Temporal Graph Networks for Bank Customer Churn Prediction
with Dynamic
Peihao Sun
a
College of Art & Sciences, University of Washington, Seattle, Washington, United States
Keywords: Temporal Graph Networks, Customer Churn Prediction, Dynamic Relationship Modeling, Causal Temporal
Difference, Targeted Retention Strategies.
Abstract: Customer churn prediction is a critical issue in banking, with a direct influence on customer retention strategy
and financial health. Traditional churn models such as Recency-Frequency-Monetary (RFM) analysis ad static
graph-based models fail to capture the dynamic nature of client-product interaction, social influence effects,
and policy shocks. To address these limitations, this work proposes a Temporal Graph Network (TGN)-based
model that integrates dynamic node embeddings and causal temporal difference mechanisms to model real-
time financial transactions. The proposed approach is tested on a 12-month transactional dataset of a European
retail bank, with a 12–18% improvement in prediction accuracy over baseline models such as LSTM, GCN,
and Node2Vec. By leveraging TGN-computed churn risk scores, the paper applies three levels of tailored
retention interventions, i.e., dynamic fee remissions, referral stabilization rewards, and policy-sensitive rate
adjustments, which collectively boost customer lifetime value (CLV) by 14% and cross-sell rates by 22%.
The findings show the effectiveness of temporal graph-based modeling in financial analytics, presenting an
interpretable and scalable churn prediction solution. Subsequent research must explore federated learning
techniques to enable privacy-sustaining cross-bank collaboration as a supplement to the impact of temporal
graph-based knowledge on financial decision-making.
1 INTRODUCTION
Customer churn is one of the primary challenges in
banking and has direct ramifications on revenue
consistency, cost of doing business, and customer
relations management for the long term. Being able to
predict and manage customer loss is critical as
keeping existing clients costs significantly less
compared to acquiring new ones. Surveying industry
experiences indicates that modest increases in
retaining customers translate to enormous
profitability and lends significance to data-based
models of predicting customer attrition (Huang et al.,
2012; Çelik & Osmanoglu, 2019). In a highly
competitive banking sector where customers have a
wide range of banking alternatives, traditional churn
prediction models have not been sufficient to define
the dynamics of evolving customer-bank
relationships (Tsai & Lu., 2009; Vafeiadis et al.,
2015).
a
https://orcid.org/0009-0006-2685-6589
Compared to conventional models, TGN-based
models adopt temporal node embeddings that evolve
dynamically, allowing churn prediction in real-time
with greater accuracy (Rossi et al, 2023). By
incorporating causal temporal difference mechanisms,
TGN models enable policy-driven churn to be
distinguished from natural customer attrition, such
that retention initiatives are targeted at genuinely at-
risk customers and not those reacting to short-term
external stimuli. This is particularly significant for
financial institutions that wish to maximize targeted
retention treatments while minimizing wasteful
outreach. The ability to model macroeconomic
impacts, network-caused churn propagation, and
time-varying product-client interaction makes TGN-
based approaches especially well-suited for modern-
day churn prediction problems.
This paper also addresses the real-world effect of
TGN-based churn prediction on banks. On the basis
of TGN-computed churn risk scores, banks can
initiate focused retention measures such as dynamic
324
Sun, P.
Temporal Graph Networks for Bank Customer Churn Prediction with Dynamic.
DOI: 10.5220/0013688900004670
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 2nd International Conference on Data Science and Engineering (ICDSE 2025), pages 324-331
ISBN: 978-989-758-765-8
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
fee remissions, referral stabilization rewards, and
policy-adaptive rate adjustments. These measures are
observed to increase customer lifetime value (CLV)
by 14% and cross-sell rates by 22%, thereby
measuring the real-world benefit of temporal graph-
based analytics. Furthermore, this research
demonstrates the potential of federated learning
techniques to enable cross-bank collaboration in
churn prediction without compromising customer
privacy, addressing a significant problem in data-
sharing regulations. The findings contribute to
financial analytics by providing a scalable,
interpretable, and privacy-conformable approach to
churn prediction. Future studies will focus on
enhancing model interpretability and exploring
counterfactual analysis techniques to model other
retention policies under different economic
conditions.
