Capgemini: How to leverage data for a granular view of operational risk

We are in a period of upheaval and never has so much pressure been exerted on the risk functions to have a more consolidated vision, nor so many challenges for the Chief Risk Officers.

With the constant emergence of new risks and threats, there is significant pressure to manage operational risks with a comprehensive strategy and an extended field of vision.. But on the other hand, there are huge benefits for organizations that can get an accurate, clear, and granular view into the complex complexities of their own unique instances of operational risk.

AI, data and the cloud enable organizations to have a consolidated 360 degree view of risk, providing greater transparency and digitization of processes coupled with a robust framework, can help organizations better identify risks . This will not only provide a clearer view of the risks, but also reduce the burden of risk assessment for humans.

Organizations’ operational risk frameworks often lack integration and are a collection of fragmented and disparate activities with a divergent variety of risks. But the risks interact in complex ways with great potential for overlap and cross-contamination. However, a much more comprehensive and holistic view is needed. Data and AI, when applied with the right expertise, enable more fluid risk identification, more systematic risk assessment, and multiple risk anticipation capabilities.

In financial services

Over the past few years, financial services (FS) as an industry has been rocked by sanctions related to client mismanagement and market misconduct. Add to that how increasing data permeation and new business models are revolutionizing the way banks serve their customers (a recent Capgemini Research Institute report found that only 34% of banks believe they have business model innovation they need), you have an environment in which operational risk capabilities can suffer.

But FS leaders in applying AI and ML are already seeing it pay off with measurable impact. Artificial intelligence and deep learning enable weak signal analysis of the banking ecosystem’s vast datasets so they are better able to detect anomalous behavior from rogue traders. Such fraudulent behavior represents a huge risk for banks. More than two decades ago, in an era before the advent of AI capabilities, rogue trader Nick Leeson caused the catastrophic collapse of Barings, London’s oldest merchant bank and banker in the Queen. AI, big data and data analytics provide banks with the means to track, predict and prevent this illicit trade.

In the making

While there is no industry where the appropriate harnessing of AI and data in operational risk would not bring significant benefits – being able to foresee or prevent hazards is beneficial for any organization. – but in manufacturing, the benefits are particularly easy to discern. There is no good time for downtime in the workshop. Thus, being able to detect new drivers and emerging risks to the supply chain, along with weak signal analysis on supply chain disruption, can bring much greater agility in the face of future headwinds.

A shot in the arm for risk assessment

The pandemic has caused enormous disruption and suffering globally, but valuable lessons have been learned about the risks and potential of data to anticipate outbreaks. The pandemic has shown that many organizations simply do not have the flexibility and risk views that are sufficiently granular, with considerable industry or geo-specificity. Better risk management systems could simply have helped many organizations respond faster and more effectively. Bluedot, for example, an outbreak intelligence platform, was able to identify the emerging risk of a COVID-19 outbreak a full eight days before the World Health Organization; prove that time and data matter. By analyzing public health sources and 10,000 mass media stories a day, and real-time flight routes, the data showed its ability to shorten risk assessment from days to minutes.

Our six demonstrators

At Capgemini Invent, we have developed 6 key demonstrators which, by leveraging AI, data and the cloud, help organizations get the more complete and granular view of the risks they need:

Dynamic RCSA (risk control self-assessment) makes it possible to adapt the risk assessment of a specific type to the precise nature of a given organization. It automatically generates an individualized set of risk and control libraries based on real-time auditing, thereby reducing manual and repetitive workload, in turn giving real-time RCSA results.

The RCSA Assessment Tool facilitates the RCSA assessment, simplifying it with a common methodology. It consolidates RCSA assessments to provide a holistic view of risk. Benefits include a standardized process and methodology for RCSA assessment with a greater degree of control facilitating the collection process at each assessment phase.

RCSA Consolidation/ Visualization brings together a consolidated view of risk across the organization, using advanced visualization and analytics technologies. It is a collaborative tool that supports business and operational teams in their decision-making thanks to a consolidated view by risk area. This facilitates the identification of priorities for improving the control framework and supervision, and collects the information needed for internal and external reporting.

Benchmark of internal/external risks provides powerful exploration of external incidents to provide insights for increased accuracy in risk assessment through appropriate benchmarks (both generic and specific). It provides support for the detection and identification of risks by analyzing relevant databases of historical incidents.

The Taxonomy Streamlining and Classification Tool Automatically categorizes operational risk incidents into an internal risk taxonomy based on incident descriptions. It helps risk managers with advanced exploration and visualization capabilities for better operational risk assessment. By automating the benchmarking of metrics and repetitive, time-consuming tasks, it frees up humans for more roles that add value and drive efficiency.

The weak signals analysis tool reveals an organization’s most recurring operational risks, relevant topics and related causes, based on incident descriptions. It provides a more powerful lens for the detection and precise definition of key risk indicators (KRIs). It enables faster identification of risk factors and emerging risks based on inputs provided by AI data mining and analysis.

In conclusion

Leveraging data and AI has many benefits to get a clearer and more detailed view of risk. Imagine knowing exactly how many mosquitoes entered your room and exactly where they were before you fell asleep! You would be able to locate and neutralize the threat and avoid significant inconvenience, discomfort and disruption. Data, AI and the cloud can provide organizations with such a granular view of risk and bring huge benefits if only applied in the right way.

If an organization can reduce and avoid risk, it can indeed become powerful, but laggards in optimally leveraging AI and data will remain open to those annoying stings in the dark. If you can see it, you can crush it.


Adam Meskini

Senior Manager, Risk Management Powered by Data, Capgemini Invent
Creation and scaling of value for Risk & Corporate functions