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Anomaly detection services

Keep your business safe and sound with state-of-the-art machine learning services

Get alerted about security breaches, digital intrusions and other anomalous activities.
Implement an effective mechanism to prevent fraud-related losses and damages.

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How can anomaly detection services
support business needs?

  • Enhance cybersecurity

    Guard your organization from cybersecurity attacks and get notified of suspicious activities, such as unexpected increase in traffic on devices. Predict future attacks with artificial intelligence algorithms.

  • Prevent fraud

    Safeguard your company from identity thefts, insurance application frauds, scams, phishing, and fraudulent credit card transactions. Get an embedded anomaly detection monitoring system that guards your systems.

  • Prevent losses

    Identify fraud-related activities and damages in realtime before they occur and can do any harm to your business. Implement effective mechanisms to keep your digital assets safe.

  • Save costs

    Create meaningful cost savings by protecting your organization’s assets that could be taken away by fraudsters. Increase your business productivity and efficiency by making the most of your resources.

  • Increase system health

    Get alerts about potential application performance incidents. Utilize preventive maintenance measures to keep your systems not only safe and sound but up and running smoothly.

  • Improve customer satisfaction

    Keep your customers happy and protect your system from fraudulent checkouts by embedding automated ML anomaly detection services. Increase your business reputation with a fine-tuned anomaly detection solution that doesn't reject false positives that are legitimate transactions.

Anomaly detection model creation process

Let’s take a quick look into the anomaly detection project phases that we can provide to you. The process of anomaly model creation includes the following activities:

  • Business context research

    We carry out Q&A sessions and workshops to get a deeper understanding of the patterns and outliers in the data.

  • Discovery of data sources

    We perform data exploration to determine how to handle data elements in the best way in order to get information about the anomalies and the relationships between them.

  • Data pre-processing and data cleansing

    We mine the data, then pre-process and cleanse it so as to ensure there are no duplicates, invalid data combinations, out-of-range values, or missing data that could generate misleading results.

  • Data aggregation and data enrichment

    We aggregate and enrich the data to augment and append the dataset with relevant additional information.

  • Data preparation

    We prepare the data for the selected algorithms and feature engineering, and then verify the data quality, which in turn ensures the results are accurate.

  • Anomaly detection algorithm selection

    We decide upon the most suitable machine learning algorithms for your anomaly detection use case, by choosing from among the most fitting supervised, semis-supervised
    and unsupervised techniques.

  • Feature descriptions

    We identify and describe in detail the anomalous features that signal fraud (e.g., number of transactions, behavioral time patterns, etc.)

  • Model training

    We train the anomaly detection model to obtain predictions about new and potential anomalies.

  • Performance evaluation

    We assess the anomaly detection model’s performance in order to determine the accuracy of the model for future anomalies on yet unseen data.

  • Crash monitoring

    We deploy the anomaly detection system to production and then conduct model crash monitoring to ensure any potential іssues are identified promptly.

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Anomaly detection case studies

  • AI-Powered Fraud Detection

    Trov | Insurance

    Custom solution for Insurtech that automatically identifies fraudulent activities and prevents fraud-related losses and damages by means of Artificial Intelligence and statistical techniques.


Industries we work with

Avenga’s anomaly team has large-scale experience in implementing the latest anomaly detection techniques for a range of industries, including the BFSI, oil & gas, and automotive industries.

  • From credit card frauds and identity theft to hedge fund related frauds, Avenga’s anomaly detection team will implement the latest machine learning algorithms so that frauds can be reacted to quickly.

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  • Make sure your insurance business protects its customers and that your insurance contracts aren't exploited to enrich the insured.

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  • Use autoencoders and k-nearest neighbors to determine which doctors should be given more priority by sales representatives in order to get the maximum sales of a drug.

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  • Save substantial amounts of money, enhance security and increase customer satisfaction with the early detection of fuel fraud.

  • Save lives using machine learning algorithms that detect threats, vulnerabilities and anomalies before they can cause any harm to a vehicle.

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  • Protect your platform from chargeback fraud, card cracking and other e-commerce frauds with effective anomaly detection services.

What you’ll get from Avenga

  • Analytical data report

  • A data modeling module

  • Source code with the necessary documentation

  • Fast and optimized data processing pipelines

  • Deployed application

The anomaly detection algorithms we use

  • Here’s a list of the machine learning algorithms that we have recently used to assist our clients with fraud detection tasks. The Avenga anomaly detection team is comfortable working with a plethora of both supervised, semi-supervised and unsupervised anomaly detection techniques and will choose the best-fitting algorithms that are suited precisely to your use case.


    • Statistical probabilistic models, such as univariate and multivariate Gaussian distribution, and Gaussian mixture models.
    • Autoencoders, variational autoencoders, and variational auto-encoding Gaussian mixture models.
    • Classification algorithms, such as isolation forest and distance to k nearest neighbors (k-NN).
    • Clustering algorithms, such as density-based spatial clustering, density-based local outlier factor, and OneClassSVM

What clients have to say

With them, we have one of the best specialists at our side. They provide us with everything from a single source – concept, design, technology and at the same time we benefit from their many years of experience in the industry. Accordingly, they understand our individual requirements down to the last detail, which greatly promotes the progress of the project. A trusting relationship as well as an uncomplicated and fast exchange complete cooperation.

Stefan Butzlaff Member of the board of tecis Finanzdienstleistungen AG, a SwissLife company

Why choose Avenga anomaly detection services?




happy clients


years of experience

  • Full-cycle

    We can confidently guide your business through all the stages of a fraud detection project, starting from data pre-processing on up to the anomaly detection model performance evaluation.

  • Business forte

    We are proud of our hands-on experience as an anomaly detection partner to industry-leading insurance, oil & gas, and automotive companies.

  • Prevention before intervention

    We have successfully implemented quite a few anomaly and fraud detection systems that detect errors and fraud before they can do any harm.

  • Consistency

    We don't limit ourselves to modeling only. Every anomaly detection solution we implement is well-automated, easy to deploy and supported on a consistent basis.

  • No prejudice

    We are not biased towards any particular anomaly detection framework. Our team will recommend the most efficient and cost-effective way to test and implement an anomaly detection solution that precisely fits your use case.

  • Orchestration

    We are cross-technology practitioners. We deliver custom software development, AI, cloud, and Salesforce solutions. We ensure the solution we implement works seamlessly and is in tune with any other services you use.

Ready to detect fraud?

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Meet any anomaly detection challenge with Avenga
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