2 DYNAMIC RELATIONSHIP
ANALYSIS AND PROBLEM
FORMULATION
2.1 Key Dynamic Relationships
Banking customer churn is driven by several
interdependent factors, which change over time and
dynamically interact with each other in a larger
financial network. In contrast to static predictive
models based on snapshots of historical data, a
temporal graph-based method allows for real-time
modeling of changing relationships. The three main
elements of such dynamic relationships—client-
product interactions, client-client social referrals, and
policy shocks from outside the system—each have
unique temporal patterns and network propagation
effects. Figure 1 illustrates how these variables are
intertwined in a temporal graph model, showing
interdependencies that heighten or diminish churn
risk. These interdependencies must be known to
create predictive models that represent actual
customer actions and enable proactive intervention
strategies.
Figure 1: Overview of Dynamic Relationships in Bank
Customer Churn. (Picture credit: Original)
2.2 Temporal Graph Construction
Construction of a temporal graph for bank customer
churn forecasting is the incorporation of dynamic
interactions between clients, financial products, and
outside activities. Unlike static graphs that accept
things as they are at a given moment, a temporal
graph is dynamic and changing in real-time to offer
real-time updates that improve the predictability of
churn models. The construction process entails
defining the important building blocks of the graph,
edge and node features, temporal pattern encoding,
dynamic adjacency matrix update, and efficient graph
update strategy. Systematic organization of such
components guarantees the model is able to capture
timely financial behavior. The required components
are listed in Table 1, and Figure 2 displays the graph
movement across three time windows to indicate how
relationships among transactions evolve with time.
Table 1: Overview of Temporal Graph Component.
Component
Description
Data Source
Nodes
Clients (age, income, product holdings)
Customer Relationship Management (CRM) System
Products (APR, Risk Level, Liquidity)
Bank’s Product Database
Edges
Transaction Frequency (Weekly, Monthly)
Transaction Records
Social Refferral Relationships
Internal Refferral Program Logs
Timestamps
Event Occurrence Time (Unix Format)
System Logs
Edge Weights
Transaction Volume (Log-Scaled)
Transaction Data
Temporal Graph Networks for Bank Customer Churn Prediction with Dynamic
325
Figure 2: Q1, G2 and Q3 Transactions. (Picture credit:
Original)
2.2.1 Node and Edge Attribute Specification
Node and edge definition are required in order to have
the model detect helpful patterns of behavior. Single
bank customers constitute client nodes, and they are
defined by demographic and behavioral features that
inform their money decisions. Demographic
information includes age, income group, and
occupation class variables, as cited in previous
studies on financial risk assessment. In addition,
behavioral attributes such as product dispersion and
monthly transaction volatility are added to portray
customer banking conduct. These characteristics
capture economic stability and risk-taking tendencies,
which are most critical in predicting impending churn.
Product nodes capture the bank's financial
products, each defined by important financial features.
These range from the annual percentage rate (APR)
that dictates the cost of borrowing to risk level
categorizations that vary from low-risk savings to
high-volatility investment products. Liquidity is
another important feature, which differentiates fixed-
term and demand deposit accounts in terms of how
accessible they are and under what withdrawal terms.
Its popularity is measured by a ninety-day rolling
window normalized transaction value, which
monitors shifts in client activity over time.
The line in the timeline graph marks interaction
between clients and financial products by frequency
and volume of transactions to allow for an actual
projection of financial movement. The transactions
are weighted based on their value, and more valuable
transactions have a greater influence on the model's
predictions. A directed graph is formed, where
forward edges from users to products indicate
purchases and reverse edges indicate redemptions or
withdrawals. The directed attributes allow the model
to distinguish between asset accumulation and
liquidation behavior, important indicators of financial
decision-making and churn.
2.2.2 Temporal Encoding and Data
Procesing
To keep the temporal character of financial activities,
raw Unix timestamps are mapped into cyclical
temporal features using sine and cosine functions.
This type of encoding keeps periodic patterns of
financial activity, such as salary deposits, recurring
bill payments, and seasonal expenditure patterns. The
transformation is mathematically defined as
where t is the Unix timestamp. The division of
encoding by 86400 seconds per day and 604800
seconds per week ensures that the model picks up on
the cyclical patterns of financial transactions. The
encoding is quite excellent at picking up patterns such
as increased spending near the end of the month, tax-
based financial patterns, and payroll payment
schedules. Missing transactional data that accounts
for approximately 3.7% of records are imputed by a
temporal k-nearest neighbor approach with k=5 based
on similar customers' historical activities for
reconstruction.
