Real-time Transaction Audit and Monitoring
As more and more transactions are processed with less and less
human intervention, it is essential to have intelligent
audit and monitoring systems in place.
Audit and monitoring business transactions occurs for various reasons;
- Regulatory compliance:
laws and government regulations require
certain controls imposed on business transactions, e.g.,
SarBox Sarbanes-Oxley Act Section 404, etc.
- Internal policies:
Companies and organizations set out
internal policies for management and marketing purposes.
- Fraud detection:
Fraudulent claims are costly for healthcare and insurance
industries. Early detection can be vital in reducing fraudulent claims.
For more, please read Healthcare Fraud Detection.
- Risk management:
Credit card payments have risk of being defaulted.
Detecting early signs of default may prevent further credits.
Transactions can be mistakenly processed. Verification
can reduce mistakes and errors.
- Event monitoring: Detection of unusual events is
important in various industries.
- Customer churn alert: detection of imminent customer churns.
- Intelligent precision marketing: Intelligent algorithms can
detect cross-sell and up-sell opportunities automatically as customers
Business Activity Monitoring (BAM) with Complex Event Processing (CEP)
Current generation Business Activity Monitoring (BAM) systems are limited
to real-time visual dash boards. They are very much stone-age monitoring
systems! They are not equiped with functions that can capture complex
events described with complex rules.
RME-EP is a new generation expert system
that combines the best of rule-based inference and
predictive modeling. RME-EP changes the
current notion of BAM. Incorporating rules with predictive models,
it makes complex event processing (CEP) possible.
How predictive modeling can harness BAM?
mean prediction of risky events!
They can be developed to detect various risky events with high accuracy.
In another words, predictive modeling can be
used to identify potentially risky transactions.
For example, neural network
can be trained so that it can detect potentially fraudulent credit purchases,
or to alert risky insurance applications to actuaries, and so on.
StarProbe data miner supports powerful predictive modeling tools.
Users can build models with the help of intuitive model visualization
tools. StarProbe data miner comes with the following
Predictive Neural Network
Neural network is a predictive model which is based on the architecture of,
say, our brains. Neural networks can be trained to predict both
numerical risk levels and categorical risk classifications.
It can predict rare events very well.
For more, please read Neural Network.
Decision tree is a predictive modeling technique which is based on
recursive segmentation and probabilistic reasoning. It is primarily used
to classify risk categories. StarProbe data miner supports
prediction of statistical probability as well.
For more, please read,
Decision Tree Classifier.
Rule Induction classification
Rule induction is based on Hotspot Profiling.
It builds predictive models based on profiles of risk hotspots.
This is a technique that you will be very interested!
Regression develops mathematical predictive formulas for numerical information.
Although limited for general modeling, it can be useful for
developing segment-specific models.
RME-EP: Audit-rule Specification Language
RME-EP is a variant Rete engine. Rete engine is a de-facto industry
standard technique for
rule-based expert systems. RME-EP provides a very powerful rule-specification
language which is based SQL-99. The following shows an example of BAM.
It shows how new sales trends can be detected automatically and informed.
Note that detecting new sales trends is not trivial tasks! It involves
For more information,
please read RME-EP.