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lib/behavior-model.js

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  1. // Model-backed user-behavior scoring (advisory only).
  2. // ---------------------------------------------------------------------------
  3. // The artifact (behavior-model.json) is trained + exported by
  4. // merchant-trust-data/models/behavior/train_behavior.py — a class-balanced L2
  5. // logistic regression over chronology-safe behavioral-deviation features,
  6. // learned from the challenge pack's authorization_history.csv (4,565 purchases,
  7. // 2025-09..2026-07; historical `status` is the past authorization outcome,
  8. // NOT a fraud label and NOT an answer key for live attempts).
  9. //
  10. // SEMANTICS: like the prompt-injection detector, this layer can NEVER approve,
  11. // decline, or loosen anything. The engine may surface its score as evidence
  12. // and route a strong anomaly to the customer's uncertainty policy. The layer
  13. // is inert when no artifact is deployed or the customer has no profile.
  14. //
  15. // Feature contract (must stay in lockstep with the trainer; asserted by
  16. // test/behavior-model.test.js against the trainer-emitted parity_vectors.json):
  17. //
  18. // 0 log_amount_z (log1p(chf) - log_mean) / log_std [n>=3]
  19. // 1 amount_p95_ratio min(10, chf / amount_p95) [n>=10]
  20. // 2 merchant_log_count log1p(approved count at merchant_id)
  21. // 3 merchant_unfamiliar 1 when that count is 0
  22. // 4 category_unfamiliar 1 when merchant_category never approved
  23. // 5 country_unfamiliar 1 when merchant_country never approved
  24. // 6 channel_unfamiliar 1 when channel never approved
  25. // 7 currency_unfamiliar 1 when currency never approved
  26. // 8 device_unfamiliar 1 when customer_device_id never approved
  27. // 9 hour_unobserved 1 when UTC hour never approved
  28. // 10 velocity_10m min(3, recent_attempt_count_10m)
  29. // 11 customer_log_total log1p(total_approved)
  30. // 12 night_hour 1 when UTC hour in 21:00-06:59 (generic night
  31. // window, NOT personalized; 0 when unreadable)
  32. // 13 log_item_qty_max log1p(max line quantity in the basket); 0 when
  33. // the event carries no item lines. Zero-variance
  34. // in the history training rows (authorization
  35. // history has no item lines), so it ships with
  36. // weight exactly 0 and only gains weight when
  37. // retrained over quantity-bearing data.
  38. // 14 qty_over_class_cap 1 when the max line quantity exceeds the
  39. // category's plausible cap (lib/item-classes.js;
  40. // raised to 3x the customer's observed per-category
  41. // max when profile.qty_max_by_category has data)
  42. //
  43. // p95 = nearest-rank; amounts are billing_amount_chf; auth fields resolve from
  44. // the flat attempt shape or the live event's merchant{} object.
  46. import { readFileSync } from 'node:fs';
  47. import { qtyCap } from './item-classes.js';
  49. // Mirrors NIGHT_HOURS in train_behavior.py — keep in lockstep.
  50. const NIGHT_HOURS = new Set([21, 22, 23, 0, 1, 2, 3, 4, 5, 6]);
  52. function loadModel() {
  53. const candidates = [
  54. new URL('./behavior-model.json', import.meta.url),
  55. new URL('../../merchant-trust-data/models/behavior/behavior-model.json', import.meta.url),
  56. ];
  57. for (const url of candidates) {
  58. try {
  59. const m = JSON.parse(readFileSync(url, 'utf8'));
  60. if (m && m.schema === 'openclaw.behavior-model/1' && m.weights && m.profiles && m.thresholds) return m;
  61. } catch { /* try next candidate */ }
  62. }
  63. return null;
  64. }
  66. let cached = loadModel(); // eager: artifact parse must never sit in the decision path
  67. export function getBehaviorModel() {
  68. return cached; // null when artifact is absent -> engine skips this layer
  69. }
  71. /** Test-only: swap the cached model (engine integration tests inject profiles
  72. * with quantity history through this; product code never calls it). */
  73. export function setBehaviorModelForTest(m) {
  74. cached = m;
  75. }
  77. /** Resolve a merchant-scoped field from either the flat attempt CSV shape or
  78. * the live event's nested merchant object. */
  79. function merchantField(auth, field) {
  80. return auth?.merchant?.[field] ?? auth?.[field] ?? null;
  81. }
  83. /** Feature vector for one authorization against a customer profile.
