Predicting DeFi Survival: A Race Against Time

Author: Denis Avetisyan

A new benchmark challenge reveals the critical importance of understanding user behavior and temporal dynamics in forecasting success within decentralized finance.

This paper introduces the FinSurvival 2025 challenge and demonstrates that effective time-to-event modeling, accounting for censoring and non-stationarity, is essential for accurate predictions in DeFi.

Despite the increasing reliance on longitudinal data for web analytics, benchmarking remains a significant challenge within the rapidly evolving landscape of decentralized finance (DeFi). This paper, ‘Benchmarking Temporal Web3 Intelligence: Lessons from the FinSurvival 2025 Challenge’, introduces a novel benchmark built around 21.8 million transaction records from the Aave v3 protocol, operationalizing 16 survival prediction tasks to model user behavior. Results demonstrate that domain-aware temporal feature engineering consistently outperformed generic modeling approaches in predicting time-to-event outcomes. Could Web3 systems, with their immutable and time-stamped data, offer a uniquely robust sandbox for developing and validating solutions to broader temporal challenges inherent in web analytics?

The Illusion of Static Markets

Conventional financial models frequently treat time as a static element, a simplification that obscures critical dynamics within markets. This approach often assumes that past relationships between financial variables will remain constant, failing to account for the inherent evolution of risk and opportunity. Consequently, assessments of volatility, creditworthiness, and potential returns can be significantly skewed, leading to flawed investment strategies and inadequate risk management. The neglect of temporal dependencies means that crucial leading indicators – subtle shifts in market behavior preceding major events – are overlooked, and the potential for predictive analytics remains largely untapped. By failing to fully integrate the dimension of time, traditional methods often miss fleeting opportunities and underestimate the likelihood of unforeseen crises, ultimately hindering optimal financial decision-making.

The advent of Decentralized Finance has unlocked a previously inaccessible trove of high-resolution financial data in the form of blockchain transactions. Each transaction is indelibly time-stamped, creating a continuous record of economic activity that extends far beyond traditional financial datasets. This wealth of temporal information offers unprecedented opportunities for analyzing market dynamics, identifying emerging trends, and predicting future events with greater precision. Unlike aggregated reports, blockchain data allows for the granular examination of when transactions occur, not just that they occurred, enabling researchers to model complex dependencies and uncover subtle patterns in real-time. This detailed chronology is proving invaluable for developing innovative financial instruments and risk management strategies, potentially reshaping the landscape of modern finance through data-driven insights.

Financial datasets derived from sources like blockchain transactions exhibit a fundamental characteristic known as non-stationarity. This means that the statistical properties of the data – its mean, variance, and correlations – are not constant over time, unlike the assumptions underpinning many traditional analytical techniques. Conventional methods, designed for stable datasets, struggle to accurately capture evolving relationships and predict future behavior when applied to these dynamic systems. For example, a trading strategy that proves profitable during one period may fail as market conditions shift, rendering historical data less reliable for forecasting. Consequently, advanced methodologies are required to account for these temporal shifts and extract meaningful insights from non-stationary financial data, demanding a move beyond static analysis toward dynamic modeling approaches.

Accurately forecasting financial events necessitates a shift beyond static analysis towards techniques that explicitly account for the passage of time. Time-to-Event Modeling, traditionally employed in medical research and reliability engineering, is now being adapted to predict the timing of financial occurrences – such as loan defaults, market crashes, or the liquidation of decentralized finance positions. These models don’t simply predict if an event will happen, but when, considering the duration until its occurrence as a crucial variable. Sophisticated implementations leverage survival analysis, recurrent neural networks, and other advanced statistical methods to handle the non-stationary nature of financial data, where patterns are constantly evolving. This pushes the boundaries of financial analytics, demanding computational power and innovative algorithms to extract meaningful signals from the complex interplay of time and market forces, ultimately offering a more nuanced and proactive approach to risk management and investment strategy.

