AI-Driven Import Evaluation and Risk Scoring Framework

AVE
Oct 7
9 min read

Produced by: Ave Bilişim – Research & Data Analytics Unit Reference: ATR-2024-02-11 Type: Internal Technical Report (Prior Work) Completion Date: 20 February 2024 Confidentiality: Internal – Abstract page may be made public as a summary Authors: Research & Data Analytics Unit (5 researchers)

1. Abstract

This internal report documents a multi-phase research programme to design and evaluate an AI-driven import evaluation and risk scoring framework for document-centric, high-volume operational environments. The pipeline ingests declaration-like records, shipping manifests, product narratives and firm-registry data; performs document understanding (Transformer encoders), probabilistic entity resolution, and graph-based link analysis; and produces calibrated risk scores to guide targeted inspections under explicit workload budgets.

We formalise the problem as risk ranking under budget constraints, fuse complementary signals via a calibrated meta-learner (tabular gradient-boosted learner + heterogeneous GNN + document encoder), and embed the solution in a governed MLOps stack with lineage, model cards, and incident SOPs. On synthetic yet statistically realistic datasets, the ensemble improves Recall@budget by ~12 points over a strong tabular baseline and halves false positives versus rules, with Expected Calibration Error near 0.05. The system preserves interpretability through feature attributions, code-text divergence, route-graph snapshots and compact rationales.

This work was completed and archived in February 2024 as part of Ave Bilişim’s independent research portfolio. The methodology is domain-agnostic and transferable to other document-heavy, networked risk contexts.

2. Executive Summary

Challenge. Operational risk in import evaluation is buried across multi-language text, heterogeneous codes, values/quantities, route sequences, and historical relationships among firms and brands. Rules struggle with drift and limited context. Traditional classifiers often improve accuracy but lack calibration and traceability, weakening trust.

Approach. We built a five-layer architecture — ingestion → normalisation → modelling → scoring → analyst UI — that converts noisy paperwork into structured features; resolves entities across sources; constructs a firm–brand–route graph; and computes calibrated probabilities via a fused ensemble. The stack includes drift monitors, shadow scoring, canary deployments, and a feedback loop from analyst actions.

Outcomes. At fixed recall (0.80), ~48% fewer inspections; at fixed workload (30%), ~22% recall gain; ECE ~0.05. Explanations (SHAP, divergence maps, prototypes) reduce triage time and increase consistency across analysts.

Why it matters. The framework balances predictive power, explainability, and governance, delivering a portable methodology for document-centric risk decisions.

3. Problem Decomposition and Formalisation

Consider a declaration d with fields: d=(xtext, xcode, xval, xqty, xorigin, xdest, a1, a2, t)

manifest legs L={(ℓi,ti)}L=\{(\ell_i, t_i)\}L={(ℓi,ti)}, and registry entries RRR for actors/brands. We aim to compute a calibrated probability p^=P(y=1∣d,L,R)\hat p = P(y=1 \mid d,L,R)p^=P(y=1∣d,L,R) such that for a workload budget k%k\%k%, the top-k% ranked items maximise Recall@k while controlling FPR.

Model fusion:

s=σ ⁣(w1stext+w2stab+w3sgraph+b),p^=Calib(s;T),s = \sigma\!\left(w_1 s_{\mathrm{text}} + w_2 s_{\mathrm{tab}} + w_3 s_{\mathrm{graph}} + b\right), \quad \hat p = \mathrm{Calib}(s;T),s=σ(w1stext+w2stab+w3sgraph+b),p^=Calib(s;T),

where stexts_{\mathrm{text}}stext, stabs_{\mathrm{tab}}stab, sgraphs_{\mathrm{graph}}sgraph are logits from the document encoder, tabular learner and GNN; σ\sigmaσ is logistic; Calib\mathrm{Calib}Calib denotes temperature/isotonic scaling with temperature TTT.

Code–text divergence (miscoding indicator) uses cosine similarity between embedded product narrative and canonical code description:

δ=1−⟨ecode,etext⟩∥ecode∥ ∥etext∥.\delta = 1 - \frac{\langle \mathbf{e}_{\mathrm{code}}, \mathbf{e}_{\mathrm{text}}\rangle} {\|\mathbf{e}_{\mathrm{code}}\|\,\|\mathbf{e}_{\mathrm{text}}\|}.δ=1−∥ecode∥∥etext∥⟨ecode,etext⟩.

Large δ\deltaδ suggests inconsistency.

Entity resolution: a Fellegi–Sunter-style scorer SER(u,v)S_{\mathrm{ER}}(u,v)SER(u,v) over pairwise features (name distance, geohash proximity, director overlap, hashed contacts). Pairs with SER>τS_{\mathrm{ER}}>\tauSER>τ are merged; uncertain pairs are queued for clerical review (simulated).

