Autonomous auditing of digital asset reserves using a multi-model architecture

1 . A computer-implemented method for continuously verifying reserve backing of a digital token on a blockchain, the method comprising:

executing, by one or more processors of a computing system, at a predefined interval of less than one hour, a parallel analysis pipeline implemented by the computing system, the parallel analysis pipeline querying, via network interfaces of the computing system, a plurality of external data sources comprising custodial bank application programming interfaces (APIs), securities pricing feeds, and blockchain networks, and ingesting, in response to the querying, current reserve data and token circulation data from the plurality of external data sources into memory of the computing system;

normalizing, by the one or more processors, the ingested data into model-ready feature sets stored in the memory;

processing, by the one or more processors, the model-ready feature sets concurrently through an ensemble of four distinct artificial intelligence models operating in parallel to provide model redundancy and mitigate single points of failure, the ensemble comprising:

(i) a supervised learning model comprising a neural network that has been trained on labeled training data comprising historical examples of fraudulent and legitimate reserve patterns and that computes a fraud probability based on labeled transaction features and known fraud patterns;

(ii) an unsupervised anomaly detection model comprising multiple anomaly detection algorithms that have learned a baseline of normal multi-variate behavior and that compute an anomaly score by comparing observed multi-variate behavior to the learned baseline;

(iii) a time-series analysis model comprising at least one of an ARIMA model or an LSTM neural network that has been trained on historical time-series data and that computes trend health and forecasts reserve ratio trajectories across multiple temporal horizons; and

(iv) a deterministic rule-based compliance engine that evaluates non-probabilistic regulatory and policy constraints and outputs constraint evaluations with hard pass/fail determinations;

executing, by the one or more processors, each of the four distinct artificial intelligence models to actively compute respective model outputs from the model-ready feature sets, the supervised learning model outputting a fraud probability and a confidence score, the unsupervised anomaly detection model outputting an anomaly score and an algorithm agreement metric, the time-series analysis model outputting a deviation score and trend health metrics, and the rule-based compliance engine outputting a compliance status and any violated rules;

aggregating, by the one or more processors executing a consensus engine implemented by the computing system, the respective model outputs of the supervised learning model, the unsupervised anomaly detection model, and the time-series analysis model by applying weighted scoring based on historical accuracy of each model to compute a weighted average verification score;

determining, by the consensus engine, an audit status based on the weighted average verification score and multi-signature threshold validation requiring agreement among at least three of the four distinct artificial intelligence models, while granting veto priority to the rule-based compliance engine such that any hard violation identified by the rule-based compliance engine overrides the weighted average verification score;

generating, by the one or more processors, a cryptographic attestation data structure comprising the audit status, reserve and token data, and respective model outputs;

transmitting, by the one or more processors via the network interfaces, the cryptographic attestation to a first blockchain network and recording the cryptographic attestation to the first blockchain network with near-real-time finality at each predefined interval; and

periodically batching, by the one or more processors, a plurality of the cryptographic attestations, computing a Merkle root of the batch, and transmitting and anchoring the Merkle root onto a second blockchain network distinct from the first blockchain network to provide immutable checkpointing of audit results, the dual blockchain settlement providing high-frequency operational verification on the first blockchain network combined with periodic security anchoring on the second blockchain network.

2 . The computer-implemented method of claim 1 , wherein the neural network of the supervised learning model is trained using backpropagation with gradient descent optimization on the labeled training data comprising historical examples of fraudulent and legitimate reserve patterns, the labeled training data comprising historical fraud cases exhibiting patterns of reserve manipulation and legitimate operational patterns from compliant stablecoin operators.

3 . The computer-implemented method of claim 1 , wherein the supervised learning model comprises an ensemble of a gradient boosting classifier and a deep neural network, each trained on the labeled training data and generate outputs which characterize fraud probability.

4 . The computer-implemented method of claim 1 , wherein the multiple anomaly detection algorithms of the unsupervised anomaly detection model comprise an isolation forest algorithm, a local outlier factor (LOF) algorithm, a one-class support vector machine (SVM), and an autoencoder neural network, each trained to learn the baseline of normal multi-variate behavior and to produce respective anomaly scores that are aggregated to compute the anomaly score and the algorithm agreement metric.

5 . The computer-implemented method of claim 1 , wherein the time-series analysis model comprises a plurality of forecasting techniques including at least one of an AutoRegressive Integrated Moving Average (ARIMA) model trained to predict future values based on historical patterns, a Long Short-Term Memory (LSTM) neural network trained to learn temporal dependencies in sequential data, a seasonal decomposition technique that separates trend, seasonal, and residual components, or a change point detection technique that identifies moments where statistical properties of the time series change.

6 . The computer-implemented method of claim 1 , wherein the time-series analysis model is trained to forecast reserve ratio trajectories across micro-pattern, daily, weekly, and monthly temporal horizons and to output deviation scores quantifying distances between observed values and expected predictions.

7 . The computer-implemented method of claim 1 , wherein the predefined interval is less than 10 minutes, and the parallel analysis pipeline executes a continuous audit cycle at each predefined interval.

8 . The computer-implemented method of claim 7 , wherein the predefined interval is less than or equal to about one minute, and each audit cycle completes within less than or equal to about one minute including data ingestion via the network interfaces, model execution by the one or more processors, consensus determination by the consensus engine, and attestation recording to the first blockchain network.

9 . The computer-implemented method of claim 1 , wherein the Merkle root of the batched plurality of cryptographic attestations is anchored onto the second blockchain network daily to provide periodic security anchoring.

10 . The computer-implemented method of claim 1 , further comprising:

in response to the audit status indicating a failure condition based on the aggregated outputs of the four distinct artificial intelligence models, automatically transmitting, by the one or more processors, a halt instruction to a smart contract on the blockchain to reject execution of at least one pending token transaction.

11 . The computer-implemented method of claim 1 , wherein the consensus engine determines an audit status selected from PASS, CAUTION, ALERT, or HALT based on the weighted average verification score and the multi-signature threshold validation.

12 . The computer-implemented method of claim 1 , wherein querying the plurality of external data sources comprises querying custodial bank APIs to retrieve real-time balance information for reserve accounts, fetching current market valuations from securities pricing feeds for reserve assets, and querying blockchain networks to determine a total supply of tokens currently in circulation.

13 . The computer-implemented method of claim 1 , wherein the non-probabilistic regulatory and policy constraints evaluated by the rule-based compliance engine comprise at least one of reserve ratio thresholds, reserve composition requirements, transaction limits, custody diversification requirements across at least three independent custodians, access controls, or audit requirements.

14 . The computer-implemented method of claim 1 , wherein the plurality of external data sources for reserve data comprises at least three independent custodians, thereby enabling cross-verification of reserve balances across custodial venues.

15 . A system for continuously auditing reserve backing of a blockchain-based digital token, the system comprising:

a data collection subsystem comprising network interface hardware that periodically queries a plurality of external data sources comprising custodial bank application programming interfaces (APIs), securities pricing feeds, and blockchain networks, and that ingests current reserve data and token circulation data from the plurality of external data sources;

memory hardware that stores the ingested reserve data and token circulation data as normalized, model-ready feature sets;

one or more processors coupled to the memory hardware and executing an artificial intelligence ensemble subsystem comprising four distinct, independently operating models, the four distinct models comprising:

(i) a supervised learning model comprising a neural network that has been trained using backpropagation on labeled training data comprising historical fraud cases and legitimate operational patterns, the supervised learning model computing a fraud probability and a confidence score based on labeled transaction features and known fraud patterns;

(ii) an unsupervised anomaly detection model comprising an ensemble of an isolation forest algorithm, a local outlier factor (LOF) algorithm, a one-class support vector machine (SVM), and an autoencoder neural network, each trained to learn a baseline of normal multi-variate behavior and to compute an anomaly score and an algorithm agreement metric;

(iii) a time-series analysis model comprising at least one of an AutoRegressive Integrated Moving Average (ARIMA) model or a Long Short-Term Memory (LSTM) neural network, each trained on historical time-series data to compute trend health and to forecast reserve ratio trajectories across micro-pattern, daily, weekly, and monthly temporal horizons; and

(iv) a deterministic rule-based compliance engine that evaluates non-probabilistic regulatory and policy constraints comprising reserve ratio thresholds, asset eligibility requirements, operational policy requirements, and regulatory compliance requirements, and that outputs constraint evaluations with hard pass/fail determinations;

a consensus engine implemented by the one or more processors, the consensus engine:

receiving model-specific outputs from each of the four distinct models, the model-specific outputs comprising a fraud probability and confidence score from the supervised learning model, an anomaly score and algorithm agreement metric from the unsupervised anomaly detection model, a deviation score and trend health metrics from the time-series analysis model, and a compliance status with any violated rules from the rule-based compliance engine;

applying weighted scoring by assigning a weight to each model based on historical accuracy and computing a weighted average verification score;

evaluating multi-signature thresholds requiring agreement among at least three of the four distinct models to determine an audit status selected from PASS, CAUTION, ALERT, or HALT, while granting veto priority to the rule-based compliance engine such that any hard violation identified by the rule-based compliance engine overrides the weighted average verification score; and

in response to the audit status indicating ALERT or HALT, automatically transmitting a halt instruction to a smart contract on the blockchain to reject execution of pending token transactions; and

a dual blockchain settlement subsystem that:

generates a cryptographic attestation data structure comprising the audit status, reserve and token data, and respective model outputs;

transmits the cryptographic attestation to a first blockchain network and records the cryptographic attestation to the first blockchain network with near-real-time finality at each predefined interval; and

periodically batches a plurality of the cryptographic attestations, computes a Merkle root of the batch, and anchors the Merkle root onto a second blockchain network distinct from the first blockchain network to provide immutable checkpointing of audit results.

16 . A system for continuously verifying reserve backing of a digital token on a blockchain, the system comprising:

one or more processors;

memory coupled to the one or more processors;

network interfaces coupled to the one or more processors; and

a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, cause the system to:

execute, at a predefined interval of less than one hour, a parallel analysis pipeline implemented by the system, the parallel analysis pipeline querying, via the network interfaces, a plurality of external data sources comprising custodial bank application programming interfaces (APIs), securities pricing feeds, and blockchain networks, and ingesting, in response to the querying, current reserve data and token circulation data from the plurality of external data sources into the memory;

normalize the ingested data into model-ready feature sets stored in the memory; process the model-ready feature sets concurrently through an ensemble of four distinct artificial intelligence models operating in parallel to provide model redundancy and mitigate single points of failure, the ensemble comprising:

(i) a supervised learning model comprising a neural network that has been trained on labeled training data comprising historical examples of fraudulent and legitimate reserve patterns and that computes a fraud probability based on labeled transaction features and known fraud patterns;

(ii) an unsupervised anomaly detection model comprising multiple anomaly detection algorithms that have learned a baseline of normal multi-variate behavior and that compute an anomaly score by comparing observed multi-variate behavior to the learned baseline;

(iii) a time-series analysis model comprising at least one of an ARIMA model or an LSTM neural network that has been trained on historical time-series data and that computes trend health and forecasts reserve ratio trajectories across multiple temporal horizons; and

(iv) a deterministic rule-based compliance engine that evaluates non-probabilistic regulatory and policy constraints and outputs constraint evaluations with hard pass/fail determinations;

execute each of the four distinct artificial intelligence models to actively compute respective model outputs from the model-ready feature sets, the supervised learning model outputting a fraud probability and a confidence score, the unsupervised anomaly detection model outputting an anomaly score and an algorithm agreement metric, the rule-based compliance engine outputting a compliance status and any violated rules;

aggregate, via a consensus engine implemented by the system, the respective model outputs of the supervised learning model, the unsupervised anomaly detection model, and the time-series analysis model by applying weighted scoring based on historical accuracy of each model to compute a weighted average verification score;

determine, by the consensus engine, an audit status based on the weighted average verification score and multi-signature threshold validation requiring agreement among at least three of the four distinct artificial intelligence models, while granting veto priority to the rule-based compliance engine such that any hard violation identified by the rule-based compliance engine overrides the weighted average verification score;

generate a cryptographic attestation data structure comprising the audit status, reserve and token data, and respective model outputs;

transmit, via the network interfaces, the cryptographic attestation to a first blockchain network and record the cryptographic attestation to the first blockchain network with near-real-time finality at each predefined interval; and

periodically batch a plurality of the cryptographic attestations, compute a Merkle root of the batch, and transmit and anchor the Merkle root onto a second blockchain network distinct from the first blockchain network to provide immutable checkpointing of audit results, the dual blockchain settlement providing high-frequency operational verification on the first blockchain network combined with periodic security anchoring on the second blockchain network.

17 . The system of claim 16 , wherein the multiple anomaly detection algorithms of the unsupervised anomaly detection model comprise an isolation forest algorithm, a local outlier factor (LOF) algorithm, a one-class support vector machine (SVM), and an autoencoder neural network, each trained to learn the baseline of normal multi-variate behavior and to produce respective anomaly scores that are aggregated to compute the anomaly score and the algorithm agreement metric.

18 . The system of claim 16 , wherein the predefined interval is less than 10 minutes, and the parallel analysis pipeline executes a continuous audit cycle at each predefined interval.

19 . The system of claim 16 , wherein the consensus engine determines an audit status selected from PASS, CAUTION, ALERT, or HALT based on the weighted average verification score and the multi-signature threshold validation.

20 . The system of claim 16 , wherein the supervised learning model architecture comprises an ensemble of a gradient boosting classifier and a deep neural network, the supervised learning model configured to compute the fraud probability based on labeled transaction features and known fraud patterns.