⚠️ Risk Register

18 identified risks across managerial, scientific, and technical categories

Risk Matrix

Low SeverityMedium Severity
High LikelihoodR7, R14R6
Medium LikelihoodR2, R3, R5R9, R15, R17, R18
Moderate LikelihoodR1
Low LikelihoodR4, R16R8, R10, R11, R12, R13

💼 Managerial Risks (5)

R1: Lack of cooperation amongst the consortium

Likelihood: Moderate • Severity: Low • WPs: WP8

Mitigation: Consortium has successfully collaborated for many years. Will increase cooperation through secondments and joint research activities. Numerous additional resources to facilitate collaboration.

R2: Dissemination of results is not as straightforward as initially envisioned

Likelihood: Medium • Severity: Low • WPs: WP7

Mitigation: Affiliated with leading European Networks (COST CA19130, ECMI, EIT Digital, European Consortium of Innovative Universities) as additional channels for dissemination.

R3: Partners are leaving the consortium

Likelihood: Medium • Severity: Low • WPs: WP4, WP7, WP8, WP5, WP2, WP1, WP6, WP3

Mitigation: All partners want to assume significantly more responsibilities. Initially 40 internationally renowned partners wanted to join and are still willing to participate.

R4: Doctoral candidates do not show sufficient progress

Likelihood: Low • Severity: Low • WPs: WP6

Mitigation: PMP set up at start. All DCs tightly integrated into doctoral schools at home universities, being part of a team of colleagues who will provide mutual support. Partners agreed to MSCA supervision guidelines.

R5: Resignation of a Doctoral Student

Likelihood: Medium • Severity: Low • WPs: WP6

Mitigation: Keep a shortlist of additional candidates. Consortium is involved with a significant number of doctoral schools who have agreed to provide additional PhD candidates.

🔬 Scientific Risks (8)

R6: A training course does not reach its aims/cannot be offered

Likelihood: High • Severity: Medium • WPs: WP7, WP6

Mitigation: Training courses are regularly evaluated. Entire consortium has offered to contribute twice as many courses. Courses may serve as substitutes by decision of SB. Ample resources for additional training.

R7: Scientific Misconduct

Likelihood: High • Severity: Low • WPs: WP4, WP5, WP2, WP1, WP3

Mitigation: All partners have appropriate Ethics rules at their institutions. If inappropriate behaviour is observed, the strictest measures will be taken.

R8: The research on data quality does not lead to the required output

Likelihood: Low • Severity: Medium • WPs: WP1

Mitigation: WP1 leader responsible for monitoring data quality. Industrial partners have already pre-agreed to provide all relevant datasets. Academic partners have subscribed to additional data sources. Alternative data sources will be searched.

R9: AI models are too complex to achieve significant user acceptance

Likelihood: Medium • Severity: Medium • WPs: WP2

Mitigation: Selection of research topics is primary risk mitigation strategy. Additional resources will be allocated to WP3 in order to support overall research objectives.

R10: Trade-off between explainability and model performance is too severe

Likelihood: Low • Severity: Medium • WPs: WP3

Mitigation: The research program compares extremely powerful outcome models in WP2 to a reasonably well-explainable and still-powerful model as fallback option while still advancing understanding.

R11: Interdependencies among cryptos

Likelihood: Low • Severity: Medium • WPs: WP4

Mitigation: Methodology for the risk index for cryptos is based on the established risk management tool FRM Financial Risk Meter.

R12: Non-disclosure of ESG risks and low standardisation and comparability

Likelihood: Low • Severity: Medium • WPs: WP5

Mitigation: Financial technology and advanced digital approaches offer solutions. Partners will play enabling role in creating conditions for a unified framework for defining ESG and improving risk assessment and mitigation practices.

R13: One of the deliverables/milestones cannot be reached

Likelihood: Low • Severity: Medium • WPs: WP4, WP5, WP2, WP1, WP3

Mitigation: Constant monitoring, deviations detected immediately. Refocus on deliverables/milestones at risk. Extensions granted as necessary after consulting with SB and EU officer.

⚙️ Technical Risks (5)

R14: Computational resources are not sufficient for complex models

Likelihood: High • Severity: Low • WPs: WP4, WP5, WP2, WP1, WP3

Mitigation: Internationally renowned research centres have dealt with more complex models. All partners agreed to provide significantly more resources if necessary. Focus on less resource-intensive models and adopt if needed.

R15: Equipment failure

Likelihood: Medium • Severity: Medium • WPs: WP4, WP5, WP2, WP1, WP3

Mitigation: All necessary hardware will have redundant infrastructure. Combined infrastructure of internationally renowned consortium far exceeds needs.

R16: Delay of hiring process

Likelihood: Low • Severity: Low • WPs: WP4, WP5, WP2, WP1, WP3

Mitigation: Progress closely monitored by SB from the very beginning. Monthly status reports on hiring process. If unsatisfactory after 9 months, EB will take appropriate actions including changing the beneficiary.

R17: Implementation issues for datasets

Likelihood: Medium • Severity: Medium • WPs: WP4, WP5, WP2, WP1, WP3

Mitigation: DIGITAL has extensively researched datasets, completed rigorous preprocessing and validation, conducted preliminary pilot tests, and continues to monitor and update to accommodate evolving data dynamics.

R18: Lack of useful datasets

Likelihood: Medium • Severity: Medium • WPs: WP4, WP5, WP2, WP1, WP3

Mitigation: Initial investigations reveal already available datasets are more than adequate. Each research WP required to provide overview of required datasets within first 6 months, updated by M12 and M24.