ID2Pattern: Pretraining Generalizable Identity Behavior Patterns for Transaction Risk Foundation Models

Ze Zhao; Haoyu Jiang; Weiyu Han; Ruobing Zuo; Xiao Tan; Shuai Chen; Tianyi Zhang

cs.AIOct 30, 2026

ID2Pattern: Pretraining Generalizable Identity Behavior Patterns for Transaction Risk Foundation Models

Authors: Ze Zhao, Haoyu Jiang, Weiyu Han, Ruobing Zuo, Xiao Tan, Shuai Chen, Tianyi Zhang

Organizations: Ant International · East China Normal University

Abstract

Large-scale behavioral pretraining offers a promising route to generalizable representations in online payment systems. However, high-cardinality identity fields such as card, device, and account IDs, though rich in behavioral signals, remain underexploited. Their continually growing vocabularies and limited semantic meaning make existing vocabulary-based and semantics-based encodings ill-suited. Their generalizable signal lies not in specific values, but in the identity behavior patterns they exhibit across a transaction sequence. Building on this insight, we propose ID2Pattern, a pretraining framework that brings these patterns into transaction foundation models. ID2Pattern first encodes identity fields with a Local Anonymous ID (LAID) tokenizer, which captures how each identity appears and recurs within the sequence, independent of its specific value. It then pretrains a multi-scale pattern codebook on unlabeled sequences, which abstracts LAID sequences into discrete behavior patterns for downstream prediction. Experiments on an industrial dataset with over 100M sequences and six public datasets show that ID2Pattern consistently outperforms strong baselines, improving industrial by 4.13% and public datasets by 8.83% on average. Notably, ID2Pattern remains equally effective even when all identity values are unseen during training, highlighting its potential to generalize to new users and markets.

Figures & tables

Figure 1. Identity fields vs. semantic attributes on our industrial payment data: (a) continual vocabulary growth; (b) limited semantic meaning.

Figure 2. Overview of ID2Pattern . (A) Pattern tokens and event tokens are jointly modeled for risk prediction. (B) The LAID tokenizer encodes each identity by its relative identity within the sequence. (C) The multi-scale pattern codebook quantizes LAID sequences over short, medium, and long windows into discrete pattern tokens.

Dataset	Domain	#Sequences	#ID Fields	Pos. Rate
Industrial	Risk	100.0M+	9	–
Elliptic++	Risk	37.3K	2	5.69%
IBM AML	Risk	1.54M	2	0.20%
CCT	Risk	7.44M	2	0.12%
RetailRocket	Commerce	540.4K	1	2.78%
Synerise	Commerce	6.12M	2	24.44%

Table 1. Statistics of evaluated datasets. #Sequences counts the supervised targets, each with its own history sequence; the positive rate is computed on the test set.

Method	AUC-ROC (%)	T1 (%)	AddCB (%)
Semantic-attribute-only	—	—	—
+ Hash ID	$-5.87$	$-11.91$	$-8.07$
+ Hash ID (relabeled)	$-8.00$	$-50.53$	$-46.96$
+ LAID	$+1.11$	$+6.96$	$+6.09$
+ LAID (relabeled)	$+1.11$	$+6.96$	$+6.09$
ID2Pattern	+4.13	+22.64	+19.67

Table 2. Relative performance changes on the industrial payment-risk test set, using the semantic-attribute-only baseline as the reference.

Figure 3. Online shadow evaluation from July to August. AUC is normalized by the average AUC of the semantic-attribute-only model over the first three days.

	Risk						Commerce
	Elliptic++		IBM AML		CCT		RetailRocket		Synerise		Taobao
Method	PR	[email protected]%	PR	[email protected]%	PR	[email protected]%	PR	[email protected]%	PR	[email protected]%	PR	[email protected]%
Sequential Models
LSTM	.2410	.0697	.2014	.3436	.0318	.0665	.7255	.5168	.4411	.0341	.2078	.1528
GRU	.2428	.0860	.1600	.3594	.0315	.0675	.7358	.5685	.4487	.0337	.2102	.1553
Transformer	.2333	.0540	.2129	.3800	.0303	.0873	.7518	.5695	.4138	.0214	.2026	.1480

Table 3. Performance on six public datasets in terms of PR-AUC (PR) and recall at a false positive rate of at most 0.5% ([email protected]%). Best results are in bold, and second-best results are underlined.

