object-oriented methodology, supported by computer algebra software, to generate and relate features in a mathematical model. Evidence shows that there is little heuristic or theoretical research into this problem from any of the three principal modelling methodologies: 'case study’, ‘scenario’ and ‘generic’. This thesis comprises two other major strands: applications of computer algebra software and the efficacy of symbolic computation in teaching and learning. Developing the principal algorithms in computer algebra has sometimes been done at the expense of ease of use. Developers have also not concentrated on integrating an algebra engine into other software. A thorough review of quantitative studies in teaching and learning mathematics highlights a serious difficulty in measuring the effect of using computer algebra. This arises because of the disparate nature of learning with and without a computer. This research tackles relationship formulation by casting the problem domain into object-oriented terms and building an appropriate class hierarchy. This capitalises on the fact that specific problems are instances of generic problems involving prototype physical objects. The computer algebra facilitates calculus operations and algebraic manipulation. In conjunction, I develop an object-oriented design methodology applicable to small-scale mathematical modelling. An object model modifies the generic modelling cycle. This allows relationships between features in the mathematical model to be generated automatically. The software is validated by quantifying the benefits of using the object-oriented techniques, and the results are statistically significant. The principal problem domain considered is Newtonian particle mechanics. Although the Newtonian axioms form a firm basis for a mathematical description of interactions between physical objects, applying them within a particular modelling context can cause problems. The goal is to produce an equation of motion. Applications to other contexts are also demonstrated. This research is significant because it formalises feature and equation-generation in a novel way. A new modelling methodology ensures that this crucial stage in the modelling cycle is given priority and automated

Shapley allocation, diversification and services in operational risk

In this paper, a method of allocating operational risk regulatory capital using a closed-form Shapley method, applicable to a large number of business units (BUs), is proposed. It is assumed that if BUs form coalitions, the value added to a coalition by a new entrant is a simple function of the value of that entrant. This function represents the diversification that can be achieved by combining operational risk losses. Two such functions are considered. The calculations account for a service that further reduces the capital payable by BUs. The results derived are applied to recent loss dat

A central limit theorem formulation for empirical bootstrap value-at-risk

ABSTRACT In this paper, the importance of the empirical bootstrap (EB) in assessing minimal operational risk capital is discussed, and an alternative way of estimating minimal operational risk capital using a central limit theorem (CLT) formulation is presented. The results compare favorably with risk capital obtained by fitting appropriate distributions to the same data. The CLT formulation is significant in validation because it provides an alternative approach to the calculation that is independent of both the empirical severity distribution and any dependent fitted distributio