Operating points. Two operating modes are defined: Fixed Recall (minimise workload at target recall) and Fixed Workload (maximise recall under a review budget).

4. Data Assets and Quality Controls

4.1 Sources (synthetic/anonymised)

• Declarations (8.2M rows): product text (multi-language), HS-like codes, unit/value/qty, origin/destination, incoterms, parties.

• Manifests (2.9M rows): vessel, route legs, container IDs, ETD/ETA.

• Firm registry (1.15M rows): names, aliases, addresses, directors, status.

• Watchlists (94k rows): mock brand/entity flags.

• Historical actions (410k rows): prior inspection outcomes used for simulation labels.

4.2 Normalisation & integrity

• Canonical schema; UTC time; duplicate suppression by (container, route, day); unit harmonisation.

• Language handling: transliteration, language ID, normalised tokenisation; raw text always preserved for audit.

• Lineage: dataset_id, transform_hash, feature_store_version recorded per record.

• Privacy: tokenised identifiers; controlled noise on sensitive aggregates; least-privilege access.

5. Feature Engineering

Textual features

• Transformer embeddings (small, domain-adapted); TF-IDF residuals for rare term bursts.

• Code–text divergence δ\deltaδ; synonym overlap; negation/uncertainty cues.

• Narrative quality: entropy, repetition ratio, language irregularity; abnormal length flags.

Entity/Network features

• Pairwise ER features (name edit distance; address geohash proximity; director overlap; phone/email hashes).

• Firm–brand–route heterogeneous graph metrics: degree/weighted degree, betweenness, clustering, community boundary score, repeat-pair frequency.

• Route anomaly: detour index, atypical trans-shipment motifs, dwell-time deviations.

Historical & behavioural

• Rolling violation rate by (firm, brand, code, route).

• Unit price z-scores vs historical medians; value/quantity inconsistencies.

• Seasonality, burstiness; portfolio stability for actors.

Selection & stability

• SHAP ranking per fold; any feature with >25% rank variance across folds is dropped. Final set ≈ 150 features (≈40 text, 55 network, 55 tabular/time).

Worked example (toy). Narrative: “industrial pump spare seal ring set”; code gloss: “rubber seals”. Cosine=0.62 ⇒ δ=0.38\delta=0.38δ=0.38 (consistent). Narrative: “ceramic dinner set”; code gloss: “industrial pump parts”. Cosine=0.21 ⇒ δ=0.79\delta=0.79δ=0.79 (flag).

6. Model Stack and Hyper-Parameters

• Document encoder: 6-layer Transformer, 8 heads, 256-d hidden; lr=2e-5; maxlen=160; mixed precision.

• Tabular learner: LightGBM/XGBoost, 2,000 trees, depth 8–12, lr=0.03; monotonic constraints on prior-violation features.

• Graph learner: heterogeneous GNN (R-GCN-like), 3 conv layers (64-64-32), relation dropout=0.15, residual connections + layer norm.

• Meta-learner: logistic regression with L2; fused logits from components.

• Calibration: temperature scaling (T≈1.7) + isotonic regression; reliability diagrams tracked.

Training protocol

• Time-ordered 80/20 split; 5-fold time-series CV; class imbalance handled via focal loss (γ=2) and cost-sensitive weighting.

• HP search by Bayesian TPE; early stop on AUROC; fixed random seeds for reproducibility.

• Leakage prevention: point-in-time feature joins; no future information bleed.

Robustness & ablations

• Synthetic drift (product mix ±10%, route mix ±12%, narrative style swap).

• Ablation deltas (F1 on holdout): −0.06 w/o text; −0.05 w/o network; −0.03 w/o history; −0.09 w/o graph features altogether.

7. Evaluation Design and Metrics

Primary metrics: F1, AUROC, Precision@k, Recall@k, FPR, ECE, latency (end-to-end). Operational: time-to-decision, analyst triage time, alert budget adherence, net benefit (decision-curve analysis).

Formulas (reference).

• F1=2PRP+R\text{F1} = \frac{2PR}{P+R}F1=P+R2PR where P=P=P= precision, R=R=R= recall.

• ECE=Σb=1B∣Sb∣n∣acc(Sb)−conf(Sb)∣\text{ECE} = \sum_{b=1}^B \frac{|S_b|}{n} \big| \text{acc}(S_b) - \text{conf}(S_b) \big|ECE=Σb=1Bn∣Sb∣acc(Sb)−conf(Sb) with bins SbS_bSb.

• Precision@k/Recall@k computed over the top-k% ranked items.

Holdout strategy: final chronological 10% chunk for forward performance; decision curves built from that holdout.

