Fraud analytics is the use of big data analysis techniques to prevent
online financial fraud. It can help financial organizations predict
future fraudulent behavior, and help them apply fast detection and
mitigation of fraudulent activity in real-time.
More people are using
online banking or managing their finances online every year. In 2020,
the worldwide lockdown due to COVID19 convinced even more customers to
use online banking for at least a portion of their financial activities.
Fraud analytics applies machine learning techniques to financial data.
Machine learning is a subset of Artificial Intelligence (AI). Where AI
is the computer implementation of a human-like thought or
decision-making process, machine learning uses mathematical algorithmic
techniques to extract complex relationships within the data being
analyzed. Fraud analytics uses machine learning to examine all the
pertinent data regarding a transaction and assigns a risk score to the
transaction. Based on the risk score it makes a recommendation to allow
the transaction, block the transaction, or ask for step-up
authentication before allowing the transaction. And this can all be done
in real-time with or without human intervention, providing the
financial institution with enhanced fraud prevention without causing
undue friction in the customer session. Every transaction, from login to
logout, can be examined for potential fraud risk. Data science is part
of the solution. Financial Institutions collect huge amounts of
behavioral, device and transactional data. Analysis of this data by the
fraud detection system and/or fraud investigations team can be used in
the prevention and detection of financial fraud. But the analysis will
only be as good as the data in the data set. With good data, there are a
number of big data analysis techniques that a machine learning-based
fraud analytics system can use to combat financial fraud. Predictive
analytics looks at patterns to make predictions on future, heretofore
unknown events to understand the potential or propensity for fraud.
Pattern recognition and anomaly detection identifies events that don’t
conform to expected patterns. Machine learning algorithms can learn from
the data and make predictions on future events.

Harry Lu
https://www.linkedin.com/in/chinhengharrylu/

The Data Standard
https://datastandard.io/
https://www.linkedin.com/company/the-data-standard/
https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q