Survival Analysis: Beyond the Average

Survival analysis, also known as time-to-event analysis, is a branch of statistics focused on analyzing the expected duration of time until one or more events occur. Unlike traditional statistical methods that often focus on averages, survival analysis explicitly accounts for the time dimension and the possibility of incomplete observations. This framework is applicable across diverse fields, including medical research – analyzing patient survival times – and engineering – assessing the reliability of components. Core to survival analysis are concepts like the survival function, $S(t)$ , which represents the probability of an individual surviving beyond a specific time $t$ , and the hazard function, $h(t)$ , which describes the instantaneous risk of an event occurring at time $t$ , given survival up to that point. These functions allow researchers to model and compare event durations, identify influential factors, and make predictions about future events.

Censoring in time-to-event modeling arises when, for some study participants, the event of interest is not observed during the observation period. This can occur due to several reasons, including participants being lost to follow-up, the study ending before the event occurs, or participants not experiencing the event at all. Censoring is not the same as a missing data point; rather, it indicates that a subject contributed some time to the study but did not experience the event. There are three primary types of censoring: right censoring (most common, where the event hasn’t occurred by the end of observation), left censoring (the event occurred before observation began), and interval censoring (the event occurred within a specified interval). Accurately accounting for censoring is crucial, as simply excluding censored observations would introduce bias, potentially leading to an overestimation of event rates and inaccurate hazard function estimates.

The Cox Proportional Hazards model estimates the hazard rate – the instantaneous probability of an event occurring at a specific time, given survival up to that point – and assesses how explanatory variables influence this rate. Unlike parametric survival models, the Cox model does not require assumptions about the underlying distribution of event times. It models the hazard ratio, which represents the relative effect of a covariate on the hazard, through the equation $h(t,x) = h_0(t) \cdot exp(\beta x)$ , where $h(t,x)$ is the hazard at time t for individual with covariate values x, $h_0(t)$ is the baseline hazard, and β represents the effect of the covariate. The model utilizes partial likelihood estimation to determine the β coefficients, allowing for the identification of significant predictors of event timing without needing to directly estimate the baseline hazard function $h_0(t)$ .

The integration of machine learning algorithms, particularly gradient boosting methods like XGBoost, with traditional survival analysis techniques offers substantial improvements in predictive accuracy and the capacity to model complex relationships. While survival analysis provides a robust framework for handling censored data and estimating hazard functions, machine learning models excel at identifying non-linear interactions and high-order effects often present in complex datasets. XGBoost, known for its regularization and handling of missing values, can be incorporated into survival models by predicting risk scores used to estimate hazard ratios, or by directly predicting survival probabilities. This combined approach often outperforms traditional parametric or semi-parametric survival models, especially when dealing with high-dimensional data or datasets exhibiting complex patterns that are difficult to capture with simpler models. Furthermore, machine learning allows for variable selection and feature importance ranking, aiding in the identification of key predictors of event timing.

Feature Engineering: Exposing Temporal Signals

Effective feature engineering is a critical preprocessing step for time-to-event models, as these models operate on structured data and cannot directly interpret raw transactional records. Raw data, typically consisting of timestamps and transaction details, must be transformed into quantifiable features representing user behavior, event frequency, or recency. This involves calculating metrics such as the time since the last transaction, the total value of transactions within a defined period, or the frequency of specific event types. The quality of these engineered features directly impacts model performance; well-constructed features provide the model with the necessary information to discern patterns and accurately predict time-to-event outcomes, while poorly designed features can obscure relevant signals and lead to inaccurate predictions. Feature engineering often requires domain expertise to identify potentially predictive variables and to select appropriate aggregation and transformation techniques.

Hierarchical feature engineering involves constructing features representing data at varying granularities to expose temporal patterns not evident in single-level features. This methodology moves beyond simple time-based aggregations by creating features that summarize data at multiple levels – for example, individual transaction features, session-level features derived from sequences of transactions, and user-level features summarizing long-term behavior. By representing data in this multi-resolution format, the approach facilitates the identification of temporal dependencies and interactions that are obscured when only raw or single-aggregate features are utilized, thereby improving the predictive power of time-to-event models.