2.2.3 Dynamic Adjacency Matrix
Formulation
The adjacency matrix of the temporal graph, denoted
as
, is constructed to dynamically modify in
accordance with real-time economic interactions.
Contrary to the static adjacency matrices, which are
invariant in time, this construction allows for the
consideration of the fact that customer-product
ICDSE 2025 - The International Conference on Data Science and Engineering
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relationships change dynamically according to
transactional behavior. The adjacency matrix can be
represented mathematically as
where
is the number of transactions
client i has with product j, and
is the log-
scaled average transaction volume in euros.
Normalization terms
and
adjust
for single product holdings and the number of clients
for a particular financial instrument. This
specification punishes clients who have high-
frequency trading or who engage disproportionately
with niche financial instruments, reducing overfitting
in the model as well as improving the stability of
churn predictions.
2.2.4 Hybrid Graph Update Mechanism
The temporal graph is refreshed by a hybrid update
mechanism that balances computational efficiency
and real-time responsiveness. High-value
transactions, such as redemptions exceeding fifty
thousand euros, trigger immediate updates to node
and edge attributes. Yet, normal financial traffic, such
as small deposits or subscription payments, is updated
by batch updates accumulated at hourly intervals.
This hybrid approach reduces computational latency
without sacrificing the temporal granularity
necessary for successful churn modeling, as the
performance evaluation demonstrated. The hybrid
update method was demonstrated to reduce latency by
41% compared to pure event-driven systems without
loss of predictive accuracy (98% accuracy) (Huang et
al., 2023). This trade-off between update frequency
and computational cost ensures the model's
scalability to high-throughput banking environments.
3 CASE STUDY AND
INTERVENTION STRATEGIES
3.1 Empirical Validation
In the interest of validating the effectiveness of
proposed TGN architecture, a real-case study was
conducted on transactional data for a European retail
bank with 45,000 active customers. The study spans
twelve months, incorporating transaction history,
social referral relationships, and macroeconomic
events external to the graph, including central bank
rate hikes and changes in regulation. The purpose of
this case study is to validate the model's capability to
accurately predict customer churn, benchmark its
performance against existing methods, and test the
effectiveness of intervention strategies targeted using
TGN-based risk scores.
3.1.1 Dataset and Experimental Setup
The data employed in this research comprise three
primary components: client profiles, event
interactions, and policy shocks. Client profiles consist
of age, income level, product balances, and trading
volatility demographic data, which provides
extremely precise customer behavior data. Event
interactions consist of 2.1 million time-stamped
purchases, redemptions, and fund flows between
clients and financial products. In addition, the dataset
includes six large macroeconomic policy shocks,
including four central bank interest rate changes and
two regulatory changes, enabling the model to
examine the impact of external financial changes on
churn behavior. Table 2 depicts the description of the
composition of the dataset.
Table 2: Dataset Overview.
Component
Volume/Count
Temporal Range
Clients
45000
N/A
Transactions
2100000
Jan - Dec 2023
Policy Events
6
Q1 - Q4 2023
Social Referrals
12500
N/A
TGN was contrasted against three of the most
well-liked baseline models: LSTM (Long Short-Term
Memory networks), Static Graph Convolutional
Networks (GCN), and Node2Vec. The data were split
into 80% training, 10% validation, and 10% testing
sets for balanced evaluation across different model
structures.
3.1.2 Model Performance and Risk
Identification
The predictive performance of the TGN model was
compared against typical measures of classification,
including AUC-ROC, F1-score, precision, and recall.
As reflected in Table 3, the TGN framework was
superior to baseline models at every point in time,
particularly for identifying high-net-worth clients
with over €100,000 in assets.
Temporal Graph Networks for Bank Customer Churn Prediction with Dynamic
327
Table 3: Performance Comparison on High-Value Clients.