Ablation Variants	Risk		Commerce
Ablation Variants	CCT	IBM AML	RetailRocket	Synerise
w/o LAID	.0338	.2018	.7814	.4228
Local ID Only	.0600	.2695	.8152	.4718
w/o Pattern Branch	.0418	.1880	.8064	.4734
Single-scale	.0526	.2263	.8087	.4700
w/o Codebook	.0419	.2047	.8092	.4630

Table 4. Ablation study of different components (PR-AUC).

Figure 4. Generalization to unseen identities on CCT and Taobao. Left: PR-AUC with original and fully relabeled identities. Right: PR-AUC under increasing relabeling ratios.

Figure 5. Case study on learned codebooks. Left: positive-rate lift of codes at each scale. Right: multi-scale code combinations describing behavior patterns on CCT and Taobao.

	Risk		Commerce
Backbone	IBM AML	CCT	RetailRocket	Taobao
GRU	.1600	.0315	.7358	.2102
+ ID2Pattern	.2397	.0345	.7700	.2756
Improv.	+49.8%	+9.5%	+4.6%	+31.1%
LSTM	.2014	.0318	.7255	.2078
+ ID2Pattern	.2543	.0268	.7763	.2678

Table 5. PR-AUC of different backbones with and without the identity modeling of ID2Pattern , averaged over 3 seeds.

Pretraining data	Taobao	RetailRocket	Synerise
Without pretraining	.2608 $\pm$ .0042	.8011 $\pm$ .0055	.4561 $\pm$ .0036
Target only	.2686 $\pm$ .0022	.8283 $\pm$ .0067	.4776 $\pm$ .0047
Other two datasets	.2698 $\pm$ .0025	.8313 $\pm$ .0016	.4836 $\pm$ .0010

Table 6. PR-AUC with the pattern encoder pretrained on the target dataset alone or on the other two commerce datasets.

Figure 6. Efficiency of identity representations. (a) Parameters per identity field; numbers give identities per hash bucket. (b) Inference time per sample.

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Strategy	Size grows with IDs	Unseen IDs	Identity behavior
Vocabulary-based	Linear	OOV vector	Value-tied
Hashing	No	Arbitrary code	Value-tied
Hand-crafted	No	Supported	Predefined
Semantics-based	No	If content	Not captured
LAID (ours)	No	Invariant	Learned

Appendix

Table 7. Comparison of strategies for encoding high-cardinality identity fields.

Dataset	Positive class	Identity fields	Subjects	Train	Valid	Test	$L_{\max}$
CCT	fraud	merchant name, city	6,139	125,304	3,657,794	3,656,690	512
IBM AML	laundering	receiving bank, account	496,999	15,936	760,071	760,345	512
Elliptic++	illicit	two output addresses	46,564	18,360	7,765	11,184	256
Taobao	purchase	item, category	987,991	2,683,554	10,944,062	25,628,669	100
RetailRocket	purchase	item	58,653	428,761	55,209	56,467	100
Synerise	7-day conversion	SKU, URL	2,032,633	4,944,430	529,289	642,336	512

Appendix

Table 8. Details of the public datasets: positive class, identity fields, number of sequence subjects and supervised targets in each split, and maximum history length

L_{\max}

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Ant International accumulates large volumes of transaction history across users and payment businesses. Confirmed fraud and chargeback outcomes, by contrast, are scarce, delayed, and specific to each business. A single encoder pretrained on unlabeled histories could serve all businesses, but only if pretraining captures the behavior that separates risk. Coarse activity differences can make contrastive pretraining on transaction logs too easy: transaction volume, amount, and timing can distinguish a history's continuation from arbitrary candidates without modeling how payment resources are used. We present TRIDENT-Seq, a transaction foundation model trained with Prefix-Future Contrast (PFC). PFC matches each prefix to a summary of its subsequent behavior among candidates with similar activity profiles; activity prediction and dispersion regularization act on the encoder output. Across three production payment businesses, the pretrained encoder improves average precision (AP) and recall at a 0.5% intervention budget over architecture-matched scratch training in all six business-budget settings. With a fixed validation set, it exceeds full-label scratch training in mean AP and recall using 10% of the training labels on Wallet and 25% on Shopping and SME. Pretraining only on another business's histories still helps. Replacing profile-matched candidates with random ones cuts recall from 24.62% to 5.64%. In Ant International's deployment evaluation, recorded amount-based chargeback and overall loss rates are 37.7% and 37.9% lower than those of the previous XGBoost-based system under the same intervention budget.

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