Original article written by Dr. Jürgen H. M. van Grinsven & Ir. Henk de Vries.

Dr. Jürgen H. M. van Grinsven is the author of

Improving operational risk management,  more info here

INTRODUCTION

Over the past years, various Group Support Systems (GSS) have been used to support Operational Risk Management (ORM) [1]. ORM supports decision-makers to make informed decisions based on a systematic assessment of operational risks [2] [3].

Financial Institutions (FI) often use loss data and expert judgment to estimate their exposure to operational risk [4]. Utilizing expert judgment is usually completed with more than one expert individually, often referred to as individual and self-assessments, or group-wise with more than one expert, often referred
to as group-facilitated self-assessments [5].

While individual self-assessments are currently the leading practice, the trend is more towards group-facilitated self-assessments. There is a need to support these group-facilitated assessments using Group Support Systems. In this article we discuss how Group Support Systems (GSS) can be used to support expert judgment activities to improve ORM. First we describe how a GSS can support multiple expert judgment activities. Secondly a case study will be presented describing the application of a specific GSS, GroupSystems to ORM.

BACKGROUND

Expert judgment is defined as the degree of belief a risk occurs, based on knowledge and experience that an expert makes in responding to certain questions about a subject [6] [7]. Expert judgment is increasingly advocated in FI’s for identifying and estimating the level of uncertainty about Operational Risk (OR) [5]. Moreover, expert judgment can be used to incorporateforward-looking activities in ORM.

Group Support Systems (GSS) can be used for the combined purposes of process improvement and knowledge sharing [8]. GSS can be seen as an electronic technology that supports a common collection of tasks in ORM such as idea generation, organization and communication. GSS aim to improve (collaborative)
group work [9] [10]. Effectiveness and efficiency gains can be achieved by applying GSS to structure multiple experts’ exchange of ideas, opinions and preferences. There are several GSS tools available to support multiple experts in collaborative group work [11]. Examples are: GroupSystems, Facilitate, WebIQ, Meetingworks and Grouputer. Grinsven [5] presents an overview of GSS tools, based on GroupSystems, that can be used for supporting experts in the ORM phases, see Table 1 for examples.

Table 1: Examples of GSS tools [5]

GSS SUPPORTING ORM: A CASE STUDY

The activities of the preparation phase can be divided in the sub activities:

• determine the context and objectives
• identifying, selecting and assess the experts
• choosing the method and tools
• tryout the ORM exercise
• train the experts [12] [5].

The activities of the risk identification phase can be divided in identifying the OR, categorize the OR and perform a gap analysis. One of the objectives of this phase is to arrive at a comprehensive and reliable identification of the OR to reduce the likelihood that an unidentified operational risk becomes a potential threat to the FI. Member status, internal politics, fear of reprisal and groupthink can make the outcome of the risk identification less reliable [5]. Electronic Brainstorming was used to identify the events. Then, the Categorizer was used to define the most important OR. For this, we used a group-facilitated workshop. Using Electronic Brainstorming combined with a Vote tool we supported the experts to perform a gap analysis. In the case study, the experts appreciated the possibility to identify OR events anonymously.

The activities of the risk assessment phase can be divided in the following sub activities:

• assess the OR and
• aggregating the results.

Grinsven [5] advises to ensure the experts assess the OR individually, to minimize inconsistency and bias, also see e.g. [6]. We used the Alternative Analysis tool from Group Systems to enable the experts to assess the OR individually and anonymously. Then, using the Multi Criteria tool, we calculated the results by aggregating the individual expert assessments. The standard deviation function helped us to structure the interactions between the experts. In this interaction the experts provided the rationales behind their assessments. We learned that the GSS tools helped us to prevent the results being influenced by groupthink and the fear of reprisal.

The activities of the risk mitigation phase can be divided in three sub activities: identify alternative control measures, re-assess the residual operational risk and aggregate the results. The methods and tools that can be used in this phase are almost similar to the risk assessment phase. However, a slightly more structured method was used to identify alternative control measures. At the Dutch FI we used the Topic Commenter tool to support this activity. Experts were enabled to elaborate / improve the existing control measures and provided examples for each of them. Then, we used the Alternative Analysis tool to anonymously
re-assess the frequency and impact of the OR. After this, we calculated and aggregated the results using the standard deviation function combined with a group facilitated session.

The activities of the reporting phase can be divided in the sub activities: documenting the results and providing feedback to the experts. Documenting the results needs to follow regulatory reporting standards. This was done by a person of the Dutch FI, using the output results from the GSS as an input for the report.

The intermediate results were presented to the experts immediately after the ORM sessions. Following Grinsven [5], a highly structured process was used to present these results thereby enabling the experts to leverage the experiences gained and to maintain business continuity. We facilitated a manual workshop to provide feedback to the experts. Moreover, at the Dutch FI we made sure the final report complied to the relevant regulatory reporting standards. Future research should investigate applying GSS to provide structured feedbacks.

CONCLUSIONS

Expert judgment is extremely important for ORM when loss data does not provide a sufficient, robust, satisfactory identification and estimation of the FI’s exposure to OR. The case study indicates that a GSS can be used to support expert judgment in every phase of the ORM process. GroupSystems can be used in each ORM phase to support experts in order to achieve more effective, efficient and satisfying results.

GSS can be used to help gathering and processing information about operational risk. Moreover, GSS has the potential to improve the ORM process by minimizing inconsistency and biases, reducing groupthink and gather and processing information. However,more research need to be done to find out which GSS packages besides GroupSystems are suitable to support the ORM process.

FUTURE RESEARCH

The case study indicates that GroupSystems can be used to support multiple experts in the ORM process. However, our case study indicates that research should be done in the possibility of applying GSS in the reporting phase to provide structured feedbacks. Furthermore, concerning the other GSS tools such as Facilitate, WebIQ, Meetingworks and Grouputer, it is yet unknown whether these packages can be used to support expert judgment in ORM. Therefore further research should be done to investigate the possibility of using these GSS packages to support ORM. This would also make comparisons possible.

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