Hierarchical feature engineering enhances the analysis of sequential data by explicitly modeling interactions and dependencies between events. Traditional feature engineering often treats each data point independently, potentially overlooking relationships crucial for time-to-event prediction. This approach constructs features that represent combinations of prior events and their temporal relationships – for example, the time elapsed since a specific sequence of actions occurred, or the frequency of certain event pairings. By capturing these interdependencies, the resulting features provide models with richer information about the underlying processes governing event timing, improving predictive accuracy and interpretability in scenarios where event order and context are significant.

Engineered features significantly improve the predictive capability of time-to-event models by providing more informative inputs than raw transactional data. These features allow models to discern subtle patterns indicative of varying event durations, enabling more accurate predictions of when events will occur. Furthermore, feature importance analysis, conducted on models utilizing these engineered inputs, can pinpoint specific variables and interactions that strongly correlate with temporal behavior. This identification of key drivers facilitates a deeper understanding of the underlying processes governing event timing and allows for targeted interventions or further investigation into influential factors.

FinSurvival: A Testbed for Temporal Intelligence

The FinSurvival 2025 Challenge serves as a crucial proving ground for advancements in Time-to-Event Modeling, pushing the boundaries of predictive analytics within the rapidly evolving decentralized finance (DeFi) landscape. By focusing on real-world data sourced from the Aave v3 protocol, the challenge compels researchers to move beyond theoretical benchmarks and address the unique complexities of on-chain events – such as liquidation or deposit activity. This concentrated effort fosters innovation in methodologies capable of accurately predicting when these events will occur, rather than simply if, offering tangible improvements to risk management and strategic decision-making within DeFi ecosystems. The platform’s emphasis on practical application distinguishes it as a vital component in translating academic research into functional tools for a dynamic financial environment.

The FinSurvival challenge centers around predictive modeling within the dynamic landscape of Aave v3, a leading decentralized finance (DeFi) protocol. Participants are tasked with forecasting the timing of critical events – such as loan liquidations, protocol upgrades, or changes in user behavior – using Aave’s publicly available data streams. This focus on time-to-event prediction, also known as survival analysis, allows researchers to move beyond simple classification and delve into the complexities of predicting when specific actions will occur within the DeFi ecosystem. Successfully anticipating these events is crucial for risk management, protocol optimization, and ultimately, maintaining the stability and efficiency of the Aave protocol and the broader DeFi space.

The FinSurvival 2025 Challenge streamlined the development and assessment of time-to-event prediction models through its utilization of the Codabench platform. This centralized resource provided participants with consistent access to a comprehensive dataset derived from Aave v3, simplifying the data ingestion process and ensuring a level playing field. Models were submitted via Codabench, where automated evaluation pipelines calculated performance based on the Concordance Index (C-index), a metric quantifying a model’s ability to correctly rank the time-to-event for different instances. The C-index, ranging from 0 to 1, allowed for objective comparison of diverse approaches, ultimately identifying solutions like Balancehero India, which demonstrated exceptional predictive power with a C-index of 0.914, and highlighting the effectiveness of the platform in fostering robust and reproducible research in the rapidly evolving field of decentralized finance.

The FinSurvival 2025 Challenge culminated in a striking demonstration of predictive capability, with the Balancehero India solution achieving a Concordance Index (C-index) of 0.914 – a figure that notably surpassed the performance of end-to-end deep survival models. This success wasn’t simply a matter of algorithmic complexity; it underscored the critical importance of domain-informed feature engineering within decentralized finance. While strong performances were seen across the board, with the second and third place solutions achieving C-indices of 0.849 and 0.847 respectively, the winning approach highlighted how a deep understanding of the Aave v3 ecosystem, coupled with carefully crafted features, could substantially enhance the accuracy of time-to-event predictions – offering valuable insights into the dynamics of this complex financial landscape.

Beyond Finance: The Long View of Time

Initially developed to assess risk and predict outcomes in financial markets, the methodologies of Time-to-Event Modeling and sophisticated feature engineering are proving remarkably adaptable to diverse fields. In healthcare, these techniques enable researchers to analyze patient data, predicting the likelihood of disease progression or response to treatment based on longitudinal health records. Similarly, in web analytics, these principles allow for a deeper understanding of user behavior, moving beyond simple click-through rates to forecast the timing of conversions, identify at-risk users likely to abandon a platform, or even predict emerging trends based on patterns of online activity. The core strength lies in the ability to model not just whether an event occurs, but when, providing a more nuanced and predictive capability than traditional static analyses.