Model
AUC-ROC
F1-Score
Precision
Recall
LSTM
0.781
0.682
0.714
0.653
Static GCN
0.796
0.703
0.726
0.681
Node2Vec
0.723
0.641
0.669
0.615
Proposed TGN
0.854
0.761
0.792
0.732
The TGN model reduced false positives by 23%
compared to LSTM, which is the most important
aspect of maintaining minimum retention costs.
Clustering analysis revealed two distinct high-risk
segments with higher chances of churn. Hub
customers, customers with a large social circle in the
bank, had a 58% probability of churning within thirty
days of a close referrer's exit. Peripheral clients, with
less financial product diversification, experienced a
41% churn rate after big policy shocks since they
were more likely to be influenced by interest rate
changes and regulatory modifications. Figure 3
provides a graphical representation of churn risk
distributions of different client clusters.
Figure 3: Churn Risk Distribution Client Clusters. (Picture
credit: Original)
3.1.3 Targeted Intervention Strategies
In response to TGN-driven analysis, the bank
introduced two data-driven retention programs
designed to offset churn risk in at-risk customer
segments. The programs were targeted to address key
drivers of churn head-on, including the effects of
policy-related financial pressure and network-driven
attrition.
As compensation for the negative effect of
monetary policy tightening and interest rate increase,
waivers for fees were made for those most vulnerable
to monetary policy shocks based on a higher-than-0.7
causal impact score (Huang et al., 2023). Clients
meeting the stipulated threshold received fee waivers
that lasted up to ninety days with a purpose to offset
the near-term liquidity constraints. As discovered, it
proved very efficient and reduced the rate of churn by
19% and amounted to about €1.2 million saved each
quarter in revenues.
For extremely socially networked highly referral-
central clients, an incentives strategy was undertaken
to stabilize the retention within the network. A €50
client referral bonus was offered to degree centrality
fifty-plus clients when they kept three or more
referred clients for at least three months. The
intervention yielded a reduction in churn for the hub
clients by 27%, with an incentive cost vs. revenue
retained five-to-one return on investment.
The relative effectiveness of these specific
interventions is calculated in Table 4, comparing the
impact of fee waivers, referral bonuses, and generic
email-based retention efforts.
Table 4: Effectiveness of Targeted Retention Interventions.
Strategy
Churn
Reduction
Cost per Saved
Clieent (€)
Dynamic Fee
Waivers
19%
120
Referral Bonuses
27%
85
Generic Email
Campaigns
6%
210
The results indicate that focused financial
incentives based on TGN-driven churn risk analysis
outperform traditional generic retention strategies,
such as mass email campaigns.
3.1.4 Operational Challenges and
Mitigations
While the intervention strategies worked well, there
were two significant operational concerns that were
faced in deployment. First was the real-time data
latency issue, where constant updating of the dynamic
transaction graph resulted in 15% latency spikes
during peak transaction periods. To address this, the
system was redesigned to prioritize high-risk
transaction updates through Apache Kafka stream
processing, which successfully removed update
latency without any loss in model accuracy (Zhang et
al., 2023).
The second issue had to do with client privacy
issues, specifically with the sharing of transactional
data between subsidiaries. To overcome this
challenge, the Federated TGN framework (described
in Section 3.3.3) was implemented, which enabled
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training models locally by each branch and only
sharing aggregated knowledge. This effort managed
to lower direct exposure of data by 92% and still
retain 97% of the predictive power of the original
model.
3.1.5 Long-Term Impact Assessment
A post-intervention analysis was conducted after six
months to determine the sustainability of retention
efforts and overall financial performance. The
outcome indicated that customer lifetime value (CLV)
had been increased by 14% for the customers who
were treated with focused interventions,
demonstrating long-term improvement in customer
engagement and profitability. In addition, cross-sell
rates—uptake of new financial products by current
customers—rose by 22%, suggesting that customers
who were favorably affected by proactive retention
efforts established greater trust levels in the financial
products provided by the bank.
These results demonstrate the practical utility of
the TGN model, revealing how data-driven churn
prediction and targeted intervention can successfully
strengthen customer retention while optimizing the
use of financial resources. Next steps for research
include integrating reinforcement learning techniques
to automate and optimize retention policies further,
enabling banks to implement real-time adaptive
interventions to evolving customer behavior.
