Identifying rare deviations from huge data sets

One of the biggest challenges in anomaly detection is detecting a deviation in normal patterns from an enormous set of data that is growing in size every second. This challenge is further complicated because of the frequency at which a rare deviation occurs. There is no set pattern for when it will happen and this skews the data to a great extent. Our AI-based model for anomaly detection is based on a robust framework that identifies the best model fit for specific segments to develop a segment-specific detection engine and therefore is very effective in monitoring, identifying, and alerting for anomalies that would usually slip past the usual monitoring models.

SERVICE OFFERINGS

Detect every anomaly to ensure optimal performance

Apexon’s Anomaly Detection offering is one of a kind in that it is not based on a single or dual model but provides the flexibility to add any number of models. Businesses can add unlimited rules on top of the models to accurately capture outliers with business relevance.

Micro Segment classifier framework

In an industry first, Apexon’s anomaly detection capabilities allow businesses to develop multiple models, evaluate the same for multiple segments and select the best combination and also incorporate tribal knowledge by allowing the framework to incorporate business-specific rules. Organizations need no longer restrict themselves to just one model; we give them the option to work with as many models as they want. We help you with:

Supervised anomaly detection using imbalanced supervised data
Rule-based anomaly detection that segregates edge cases based on business rules
Time series anomaly detection includes a time component along with the data.
Unsupervised anomaly detection where there is no target
Data connectivity from flat files and databases
Up-sampling or down-sampling the dataset for a more balanced class

EdgeDetect – Apexon Anomaly detection Engine

Our EdgeDetect Platform detects unusual events that are suspicious due to huge deviations from regular patterns. The ML-powered platform allows businesses to track critical KPIs and ensure timely data-driven intervention. Here is what it offers:

Faster Detection – This allows for much faster development of anomaly detection with no need for any coding. Zero Code required to build the anomaly detection framework, custom made for the business
Identifying best models – Provides hyperparameter optimization that helps identify the best possible ML models
Continuous Model Surveillance – Built-in alerting system that monitors model decay and health status

CASE STUDIES

Apexon in Action

Read more about how we help businesses identify critical anomalies and prevent issues from snowballing.

Asset Management Company Analyzes Churn to Retain 50% of Passive Customers

Discover how we helped an asset management company analyzes its high churn rate to identify opportunities for customer retention and improve CSAT.

Claim Error Prediction for a Healthcare Payer Through a 360° View of the Customer

Find out how we helped a major healthcare payer leverage our proprietary solution ApexClaims to create focused samples for audit to improve financial accuracy by 99.7% and generate savings worth $1.5M per annum as overpayment avoidance.

Let’s head to the apex

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Anomaly Detection

Identifying rare deviations from huge data sets

Detect every anomaly to ensure optimal performance

Micro Segment classifier framework

EdgeDetect – Apexon Anomaly detection Engine

METHODOLOGY

Customizable ML-based Anomaly Detection

Apexon in Action

Asset Management Company Analyzes Churn to Retain 50% of Passive Customers

Claim Error Prediction for a Healthcare Payer Through a 360° View of the Customer

Highlight business-specific outliers

Let’s head to the apex

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