1 research outputs found
An anomaly prediction framework for financial IT systems using hybrid machine learning methods
In financial field, a robust software system is of vital importance to ensure
the smooth operation of financial transactions. However, many financial
corporations still depend on operators to identify and eliminate the system
failures when financial software systems break down. This traditional operation
method is time consuming and extremely inefficient. To improve the efficiency
and accuracy of system failure detection and thereby reduce the impact of
system failures on financial services, we propose a novel machine
learning-based framework to predict the occurrence of system exceptions and
failures in a financial software system. In particular, we first extract rich
information from system logs and eliminate noises in the data. Then the cleaned
data is leveraged as the input of our proposed anomaly prediction framework
which consists of three modules: key performance indicator(KPI) data prediction
module, anomaly identification module and severity classification module.
Notably, we design a hierarchical architecture of alarm classifiers and try to
alleviate the influence of class-imbalance problem on the overall performance.
Empirically, the experimental results demonstrate the superior performance of
our proposed method on a real-world financial software system log data set