AI Telephone Call Classification for Prohibited Activity Detection
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Solution Overview
Problem
Current methods for classifying telephone calls to detect prohibited activities, such as account takeover or identity theft, lack automation and rely heavily on human judgment, making it difficult to accurately assess the likelihood of illegitimate calls in real-time.
Innovation Solution
The use of machine learning and artificial intelligence techniques to classify telephone calls by training a model with various telephony and additional variables, including externally provided call classification scores, to generate a score indicating the percentage chance of a call being related to prohibited activities, utilizing a system that processes and combines heterogeneous data inputs into conglomerate vectors for neural network training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated machine learning classification is implemented, then measurement precision of call legitimacy is improved, but device complexity increases
Solution Approach 1:
The system segments the call assessment process into distinct functional modules: data collection module that gathers telephony variables and external scores, feature engineering module that creates conglomerate vectors, machine learning classification module that processes vectors, and remedial action module that executes responses. This segmentation improves measurement precision through specialized processing at each stage while managing device complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by collecting and preprocessing call data, creating conglomerate vectors from multiple variables, and training machine learning models before actual call classification is needed. External call classification scores are obtained in advance, and feature engineering is performed beforehand to transform raw data into meaningful vectors, thereby improving classification accuracy without increasing real-time system complexity.
2Measurement precision
If multiple heterogeneous data variables are processed, then measurement precision of call risk assessment is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system merges multiple heterogeneous data variables including telephony variables (call duration, caller ID, routing information) and external call classification scores from third-party providers into unified conglomerate vectors. This combining approach improves risk assessment accuracy by integrating diverse data sources while managing processing difficulty through standardized vector representation that consolidates disparate data types into a consistent format suitable for machine learning classification.
Data Source
AI summary
Techniques are described relating to automatically classifying telephone calls into a particular category using machine learning and artificial intelligence technology. As one example, calls to a customer service phone number can be classified as related to prohibited activity, or as legitimate. In particular, a number of different telephony variables as well as additional variables can be used to make such a classification, after training an appropriate machine learning model. The training process may use an externally provided call classification score that is provide by an outside entity as an input, and can be calibrated so that the output score of the trained classifier provides a score that corresponds to a real-world percentage chance of an unclassified call falling into a particular category. Thus, a classifier score of “95” can indicate that a call is in fact believed to be 95% likely to correspond to prohibited activity, for example.