The machine learning layer connects the rule-detection and relevant modelling functions. It starts by generating the rules-based framework from the initial training dataset by using most relevant machine learning algorithms.

Rules Management using Rule engine (More..)

Rules can be used within the context of detecting insurance fraud for (a) claim submissions or (b) policy applications. In cases that require immediate decisions integration of the rule engine with a front-end application is required.


Our team of data scientists, modellers, team leaders and product developers, has deep expertise in development of ‘R’ and Python-based applications such as modelling automation tools and different kind of modelling.


Fraud Analyser supports remote team coordination with a feature set that is strong on visual characteristics such as 1) Drop-down functionality to select different modelling techniques 2) Data manipulation functionality to remove or encode specific variables 3) Dashboards to track progress of data scientist activity and model development 4) Model comparator views 5) Periodic customer reports. ​ But that’s not all. To differentiate the solution from industry standards such as IBM ODM and FICO Blaze Advisor, Fraud Analyser comes with a dedicated data support team, so that product adoption is swift and easy. ​ The platform is not sticky and enables easy exporting of work when done periodically.

Our blog


  • People and Process Management

    To keep the things simple, it’s good to involve the people to manage the process. Process is managed by team based in …
  • Challenges of Building Fraud Detection and Prevention Systems

    Techniques to detect and prevent Frauds: Expert driven fraud detection and prevention technique represents a good starting point and complementary to other …
  • Introduction To Frauds

    Fraud Definition: An organization loses typical 5% of revenue due to fraud each year. As per Oxford dictionary, fraud is defined as …
  • Rule Generation with Fraud Analyser

    As mentioned in previous article, if we want to make instant decision about a transaction, application or claims, we need to implement …
  • Rule Engine Implementation

    Predominately, there are two kinds of solutions in the market for fraud detection and prevention 1) Based on rule engine 2) based …
  • Score card development

    For fraud management applications, we are looking to predict fraud score (probability of fraud). So that we can process credit card and …

Detect fraud and provision for it. Reduce false positives. Improve your bottom line.

Fraud Analyser is a SaaS product with an in-house data science support team bundled in. Priced on an annual subscription basis, it delivers a highly customisable solution that is user-friendly, effective and competitively priced.