8. Results

8.1 Aggregate performance

Interpretation. The ensemble improves Recall@30% by +12 points vs GBDT, halves FPR vs rule sets, and reduces calibration error to ~0.05 — enabling stable thresholds.

8.2 Decision-curve & workload trade-offs

Across thresholds mapping to 20–40% workloads, the ensemble yields the highest net benefit with low sensitivity to policy shifts. Under fixed-recall (0.80), inspections reduce ~48%; under fixed-workload (30%), recall rises ~22%.

8.3 Error analysis

• FP-A (Ambiguous narrative): long, multi-language text with generic terms. Mitigation: stricter divergence floor + narrative-quality penalty.

• FP-B (Code drift): outdated code gloss vs modern narrative; update dictionary, re-embed code descriptions.

• FN-A (Novel pattern): new product/route combos; seed active learning on uncertain high-score near misses.

• Cold-start entities: little history; rely on conservative priors + prototype similarity until evidence accrues.

9. Case Studies

9.1 Case A – Narrative mismatch + route irregularity

Context. Two atypical trans-shipments on a lane historically direct; narrative vague (“equipment set”), high code–text divergence δ=0.74\delta=0.74δ=0.74. Signals. Route detour index high; community boundary score increases; prior clean history but sudden value/qty variance. Outcome. Fused logit 1.94 ⇒ p^=0.87\hat p=0.87p^=0.87; flagged day-0; synthetic label confirms. Analyst explanation. “High divergence, unusual route motif, rising unit-price variance; nearest prototype from quarter-3 last year.”

9.2 Case B – Recurrent pair risk

Context. Firm–brand pair appears monthly; repeat-pair frequency climbs; betweenness centrality elevated. Signals. Portfolio stability declines; seasonality breaks; watchlist adjacency (mock) marginal. Outcome. p^=0.71\hat p=0.71p^=0.71; targeted review scheduled. Remediation. Add prototype evidence, monitor for two cycles before stricter thresholds.

10. System Design, MLOps and Security

10.1 Architecture

• Ingestion: Kafka + batch importers; schema registry; validation checks.

• Processing: Spark/Beam for NLP and feature generation; feature store with point-in-time joins; Redis cache for hot features.

• Model services: TF/PyTorch/GBDT behind gRPC; model registry + lineage + approvals.

• Scoring: calibrated meta-learner; shadow scoring prior to promotion; canary deployments.

• Dashboards: Grafana/Plotly views with SHAP, divergence heatmap, graph snapshots, prototypes and rationales.

10.2 Performance & resilience

Sustained 95k records/min; p95 E2E latency <10 s over eight-hour stress runs; autoscaling; bounded queues; priority channels for critical flows; structured logs, metrics and traces for deep audits.

10.3 Security

Container hardening, image scanning, secrets in KMS, mTLS, least-privilege IAM, network policies and rate limiting.

11. Interpretability and Analyst Workflow

Each alert contains:

1. Top contributing features (SHAP),

2. Code–text divergence heatmap and score,

3. Route-graph neighbourhood snapshot,

4. Nearest prototypes with short notes,

5. Compact rationale (2–4 lines).

Counterfactual (training-time only). Minimal narrative/metadata change that would drop score below threshold (non-prescriptive, analyst education).

Triage loop. Ranked queue; actions clear / inspect / defer logged to feedback store; weekly threshold tuning under governance sign-off; change log with version hashes.

12. Governance, Fairness and Risk Management

• Model cards & data sheets: scope, assumptions, metrics, slices, limitations, failure modes.

• Lineage & approvals: data- and model-level hashes; reviewer roles; timestamps.

• Fairness monitoring: slices by product family, route region, proxy firm size; gaps >5% trigger remediation (re-weighting, threshold per slice).

• Risk register (excerpt):

o R-01 Data drift → PSI monitors; daily temperature rescaling.

o R-02 Cold-start mis-scoring → priors + prototype similarity.

o R-03 Alert fatigue → budgeted alerts; explanation-first UI.

o R-04 Feature leakage → strict time-ordered CV + audits.

o R-05 Infra bottlenecks → autoscaling, backpressure, canary deploys.

13. Standard Operating Procedures (SOPs)

SOP-01: Model change control

1. Open Change Request (CR) with scope and KPIs.

2. Offline evaluation on frozen splits; fairness slices, calibration diagrams.

3. Governance review & approval.

4. Shadow scoring for ≥1 week; compare to incumbent on live metrics.

5. Canary promotion with rollback guardrails.

6. Full promotion upon acceptance thresholds; archive artefacts and decisions.

SOP-02: Incident response

• Trigger: drift, spike in FPR, infra errors.

• Actions: freeze promotions; revert to last good model; root-cause analysis (RCA) within 48h; corrective actions documented.