3.2 Intervention Strategies
Not only does the TGN framework enable accurate
churn forecasting, but also actionable customer
retention policy prescriptions with resource and
intervention time optimization. Despite the clear
advantages the framework has over using legacy
models, it has numerous challenges to overcome in
order to strengthen its robustness and scalability. This
section explains the three-level retention strategies
grounded on TGN's findings, criticizes the current
weaknesses of the framework, and suggests directions
for future studies to advance the boundaries of
temporal graph modeling in predicting churn in
banking.
3.2.1 Data-Driven Retetion Strategies
The TGN model supports a hierarchical customer
retention policy with intervention priorities based on
churn risk scores and causal impact analysis. As
indicated in Figure 4, these policies are of three types:
proactive high-risk interventions, network-based
containment, and policy-responsive adjustments.
Each intervention targets a particular churn behavior,
and resources are allocated to clients who are most
likely to be benefited by targeted interaction.
Figure 4: Tiered Retention Strategy Based on TGN Insights. (Picture credit: Original)
For those with an estimated probability of churn over
70%, separate retention actions are undertaken to
deliberately constrain attrition. Personalized financial
incentives are one of the optimal practices in this
category. Customers who meet high-risk criteria are
given tailored offers, such as short-term suspensions
of increased temporary APR on deposits or low loan
refinancing rates for 60 days. Empirical results
indicate that 24% of customers availed themselves of
such types of offers, leading to a reduction in churn
by 18% (Huang et al., 2023).
Apart from financial incentives, direct contact
through relationship management is used for high-
net-worth clients who possess more than €100,000 in
assets. These customers are assigned individual
managers who receive weekly calls, with constant
follow-up and early intervention in the event of
problems. As seen from Table 5, the strategy is highly
effective, achieving a 33% churn reduction within 30
days.
Table 5: Proactive High-Risk Intervention Strategies.
Intervention Type
Churn
Reduction
Cost per
Client (€)
Personalized
Incentives
18%
150
Manager Outreach
33%
300
Automated
Notifications
9%
20
More time-consuming, perhaps, but managing
relationships directly has the greatest effect on
Temporal Graph Networks for Bank Customer Churn Prediction with Dynamic
329
retention, which is particularly valuable in dealing
with high-net-worth individuals and corporate
banking.".
Since social network effects significantly
influence churn behavior, interventions supporting
referral chains and community structures are
deployed to prevent cascade churn. Upon the
presence of a hub client, as identified by degree
centrality more than 50, indicating churn intention,
preemptive loyalty rewards are given to their direct
referrals. This tactic prevents the transmission of
churn within the network and, as indicated in a six-
month controlled test (Bauer and Dietmar, 2021),
successfully avoids 62% of potential referral-based
attrition.
Second, community-based incentives are given to
client groups that are highly internally connected as
reflected in terms of having a modularity score that
exceeds 0.8. In such a situation, group retention
bonuses such as fee waivers or provision of special
lending rates to the entire group in the event of 80%
group members maintaining active accounts prevent
community disintegration and bulk outflows.
For the highly causal policy-sensitive customers,
dynamic pricing buffers are used to counter
macroeconomic impacts. Customers with a causal
policy impact score of over 0.6, are offered temporary
rate locks during central bank adjustment periods,
offering fiscal stability in the event of changing
economic conditions (Huang et al., 2023). The
intervention has been found to reduce policy-induced
churn by 29% in Q3 2023, demonstrating the worth
of adaptive financial product forms that serve to
dampen external shocks.
3.2.2 Limitations of the Current Framework
Though it provides advantages, the TGN model poses
four challenges that must be overcome to improve its
performance in real banking application.
The first issue concerns temporal sparsity of data,
as a portion of clients is defined by low transaction
frequency. Specifically, 23% of the data consist of
clients with fewer than five transactions per month,
which corresponds to 38% higher prediction variance
for this category. Sparse interaction histories make it
difficult to establish strong temporal dependencies,
which lowers the accuracy of churn risk estimation.
The second limitation involves causal inference
assumptions inherent in the doubly robust estimator.
The doubly robust estimator assumes no unmeasured
confounders, an assumption that can be violated in
cases where external market trends influence multiple
clients simultaneously. For instance, cryptocurrency
price volatility can trigger huge banking withdrawals,
but these trends are beyond the TGN's transactional
causal analysis.
