Banks and financial institutions lose money every year due to fraudulent activities.
A survey conducted by KPMG shows that only less than 25% of fraud losses are recovered. This demonstrates that fraud prevention is the key. The good thing is that we are in the era of fascinating advancements and developments in technology. With the likes of artificial intelligence and machine learning, banks and financial institutions can detect fraud early on and in real time and with more accuracy.
The survey also did a good job of identifying patterns of fraud across the globe. It appears that fraud speaks the same language no matter where in the world it happens.
Similarly, fraud is not restricted or does not occur only in the finance industry. Industries such as health care and government agencies are equally affected by this. Simply put, fraud detection is a system to identify suspicious occurrences that can potentially harm the business.
Before the advancements in computer technology, instances of fraud were traditionally identified by analyzing a lot of structured information. This approach was highly time consuming because computers had to look through information to find repetitive or identical instances. There is a good chance that the analysis of structured data often turns up false positives. On the other hand, a rule-based approach can filter fraudulent instances, but it takes a long time to process the information and requires a lot of manual work as well.
Fraud is a form of crime that is adaptative and tech-savvy. This only opens up the possibilities of risk to markets that are heavily seeped or run on technological tools. The flip side is that it is a technology that is aiding businesses to identify and prevent fraud. Some of the data analysis methods applied to detect fraud are:
With the high volume of e-transactions growing, fraud detection becomes more challenging, particularly when you use traditional methods or through data analysis. Since Fraud is also become technologically sophisticated, end users find it extremely difficult to protect themselves against it. Hence, the tools you need to fight or prevent it should be equally sophisticated.
Another factor contributing to the challenges in fraud detection is the amount of data available in each industry. With large amounts of data, it becomes difficult to sift through them and find the necessary information to identify fraud.
Fraud prevention laws across the globe, state that it is the legal responsibility of the financial service providers for any such fraud damages experienced by the end user. This results in higher costs of doing business for the financial institutions.
To cope with these challenges of high data volume, high business costs, and high time to insight, organizations need to take the machine learning approach that allows financial institutions to find hidden fraud instances and also predict what to expect in the future.
The key to building a fraud detection framework is to remember that preventing fraud is a dynamic process. It is a process of constant monitoring, identification, decision making, and implementation. The machine learning system that you build needs to learn from past fraud incidents to look for similar patterns, it will analyze information feeds from various sources to predict new forms of fraud. It should be able to use the obtained insights and information to monitor business and financial processes.
Another point to remember is that when you build machine learning algorithms to detect fraud, your AI models should be able to differentiate legitimate transactions and consumer behavior from that of fraudulent ones. This should then be adapted into the learning of unseen tactics and behavior. In short, your AI model should do the right things right from the start.
This essentially means that there is no one-size-fits-all AI model. The AI model should be custom built to meet the most specific and unique requirements of your business. It should be able to integrate with various kinds of data sources, unify them and incorporate them into the analytical process.
In most machine learning instances, supervised learning approach is taken, and the models are built based on this approach. The models are trained on a rich source of transactions that are properly structured or ‘tagged’ as fraud or non-fraud. Some sophisticated platforms have algorithms that are able to pick up these signals automatically and tag them as fraud or non-fraud instances. The accuracy of the model depends highly on how clean the training data is.
With the unsupervised learning approach, the machine learning models are designed in such a way to be able to detect abnormal behavior without the help of tagged data or with minimal information. At the heart of this approach is the need to structure the underlying information so that the system is able to learn more about the data. In this approach there are no correct or wrong answers. Since the algorithms are self-learning, they are required to find patterns in data that are otherwise invisible to other kinds of analytics. They essentially are required to find previously unseen form of fraud.
As a rule of thumb, a good machine learning system for fraud detection is a good blend of both supervised and unsupervised learning techniques, along with behavioral and adaptative analytics that help financial institutions with real-time decision making.
An effective fraud prevention system should be able to promptly fraud instances so that these transactions can be reviewed and addressed early on. It should be able to learn from the history of fraudulent transactions and recommend process changes that will essentially prevent those instances from occurring again in the future. It should be able to learn and identify instances that are not heard of before. This could be in the form of sentiment analysis, web scraping, social signals.
A well-orchestrated machine learning system should be able to learn the right things from a complex set of data that your organization has. It should be able to provide trends and forecasts on the fraud trends in your industry so that you are identify weak products and proactively fix them. This way you can build operational safety for your business and a risk-free experience for your end users.
If you are worried about what the future hold for your business and how you would like to build a reliable AI safety net for your organization and customers, talk to us or register for a quick demo of Brainalyzed Insight.