Manisha was relaxing on a weekend at home when her phone beeped thrice. Thinking it to be regular messages from friends, she grabbed the phone. But what she saw left her aghast!! Purchases worth Rupees 50,000 had been done against her credit card in three transactions. She immediately checked her wallet and found her credit card there. How could this be possible?
Unfortunately such incidents are quite common nowadays. While an increase in online transactions for banking, insurance, e-commerce etc. have made life easy, it has also led to rampant rise in fraudulent activities. Businesses are facing huge losses due to this and losing customer trust. Hence, they are increasingly investing in fraud analytics to arrest the occurrences of such crimes.
What is Fraud Analytics?
Fraud Analytics is the system, program or process through which transactions by illegitimate buyers/customers can be caught in real-time and only the genuine transactions can be allowed. Such programs are created after studying the pattern of such activities, technical details as well as other suspicious indicators.
Though widely used in e-commerce businesses, Fraud Analytics is quite in demand by banks, financial institutions, and insurance, healthcare as well as government agencies.
Why do we need Fraud Analytics in spite of the current Fraud Detection techniques?
- Many companies do the process manually. However, this has its own limitations. The existing mechanisms are suited to countable number of records and just a few megabytes of data. However, in the real world, problems can extend up to gigabytes or even terabytes of data.
- Traditional systems to flag fraudulent transaction is based on a fixed set of rules which have been set well in advance. However, criminals are smart enough and often change their way of functioning. Fixed set of rules become ineffective in such cases. Fraud Analytics have far more flexibility in tackling changing behavioural patterns.
- Not only can analytics tools enhance rules-based testing methods, but they can also help measure performance to standardize and help fine tune controls for constant improvement.
- Through Analytics, Frauds can be detected real-time and can be prevented. For example, some insurance companies have processes to scan for fraud even before a policy or claim is approved. On the other hand, in manual checks, the process is always done after the transactions are complete.
- In some cases frauds are pretty obvious, while in other cases, it is quite difficult to identify. In such cases, where the pattern is complex, Machine Learning is the answer. An algorithm can be developed to detect real-time frauds as well as predict their occurrences in future.
The current approach to Fraud Analytics involves leveraging the vast amounts of Big Data collected from online transactions. Analysts study the intricacies of this data and model it in a manner that allows them to flag real-time frauds or predict fraudulent activities in future transactions. For this, Data Science, Predictive Analysis and Machine Learning techniques are the obvious answers!
Source: Genpact Analytics
So what exactly does a Fraud Analytics Team do?
- Studying transactions as research materials, analyzing individual elements such as the IP address of the order, discrepancy in billing/shipping address, identifying use of proxy servers, and developing automated fraud-detection algorithms using these elements.
- Leveraging data analytics and on-demand metrics to create and improve the algorithms.
- Streamlining the fraud recovery process
- Maximizing fraud prevention and error prevention opportunities.
- Designing and applying scoring and predictive analytics techniques.
What are the challenges faced by Fraud Analysts?
- Building a model for fraud often depends on previous behaviours for fraud predictions, however, criminals change their moves very quickly and it is difficult for the model to factor in so many rapid changes.
- The entire modelling, detection and prediction will be based on Data, hence the quality and consistency of data is of paramount importance. Since the data is from various sources, it is quite possible for the quality to be compromised. However, as a Fraud Analyst you constantly need to monitor the data quality and do periodic clean ups when required.
- Organizations are inundated with data from various sources and this may result in humongous data volumes for each business process. The data analytics testing infrastructure should be well-equipped to handle huge volumes of data.
Opportunities ahead for a Fraud Analyst
There exists a huge skillset gap as far as Fraud Analytics is concerned. A certification in Data Science is sure to open avenues in this profile for you, but it is your mind set that will ensure your success as a Fraud Analyst.
There are three important qualities a Fraud Analyst that will give him an edge as a professional. Firstly, you should have sharp business acumen. While creating models or working on Fraud Analytics you must always keep the larger business goals in mind and align your activities accordingly.
Secondly, you need to have excellent communication and visualization skills. A good Fraud Analyst knows how to represent complex analytical models and related statistics in the most user-friendly manner. The art is to communicate the right amount of information at the right time.
Thirdly, as a Fraud Analyst, you need to be high on adaptability. Fraudsters tend to change their modus operandi on an ongoing basis, on the other the world of Big Data and Analytics is also forever evolving. Hence, you need to be have an ability to be on toes and adapt to the dynamic nature of events.
Being a Fraud Analyst, you should be open and be able to see the story that the data is telling you. An inquisitive mind, the skill to dig deep into data, slice, dice & analyse it in different ways and the ability to use the available data through various permutations and combinations are the qualities that will set you apart as a Fraud Analyst.