Another key challenge is computational overhead.
In order to update the graph in real time, there must
be an average of 12 milliseconds' processing per
transaction, although this increases to 1.9 seconds of
latency for rush hours, especially when transaction
rates are more than 5,000 per minute. Through graph
pruning and event-based updates, this is alleviated
somewhat, but computational scaling is still a
problem for larger data sets.
Finally, privacy and ethical issues arise as a result
of social network analysis methods embedded within
TGN. Inferring sensitive connections from co-
occurrences of transactions—e.g., inferring close
family relations based on joint payment patterns—
would be in danger of violating GDPR Article 9, i.e.,
prohibited automated inference of protected features
without an overt user confirmation (Zhang et al.,
2023).
3.2.3 Future research Directions
In response to these challenges, three research
priorities are set, each designed to improve the TGN
framework to better its robustness, interpretability,
and alignment with privacy issues.
First, sparse temporal graph learning techniques
can be explored to enhance predictive accuracy for
low-activity clients. Self-supervised pretraining with
masked graph autoencoders (Mengqi et al., 2021) has
the potential to allow the model to learn from
incomplete transaction histories, improving
performance in sparse data environments. In addition,
meta-learning approaches such as Model-Agnostic
Meta-Learning (MAML) can be applied for better
adaptation of model parameters to new clients,
reducing cold-start biases.
Second, causal temporal graph networks need to
be built to control for confounding variables for
policy-based churn prediction. This can be achieved
using instrumental variable analysis (Hunag et al.,
2023), which separates unobserved confounders in
customer behavior. Counterfactual policy testing can
also be used in order to identify various
macroeconomic conditions, so that banks can test
"what-if" and effectively modify their financial
planning.
Finally, advancement in privacy-preserving
deployment will be critical to meet regulatory
demands. Homomorphic encryption can be employed
to enable secure computation over encrypted
transaction graphs, eliminating raw data sharing
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(Zhang et al., 2023). In addition, on-device learning
can enable the native execution of lightweight TGN
models directly in mobile banking applications,
where user data is processed locally and not in the
cloud, where it can be exposed. Table 6 lists key
research goals and their possible influence.
Table 6: Roadmap for Future Research Directions.
Research Goal
Key Technique
Expected Impact
Sparse Data Handeling
Meta-Learning
25% AUC improvement for low-activity clients
Causal Robustness
Instrumental Variables
15% reduction in false policy attributiuon
Privacy Enhancement
Federated TGN + Encryption
40% lower GDPR compliance costs
3.2.4 Broader Implications for Financial
Services
Other than customer churn forecast, the TGN model
has more finance-related uses. In credit risk modeling,
temporal graphs are applied to model borrower-
lender relationships and predict future loan default
risks. In fraud detection, temporal analysis of patterns
of money flow is also employed to detect money
laundering scams and fraudulent transactions. Finally,
in personalized promotion, TGN-generated
behavioral embeddings can be used for predicting
real-time product affinities, enabling financial
institutions to deliver highly personalized promotions.
These broader applications open the potency of
temporal graph networks to revolutionize finance
analytics, offering more adaptive, data-driven models
for decision-making than churn management.
4 CONCLUSIONS
This paper introduces a TGN-driven method for
predicting bank customer churn, addressing the static
model limitation through embracing temporal
dynamics and causal inference steps. Experimental
results confirm that TGN outperforms baseline
models by a 23% reduction in false positives and
enables supported targeted intervention plans to
deliver a 14% boost in customer lifetime value.
The outcomes demonstrate the efficacy of
adaptive churn prediction models within banking,
driving actionable recommendations toward
personalized retention exercises such as dynamic fee
remission and referral stability rewards. Work in the
future can explore the application of federated
learning frameworks to facilitate between-bank
collaborative work without sharing data, even further
expanding on the application of temporal graph-based
analytics in finance. These findings highlight the
growing potential of temporal graph-based
approaches in financial analytics, paving the way for
more sophisticated, data-driven decision-making. As
financial institutions continue to evolve, integrating
adaptive predictive models will be key to enhancing
customer engagement and long-term business
sustainability.
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