real - t ime frau d de t e c t i o n
82.00%
80.00%
Figure 1:
Accuracy of
fraud detection.
Accuracy
78.00%
76.00%
74.00%
72.00%
70.00%
10:1
5:1
4:1
Ratio of (no. 0):(no. of 1)
fraud detection framework to make it better and more accurate over time.
Results and Model Validation
In this case, the models were trained
on 70 percent of the transaction data,
with the remainder streamed to the
agency framework discussed above to
simulate real-time financial transactions.
Under-sampling on the modeling dataset was done to bring the ratio of number of non-fraudulent transactions to
10:1 (original was 20:1).
The final output of the agency is
the classification of the streaming input
transactions as fraudulent or not. Since
the value for the variable being predicted
is already known for this data, it helps us
gauge the accuracy of the aggregated
model as shown in Figure 1.
Conclusion
Fraud detection can be improved by
running an ensemble of algorithms in
52
|
a n a ly t i c s - m a g a z i n e . o r g
parallel and aggregating the predictions
in real time. This entire end-to-end application can be designed and deployed
in days depending on complexity of data,
variables to be considered and algorithmic sophistication desired. Deploying
this in the cloud makes it horizontally
scalable, owing to effective load balancing and hardware maintenance. It also
provides higher data security and makes
the system fault tolerant by making
processes mobile. This combination of
a real-time application development
system and cloud-based computing
enables even non-technical teams to
rapidly deploy applications. ❙
Saurabh Tandon is a senior manager with
Mu Sigma (http://www.mu-sigma.com/). He has
over a decade of experience working in analytics
across various domains including banking, financial
services and insurance. Tandon holds an MBA in
strategy and finance from the Kellogg School of
Management and a master’s in quantitative finance
from the Stuart Graduate School of Business.
w w w. i n f o r m s . o r g