  84. * Mirrors profile_features() in the trainer. */
  85. export function behaviorFeatures(auth, profile) {
  86. const chf = Number(auth?.billing_amount_chf);
  87. const n = profile.n_amount_samples || 0;
  88. const f = [];
  90. // 0 log_amount_z
  91. if (n >= 3 && profile.log_std > 0) {
  92. f.push((Math.log1p(chf) - profile.log_mean) / profile.log_std);
  93. } else f.push(0.0);
  95. // 1 amount_p95_ratio
  96. if (n >= 10 && profile.amount_p95 > 0) {
  97. f.push(Math.min(10.0, chf / profile.amount_p95));
  98. } else f.push(0.0);
  100. const mid = merchantField(auth, 'merchant_id');
  101. const mcount = profile.merchants[mid] || 0;
  103. // 2 merchant_log_count, 3 merchant_unfamiliar
  104. f.push(Math.log1p(mcount));
  105. f.push(mcount === 0 ? 1.0 : 0.0);
  107. const inSet = (val, list) => (val != null && list.includes(val) ? 0.0 : 1.0);
  108. // 4-9 novelty flags
  109. f.push(inSet(merchantField(auth, 'merchant_category'), profile.categories));
  110. f.push(inSet(merchantField(auth, 'merchant_country'), profile.countries));
  111. f.push(inSet(auth?.channel, profile.channels));
  112. f.push(inSet(auth?.currency, profile.currencies));
  113. f.push(inSet(auth?.customer_device_id, profile.devices));
  114. const hour = auth?.timestamp ? new Date(auth.timestamp).getUTCHours() : null;
  115. f.push(hour != null && !Number.isNaN(hour) && profile.hours.includes(hour) ? 0.0 : 1.0);
  117. // 10 velocity_10m
  118. f.push(Math.min(3, Number(auth?.recent_attempt_count_10m) || 0));
  120. // 11 customer_log_total
  121. f.push(Math.log1p(profile.total_approved || 0));
  123. // 12 night_hour (generic window; 0 when timestamp unreadable — parity cases
  124. // always carry a valid timestamp, so this only affects degenerate callers)
  125. f.push(hour != null && !Number.isNaN(hour) && NIGHT_HOURS.has(hour) ? 1.0 : 0.0);
  127. // 13 log_item_qty_max, 14 qty_over_class_cap — basket line quantities
  128. // (resolve both the flat attempt shape and live {qty, category} items)
  129. let maxQty = 0, worstCat = null;
  130. for (const it of Array.isArray(auth?.items) ? auth.items : []) {
  131. const q = Math.max(1, Number(it?.quantity ?? it?.qty) || 1);
  132. if (q > maxQty) { maxQty = q; worstCat = it?.item_category ?? it?.category ?? null; }
  133. }
  134. f.push(maxQty > 0 ? Math.log1p(maxQty) : 0.0);
  135. f.push(maxQty > 0 && maxQty > qtyCap(worstCat, profile.qty_max_by_category).cap ? 1.0 : 0.0);
  137. return f;
  138. }
  140. function sigmoid(z) {
  141. const c = Math.max(-60, Math.min(60, z));
  142. return 1 / (1 + Math.exp(-c));
  143. }
  145. /**
  146. * Score one authorization against the customer's learned behavior profile.
  147. * Returns null when the layer is inert (no artifact, unknown customer, or
  148. * unreadable amount). Otherwise:
  149. * { score, band: 'normal'|'suspect'|'escalate',
  150. * factors: [{feature, label, contribution}], // top positive drivers
  151. * threshold, suspectThreshold }
  152. */
  153. export function scoreBehavior(auth, customerId) {
  154. const m = getBehaviorModel();
  155. if (!m || !auth || !customerId) return null;
  156. const profile = m.profiles[customerId];
  157. if (!profile) return null;
  158. const amount = Number(auth.billing_amount_chf);
  159. if (!Number.isFinite(amount)) return null;
  161. const feats = behaviorFeatures(auth, profile);
  162. const { mean, std } = m.standardization;
  163. let z = m.intercept;
  164. const contribs = [];
  165. for (let i = 0; i < feats.length; i++) {
  166. const zi = (feats[i] - mean[i]) / std[i];
  167. const w = m.weights[m.features[i]];
  168. z += w * zi;
  169. contribs.push({ feature: m.features[i], label: m.feature_labels[m.features[i]], contribution: w * zi });
  170. }
  171. const score = sigmoid(z);
  172. const factors = contribs.filter(c => c.contribution > 0.01)
  173. .sort((a, b) => b.contribution - a.contribution).slice(0, 3);
  174. const band = score >= m.thresholds.escalate ? 'escalate'
  175. : score >= m.thresholds.suspect ? 'suspect' : 'normal';
  176. return {
  177. score,
  178. band,
  179. factors,
  180. threshold: m.thresholds.escalate,
  181. suspectThreshold: m.thresholds.suspect,
  182. version: m.version,
  183. };
  184. }