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As the impact of the Coronavirus is felt around the world, the main focus of governments and business organizations is the safety and security of the citizens, employees, and customers. Meanwhile, In the United States, criminal fraudsters are taking advantage of economic crises by committing large scale fraud against multiple state unemployment insurance programs.
Unemployment insurance fraud is the collection of benefits from unemployment claims filed using false or inaccurate information or individuals collecting benefits when they are technically no longer eligible. According to the U.S secret service, one of the ways the criminals are using is this stolen Personal Identifiable Information (PII) in unemployment insurance scams. This type of fraud is often conducted by the amount of information being stolen in breaches and then sold in underground markets is opening new avenues for criminals to commit fraud. In the past 10 years, organized crime groups have begun to work smarter. It is safer, more economically viable, and involves much less risk for these groups to purchase stolen PII and use them for financial gain.
According to the figures released by U.S chambers of commerce the Americans have filed nearly 37 million unemployment claims since the beginning of March amid the coronavirus pandemic and corresponding economic fallout.
In the face of this momentous inflow, the state unemployment agencies throughout the nation are dealing with the avalanche of work and struggling to avoid fraud with the limited resources. The agencies have been overwhelmed with over 26 million new applications to process after the outbreak of COVID-19.
With so many new applications coming in, it is much easier for their fraudulent claims to slip by undetected. They often use synthetic IDs, or the personal information of others, to obtain additional unemployment assistance illegally. Experts estimate that about 9% of unemployment claims are fraudulent during the regular seasons. But now with so many people looking for help, the amount of the fraudulent claims could be more than ever and this will be devastating for the whole nation.
IBM X-Force Research has been seeing a significant number of new malicious domains related to COVID-19 appear in the wild since late February 2020, in which more than half of the malicious activity is happening mainly in two countries: 33 percent in Spain and 23 percent in the U.S.
The key to preventing this kind of fraud is data. State agencies can use large volumes of claimant data to accurately identify fraud. Each claimant service encounters data that can be used for analytics. Examining these data enables big data analytics to identify the linkages from previous unknowns. There are a number of analytical actions that can be performed on the data collected through online customer interactions. Few of them being:
From the point of view of identity theft unemployment fraud detection, link analysis is a powerful technique used to obtain actionable intelligence from the data to which state agencies have access. As new unemployment insurance claims are submitted, organizations need to resolve entities and discover relationships between new and historical data points. One of the challenges is maintaining an environment that will let organizations quickly identify relationships between nodes, often in real-time or near-real-time as new data comes in.
Among the emerging technologies, AI has become instrumental in several real-life applications and fraud detection is one of the stronger use cases of AI. It comes as no surprise then to see banks and financial institutions turning to advanced solutions that incorporate Artificial Intelligence (AI) and Machine learning (ML) technologies.ML has caused a stir in the fraud detection domain as it is equipped to deal with large volumes of data from numerous sources and capable of spotting abnormal patterns and links that humans are not able to identify. It is this very nature of ML that has led to many financial institutions deploying it as a viable tool for fraud detection. Fraud detection methods today are evolving from being rules-based towards pattern recognition since ML can recognize patterns and consumer behavior if trained correctly. Not only this, but it can also be used to protect companies from insider fraud as it can study data access from within the organization and identify any anomalies in individuals deviating from their day-to-day jobs or exposing data to outsiders.
Adding AI to the blend gives ML the genuinely necessary edge to move beyond just algorithm-based fraud detection. Machines can be customized to self-learn in a supervised model with AI, so that transactions that do not conform to a set pattern are identified and therefore can be actioned upon – in real-time.
Monitoring transactions in real-time is the key to fight this fraud. Machine learning and AI can be effectively used to find the fraudulent patterns in the historical transactional data and apply those learning to current transactions as they are attempted. As the fraud monitoring team labels the fraudulent transactions, the AI algorithms learn to understand how the fraudulent transactions differ from those that are genuine. These algorithms can take hundreds of variables related to a transaction, including the amount, location, instrument etc., to predict the likelihood of a transaction being fraudulent. Based on this, preventive action can be taken in real-time, including informing the concerned parties, or in extreme scenarios, blocking the transaction completely.
Instead of chasing after fraudulent payments after they are found, state unemployment agencies must continue working to prevent fraud before it occurs. To do this, they need to invest in solutions that can rapidly evaluate every candidate’s form for accurate and certify various information that focuses to check the authenticity of their identity and work history. These kinds of devices can feature any issues and spare critical time for analysts, permitting them to be both careful and quick. In the event that introducing another framework isn’t doable, agencies can likewise pick one-time checking instruments that will vet large batches of applications simultaneously.
Machines are far superior to people at handling huge datasets. They are able to detect and recognize thousands of patterns on a user’s purchasing journey instead of the few captured by creating rules. We can predict fraud in a large volume of transactions by applying cognitive computing technologies to raw data. This is the reason why we use machine learning algorithms for preventing fraud for our clients. The three elements which clarify the significance of AI are –
Speed: With the increasing velocity of commerce, it is very important for a solution to detect the fraud quickly and this is possible only with the machine learning techniques which enables us to achieve a sort of confidence level to approve or decline a transaction. AI/ML can evaluate large amounts of transactions progressively.it continuously processes and analyzes the new data. Moreover, an advanced model such as neural networks autonomously updating its models to mirror the most recent patterns.
Scale: Machine learning algorithms and models become more effective with increasing data sets. Whereas in rule-based models the cost of maintaining the fraud detection system multiplies as the customer base increases. Machine-learning improves with more data because the ML model can pick out the differences and similarities between multiple behaviors. Once told which transactions are genuine and which are fraudulent, the systems can work through them and begin to pick out those which fit either bucket. These can also predict them in the future when dealing with fresh transactions. There is a risk in scaling at a fast pace. If there is an undetected fraud in the training data machine learning will train the system to ignore that type of fraud in the future.
Efficiency: Rather than individuals, machines can perform excess endeavors. In this way, ML counts to achieve the work of data examination and perhaps raise decisions to individuals when their data incorporates bits of information. ML can often be more effective than humans at detecting subtle or non-intuitive patterns to help identify fraudulent transactions. it can also help to avoid false positives. Moreover, unsupervised ML models can continuously analyze and process new data and then autonomously update its models to reflect the latest trends.
Since machine learning is a very popular field among academicians as well as industry experts, there is a huge scope of innovation. Experimentation with different algorithms and models can help your business in detecting fraud. Machine learning techniques are obviously more reliable than human review and transaction rules. The machine learning solutions are efficient, scalable, and process a large number of transactions in real-time.
The need of the hour is as much a change in the mindset of decision-makers as it is in the need for new solutions to prevent new types of frauds. All kinds of institutions will need to embrace solutions that leverage emerging technologies like AI and ML that have shown great promise in not only predicting and mitigating frauds but also reducing operating costs significantly.
While such technologies will go a long way to ensure fraudulent activities are controlled, what we need here and now are authentic resources for people to consult. To that end, referring to this list will help applicants avoid shady claims and rest assured that they are reaching the right authorities: