Systems and Methods for Online Anomaly Detection

1 . An anomaly detection system for protecting an e-commerce platform throughout a user's journey, the system comprising:

a unified application interface configured to:

integrate with a plurality of user touchpoints;

receive attributes associated with each user touchpoint; and

return a decision of block, allow, or challenge for a user action;

a data ingestion pipeline configured to:

capture and process real-time data from each integrated touchpoint; and

at least one of transform or store the processed data in a anomaly data lake;

a graphical database configured to:

link data associated with user interactions, user devices, user accounts, and/or user transactions across the e-commerce platform; and

update the linked data with new data from the data ingestion pipeline;

a neural network associated with the graphical database, configured to:

analyze the linked data to generate predictions of user identities and/or detect patterns indicative of anomalous behavior; and

based on at least one of the predictions or detected patterns, provide real-time risk assessments for each user action;

a plurality of machine learning models configured to:

use data from the anomaly data lake and real-time predictions from the neural network to generate risk scores for the user accounts and/or the user transactions; and

an anomaly orchestration system configured to:

apply a set of dynamic rules to the real-time risk assessments from the neural network and the risk scores from the plurality of machine learning models, to implement a anomaly prevention action including at least one of a blocking action, an allowance, a challenge, for each user action.

2 . The anomaly detection system of claim 1 , wherein:

the plurality of machine learning models includes an account risk scoring model, a transaction risk scoring model, and a refund risk scoring model, and

the account risk scoring model is configured to:

generate an account risk score for each user account; and

receive and use transaction risk scores and refund risk scores as features in subsequent account risk score calculations;

the transaction risk scoring model is configured to:

receive the account risk score; and

generate a transaction risk score using the account risk score for each transaction; and

the refund risk scoring mode is configured to:

receive the account risk score and the transaction risk score; and

generate a refund risk score for each return request using the account risk score and the transaction risk score.

3 . The anomaly detection system of claim 1 , wherein the plurality of machine learning models further comprises:

an account creation risk model configured to assess a likelihood of anomalous account creation attempts; and

a behavior anomaly detection model configured to identify deviations from a user's established patterns of activity.

4 . The anomaly detection system of claim 1 , wherein the graphical database is further configured to:

establish connections between seemingly unrelated accounts based on shared attributes including IP addresses, device fingerprints, or behavioral patterns; and

provide a visual representation of the connections for manual review in complex anomaly cases.

5 . The anomaly detection system of claim 1 , wherein the system is configured to detect and prevent anomalous activities at the earliest possible stage of the user journey, including account creation, by:

analyzing the attributes received by the unified application interface during account creation, including IP address, Device ID, and user action patterns;

using the neural network to compare the attributes against known anomalous patterns stored in the graphical database;

using the account risk scoring model to generate an initial risk score for the new account; and

triggering the anomaly orchestration system to block or challenge the account creation if the risk score exceeds a predetermined threshold.

6 . The anomaly detection system of claim 1 , wherein the anomaly orchestration system is further configured to:

dynamically adjust a level of authentication required based on calculated risk scores and specific action being performed; and

implement a gradual step-up in security measures for actions deemed increasingly risky, to balance user experience with anomaly prevention.

7 . The anomaly detection system of claim 1 , wherein the anomaly orchestration system is further configured to:

route high-risk actions determined based on the risk assessments and the scores to additional verification services when necessary.

8 . The anomaly detection system of claim 1 , wherein the unified application interface is further configured to:

integrate with additional touchpoints including password reset requests, payment method changes, and shipping address updates; and

assign different risk thresholds for each touchpoint based on a potential impact of anomalous activity at that stage of the user journey.

9 . The anomaly detection system of claim 1 , wherein the data ingestion pipeline is further configured to:

process and correlate data from multiple touchpoints to create a user behavior profile; and

identify and flag sudden changes in user behavior patterns for immediate risk assessment, based on the user behavior profile.

10 . The anomaly detection system of claim 1 , wherein the system is further configured to:

generate and maintain a risk profile for each user that evolves throughout their journey on the e-commerce platform; and

use the risk profile to inform decisions at each subsequent touchpoint, providing a seamless and adaptive anomaly prevention experience.

11 . The anomaly detection system of claim 1 , wherein the system is further configured to:

detect and prevent account takeover attempts by analyzing login patterns, device changes, and account activity; and

implement verification steps for high-risk account changes.

12 . The anomaly detection system of claim 1 , wherein the neural network is further configured to:

use unsupervised learning techniques to identify new, previously unknown patterns of anomalous behavior; and

adjust anomaly prediction models in real-time based on outcomes of challenged or blocked actions.

13 . The anomaly detection system of claim 1 , further comprising a rules engine configured to:

allow for creation and modification of anomaly detection rules without requiring system downtime; and

automatically suggest new rules based on patterns identified by the neural network and machine learning models.

14 . The anomaly detection system of claim 1 , wherein the anomaly orchestration system is further configured to:

dynamically route transactions or actions to different anomaly prevention services based on specific characteristics of the action and current risk assessment; and

aggregate and reconcile decisions from multiple anomaly prevention services when necessary.

15 . The anomaly detection system of claim 1 , wherein the system continuously updates risk assessments based on ongoing user interactions by:

capturing and processing real-time data from each user interaction through the data ingestion pipeline;

updating the graphical database with new interaction data to refine the user's behavioral profile;

re-analyzing the updated data using the neural network to detect any new patterns indicative of anomalous behavior;

recalculating risk scores using the machine learning models after each significant user action or at regular intervals; and

adjusting the anomaly prevention actions taken by the anomaly orchestration system based on the recalculated risk scores.

16 . The anomaly detection system of claim 1 , further comprising dynamically adapting to new services or verticals added to the e-commerce platform by:

configuring the unified application interface to automatically integrate with new touchpoints introduced by added services or verticals;

updating the data ingestion pipeline to capture and process data specific to the added services or verticals;

extending schema of the graphical database to accommodate new types of user interactions and relationships relevant to the added services;

retraining the neural network and machine learning models to recognize patterns and assess risks within a context of the new services; and

updating a rule set of the anomaly orchestration system to include anomaly prevention strategies tailored to specific risks associated with the new services or verticals.

17 . The anomaly detection system of claim 1 , wherein the system provides reduced friction for legitimate users while maintaining heightened security against potential bad actors by:

utilizing the neural network and machine learning models to establish a baseline of normal behavior for each user;

comparing each user action against a personalized baseline to identify deviations;

implementing a tiered authentication system within the anomaly orchestration system, where a level of authentication required is dynamically adjusted based on a current risk assessment;

allowing low-risk actions to proceed with minimal intervention, while applying additional security measures only to high-risk actions; and

continuously refining a risk profile of the user to more accurately distinguish between legitimate and potentially anomalous activities.

18 . The anomaly detection system of claim 1 , further comprising a feature store configured to:

maintain a repository of pre-computed features derived from historical data across all touchpoints; and

provide the features in real-time to the machine learning models and neural network for faster and more accurate risk assessments.

19 . The anomaly detection system of claim 1 , further comprising a reporting and analytics module configured to:

provide real-time dashboards showing anomaly prevention performance across all touchpoints; and

generate detailed reports on emerging anomaly patterns and effectiveness of various anomaly prevention measures.

20 . The anomaly detection system of claim 1 , wherein the system is further configured to:

integrate with third-party data sources to enrich user and transaction data for more accurate risk assessments; and

maintain compliance with data privacy regulations by implementing data minimization and purpose limitation principles.

21 . The anomaly detection system of claim 1 , further comprising a simulation environment configured to:

allow testing of new anomaly detection strategies using historical data before deployment; and

assess a potential impact of rule changes on false positive and false negative rates.