The power of discerning change over time hinges on the collection of longitudinal data – information gathered from the same subjects or systems repeatedly across substantial periods. This approach transcends simple snapshots, allowing researchers to map individual trajectories and identify broader temporal trends that would otherwise remain hidden. By tracking variables across extended durations, it becomes possible to model how behaviors evolve, predict future outcomes with greater accuracy, and understand the underlying mechanisms driving observed changes. Such data is increasingly vital in fields ranging from public health, where tracking patient conditions over years informs preventative care, to behavioral science, where understanding shifts in attitudes requires monitoring individuals over time, and even in digital spaces, where analyzing user interactions reveals patterns of engagement and anticipates future needs.

Web archives, such as the Internet Archive’s Wayback Machine, represent a largely untapped repository of longitudinal data, offering researchers a unique window into the evolution of the web and online human behavior. These archives systematically capture snapshots of websites over time, creating a historical record of content, design, and functionality. By analyzing this data, researchers can trace the emergence and disappearance of online trends, map the spread of information, and identify patterns in user engagement with websites across years or even decades. This approach transcends simple website change detection; it enables the reconstruction of past online experiences and offers valuable insights into how online spaces have shaped – and been shaped by – societal shifts and user preferences. The scale and scope of web archives provide a robust foundation for developing predictive models of online behavior and understanding the long-term dynamics of the digital world.

Temporal Web Analytics represents a paradigm shift in understanding online behavior, moving beyond static snapshots to embrace the dynamic evolution of user engagement. By integrating longitudinal data – gleaned from sources like web archives and continuously updated website metrics – with time-to-event modeling and advanced feature engineering, analysts can now discern patterns previously obscured by the sheer volume of web data. This approach doesn’t simply identify what users do, but when and how their behavior changes over time, allowing for predictive modeling of future trends. Consequently, businesses can anticipate shifts in user preferences, personalize content with greater accuracy, and proactively optimize website design and marketing strategies to maximize engagement and achieve sustained growth. The implications extend beyond commercial applications, offering valuable insights into information diffusion, societal trends, and the long-term evolution of the digital landscape.

The pursuit of predictive accuracy in decentralized finance, as demonstrated by the FinSurvival 2025 challenge, feels remarkably familiar. This insistence on modeling temporal dynamics and user history – capturing the when and how of user behavior – simply repackages problems engineers have been battling for decades. It’s a fresh coat of paint on the old problem of state management. David Hilbert once said, “We must be able to demand of every finite relation that it be capable of being expressed by a finite expression.” One suspects he’d find the complexity of capturing non-stationarity in DeFi rather amusing; the ‘finite expression’ keeps growing exponentially as production relentlessly finds new ways to invalidate assumptions. Everything new is just the old thing with worse docs.

What’s Next?

The FinSurvival 2025 challenge, and work like it, will inevitably reveal the comforting illusion of stationarity. Models built on historical DeFi interactions are, at best, capturing a fleeting moment. The real problem isn’t merely predicting when a user exits a protocol, but acknowledging that the very definition of ‘exit’-and the landscape of available protocols-is constantly shifting. Feature engineering, particularly the attempt to encode ‘user history’, feels less like scientific insight and more like a frantic effort to document a disappearing world.

The benchmarking exercise is a useful, if temporary, victory. Someone will inevitably find the adversarial example that collapses these models – a novel protocol design, a coordinated exploit, or simply a change in market sentiment. The more interesting question isn’t achieving incremental gains in prediction accuracy, but developing frameworks that gracefully degrade as the underlying assumptions crumble.

Ultimately, this work highlights a fundamental tension. The pursuit of quantitative rigor in decentralized finance runs headfirst into the chaotic reality of on-chain behavior. The models aren’t wrong; they’re simply a snapshot of a system designed to resist predictability. Perhaps the most valuable outcome of these challenges will be a renewed appreciation for the art of prolonging suffering – extending the lifespan of useful heuristics long after they’ve ceased to be theoretically sound.

Original article: https://arxiv.org/pdf/2602.23159.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/