SOP-03: Threshold governance

• Weekly review of alert budgets; monitor Recall@budget, FPR, triage time; adjust thresholds under approval; publish change notes.

14. Extended Technical Notes

Calibration. We employ temperature scaling to minimise negative log-likelihood on validation:

min⁡TΣi−log⁡σ ⁣(ziT)then piecewise isotonic refinement.\min_T \sum_i -\log \sigma\!\left(\frac{z_i}{T}\right) \quad\text{then piecewise isotonic refinement.}TminiΣ−logσ(Tzi)then piecewise isotonic refinement.

Uncertainty. For near-threshold cases, we abstain and queue for analyst review; this reduces brittle auto-decisions under drift.

Conformal add-on (optional). A non-parametric prediction set approach yields coverage guarantees; in our tests it increased abstentions ~3–5% while reducing residual FPR.

15. Limitations

• Narrative ambiguity. Multi-language, free-form text can remain noisy without domain lexicons.

• Sparse tails. Rare product–route combinations under-represented; active learning recommended.

• Cold-start graphs. Thin networks limit GNN benefits; priors help but do not eliminate risk.

• Policy alignment. Real deployments require context-specific legal/policy reviews.

16. Future Work

• Multilingual distillation for compact encoders.

• Live registry APIs for near-real-time entity enrichment.

• Semi-supervised learning to leverage unlabelled flows.

• Conformal thresholds + online calibration refresh.

• Short public abstract (2–3 pages) for knowledge sharing.

17. Conclusions

This report presents a reproducible, explainable and governed framework for import evaluation and risk scoring. By combining document understanding, entity resolution, graph analytics and calibrated fusion, the approach delivers measurable gains in Recall@budget with lower FPR and credible probability outputs. The solution integrates with analyst workflows, features robust MLOps practices, and maintains auditability through model cards, data sheets, lineage and SOPs.

The work was finalised and archived on 20 February 2024 as a prior internal study by Ave Bilişim’s Research & Data Analytics Unit.

1 Appendices

Appendix A – Metric Definitions

• Accuracy: (TP+TN)/(TP+FP+TN+FN)(TP+TN)/(TP+FP+TN+FN)(TP+TN)/(TP+FP+TN+FN) — limited value under imbalance.

• Precision: TP/(TP+FP)TP/(TP+FP)TP/(TP+FP) — alert quality proxy.

• Recall (Sensitivity): TP/(TP+FN)TP/(TP+FN)TP/(TP+FN) — missed-risk tolerance.

• F1: harmonic mean of precision/recall.

• AUROC: threshold-free ranking quality.

• Precision@k, Recall@k: performance under workload budget.

• ECE: average gap between predicted confidence and empirical accuracy.

• Latency: ingestion→score wall-clock time (p50/p95).

• Net benefit (decision-curve): utility that weights TP vs FP under policy costs.

Appendix B – Model Parameters

B.1 Document encoder Layers=6; Heads=8; Hidden=256; Maxlen=160; Dropout=0.1; LR=2e-5; Warmup=2k steps; Batch=64; Epochs=6.

B.2 Tabular learner (LightGBM) Trees=2000; Depth=8–12; LR=0.03; L2=1e-2; Colsample=0.8; Subsample=0.8; Monotone constraints on prior-violation features.

B.3 Graph learner (Hetero GNN) Layers=3 (64-64-32); Relation dropout=0.15; Residual=on; Norm=layer; Optimiser=Adam 1e-3; Epochs=40.

B.4 Meta-learner & Calibration LogReg(L2=1.0); Temperature T≈1.7; Isotonic bins=15; Validation split: last 10% chronologically.

Appendix C – Data Schema & Quality Rules

Core fields: entity_id, counterpart_id, timestamp_utc, geohash, modality, attrs_json, route_leg_id, container_id, code, code_gloss. Rules: timestamp monotonicity; null ratio <5% for critical fields; dedup by (container, route, day); unit normalisation; language ID confidences. Lineage fields: dataset_id, ingestion_job_id, transform_hash, feature_store_version.

Appendix D – Synthetic Data Generator (Specification)

• Narratives: template + noise; bilingual variants; synonym replacements; rare-term injection.

• Flows: Hawkes processes for bursts; seasonality; shocks for drift.

• Routes: shortest-path baseline + perturbations for detours; dwell-time heavy tails.

• Labels: injected motifs define positives; overlap set aside for near-misses to test calibration.

Appendix E – SOP: Model Lifecycle & Incident Handling (Summary)

Change control: CR → offline eval → fairness/calibration review → shadow → canary → promote/rollback. Incident: detect (drift/latency/FPR), freeze, revert, RCA, corrective actions, post-mortem. Documentation: versioned model cards/data sheets; decision logs; alert budget reports.