TRACE — Making Early SME Risk Build-Up Explainable

Applied PhD research | Conservative evidence layer (no new score, no consulting)

Observation from the field (aanleiding)

Across 300+ SMEs observed over multiple years in regional business ecosystems, a consistent pattern emerges that is directly relevant to interpreting banking risk.

Many SMEs that are financially low-risk—both below and above €1 million in exposure—report that a large part of their risk-preventive work occurs before financial stress becomes visible. This work appears as early coordination: renegotiating commitments, absorbing friction, repairing handovers, deliberately slowing growth, or reallocating risk internally to prevent escalation.

From the firms’ perspective, this work often remains invisible in standard banking interactions. From the bank’s perspective, these firms look similar to SMEs that are simply “financially quiet”—until stress reveals the difference too late.

This pattern surfaced repeatedly in conversations with entrepreneurs and bankers alike and was a key reason Rabobank colleagues invited me to propose a research project. It points to a structural gap: financial legibility is high, coordination legibility is low, and coordination failure is often the first place where fragility accumulates (Forrester, 1961; Ritchie-Dunham et al., 2024; Hinske, 2026a).

Why this matters now

Banking dashboards have become more powerful, more automated, and increasingly supported by analytics and AI. At the same time, the human sensing layer—especially for lower-risk SMEs—has thinned.

This creates a paradox: more data, but weaker early interpretation when signals do not align. CEOs recognize this as the “stuck dashboard” problem: indicators point in different directions, but there is no shared explanatory layer to interpret what is happening beneath the surface (Ritchie-Dunham, 2026).

Importantly, this is not an argument that AI or analytics are insufficient. It is the opposite: as analytical capacity increases, the risk of false precision and narrative overreach also increases unless the underlying evidence discipline is strengthened (Saltelli et al., 2020). TRACE is designed precisely as such a discipline.

Purpose and positioning (doel / inhoud)

TRACE adds a conservative explainability layer for early SME risk build-up by making coordination patterns inspectable, discussable, and auditable—without introducing a new score, replacing models, or redesigning policy.

This is not a consulting project and not a client intervention. It is an invitation for Rabobank to join an applied PhD trajectory that is already running with hundreds of SMEs and multiple financial institutions, and to help test whether this missing layer is decision-useful in banking practice.

What TRACE produces (form factor)

  • Short internal memos (1–2 pages) per recurring pattern cluster, containing:

    • Observable coordination “footprints”

    • A conservative risk translation (cost, delay, rework, cash tension)

    • Explicit non-claims (what it does not imply)

    • A suggested next test inside existing routines (not recommendations)

  • An anonymised pattern map showing which coordination footprints recur across cases.

These artefacts are designed to travel inside portfolio and monitoring routines (Black, 2013).

4. What is observed (and what is not)

TRACE focuses only on observable residues of coordination, not opinions or intentions. Examples include:

  • How exceptions are handled under pressure

  • Where decisions stall or bounce

  • Who absorbs downside risk when plans fail

  • Whether learning carries forward or resets

Documents are treated as claims unless verified against observable traces, reducing “paper compliance” risk (Star & Griesemer, 1989).

Hard boundary:
TRACE does not generate ratings, predictions, client advice, or improvement plans (Hinske, 2025c; Hinske, 2025d).

5. Werkwijze (how the research is conducted)

The research follows a signal-to-evidence ladder, ensuring that a valid signal is not mistaken for diagnosis:

Signal → multi-role coverage → trace checks → footprint patterns → working explanation → conservative risk translation → short memo + proposed test

This logic draws on established work in system dynamics and facilitated modelling, where the goal is decision-relevant understanding rather than model optimisation (Forrester, 1961; Franco & Montibeller, 2010; Richmond, 1993).

Cycle (first run with Rabobank)

  1. Baseline signal capture (lightweight; multi-role where possible)

  2. Pattern clustering across the case set

  3. Selective deepening (structured conversations + trace checks only where needed)

  4. Internal sense-check (usability, wording, boundaries)

  5. Repeat after ~6 months to test stability and practical value

6. Governance, data protection, and auditability

TRACE is designed safe-by-design:

  • No access to client files or dossiers

  • No recordings by default

  • No identifiable personal data in outputs

  • Anonymised aggregation for internal learning only

  • No uploading or sharing of confidential data into general AI tools

The aim is not richer storytelling, but stronger evidence discipline that remains defensible under scrutiny (Saltelli et al., 2020).

7. Tijdspad and resources

What Rabobank provides

  • One internal owner (single point of accountability)

  • Routing access to 12 SMEs (selection by Rabobank)

  • One short internal sense-check session

Indicative timeline

  • Months 1–3: Cycle 1 → memo + stop/continue decision

  • Month 6: repeat baseline + delta view

Stop rule:
If outputs do not travel inside existing routines or are judged “interesting but unusable,” the project stops after Cycle 1.

8. Invitation / routing ask

This proposal is an invitation to participate in and shape an applied PhD research trajectory, not to commission consultancy.

Request:
Assign an internal Rabobank owner and approve access to a small, anonymised start set of SMEs to test whether TRACE improves early-warning interpretability within existing portfolio routines.

Selected references (APA7)

  • Black, L. J. (2013). When visuals are boundary objects in system dynamics work. System Dynamics Review, 29(2), 70–86.

  • Forrester, J. W. (1961). Industrial dynamics. MIT Press.

  • Franco, L. A., & Montibeller, G. (2010). Facilitated modelling in operational research. European Journal of Operational Research, 205(3), 489–500.

  • Hinske, C. (2025c). Survey ≠ diagnosis: Using a valid signal without overclaiming it. 360Dialogues (PhD Notes).

  • Hinske, C. (2025d). Refusing recommendations: Why this research does not consult. 360Dialogues (PhD Notes).

  • Hinske, C. (2026a). Coordination debt kills good strategy. 360Dialogues (PhD Notes).

  • Ritchie-Dunham, J. L., Chaney Jones, S., Flett, J., Granville-Chapman, K., Pettey, A., Vossler, H., & Lee, M. T. (2024). Love in action: Agreements in a large microfinance bank that scale ecosystem-wide flourishing, organizational impact, and total value generated. Humanistic Management Journal, 9, 231–246. https://doi.org/10.1007/s41463-024-00182-y

  • Ritchie-Dunham, J. (2026). The CEO’s stuck dashboard: Managing the agreements field when purpose, ESG, and performance don’t line up.

  • Saltelli, A., et al. (2020). Five ways to ensure models serve society. Nature, 582, 482–484.

  • Star, S. L., & Griesemer, J. R. (1989). Institutional ecology, translations and boundary objects. Social Studies of Science, 19(3), 387–420.