How to Reduce False-Positive with AI-powered Fraud Detection

Businesses are proactive these days. Trying to thrive in a standard set by the globally connected online community is the priority for any business. At the same time, the world of the digital industry is filled with pitfalls like online frauds while encompassing promising benefits. These online frauds pose a severe threat to your digital goals in this digital era, and false positives are at the centre of these online frauds. False-Positives are detrimental to smooth customer experience and thereby to your business growth. This complex issue creates a series of very important questions that need to be answered, such as “Is there a way to shield your customers from this issue?” “Is Artificial intelligence a solution? Is it possible to reduce the False-Positive rate with AI?” Continue reading to get these questions answered.

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What Is False-Positive & Customer Insult In Fraud Detection?

False-Positive in the digital world is a computational or detection error. When a False-Positive occurs, a legitimate customer or transaction is falsely identified as a fraudster called a False Alarm. Typically in the event of a False-Positive in the system, the customer is denied the entry or the transaction. A False-Positive error is detrimental to the customer experience as its counterpart False-Negative, where a fraudster or a fraudster IP interaction is ruinously identified as a legitimate customer. Both False-Positives and False-Negatives are highly damaging to the business. False-Positives are damaging the digital banking industry. The expert survey states that the False-Positive rates can spike up to 90% while considering the banking industry. At that point, stakeholders/customers are forced to resubmit the transaction resulting in an unpleasant experience. Aside from the banks, AI False-Positives is a major pressing issue for Credit Union and other fintech companies.

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When and why does a False-Positive occur?

Like most subsets of Risk Operations, any fraud detection tool makes your customer susceptible to compromise in their seamless experience. False-Positives pose critical damage that results from the failure in the fraud detection tool that is employed. For example, a credit card holder can accidentally be identified as a fraudster by the bank’s fraud detection system. The bank would have developed the system to identify patterns and suspicious activities that could be seen as fraud. These patterns may be identified using the input fed to SML by the bank or through a Rule Engine. Also, identification may just be based on a simple Reputation List. Despite the meticulous programming, the fraud detection system would have holes and would not be perfect, which historically leads to the false accusation of a legitimate customer.

In the case of the SML, it works with legacy experience and needs a human analyst to input what should be considered fraudulent. Rule engines, like SML, depend on legacy experience, are reactive, and require analysts to keep on updating the rules. That makes SML and rule engines less competent to tackle the fast-moving fraudulent tactics. Also, the reputation list is far too vulnerable to be imprecise and can be corrupted.

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Why Is Reducing The False-Positive Rate More Important Than Reducing Fraud Itself?

A false accusation or ill-informed denial of a credit card transaction of a legitimate customer can leave the customer with a negative attitude towards your business; these customer insult scenarios cost more to the trades than what fraud can do to them. Accused customers majorly choose to replace their bank after such transaction denial. Getting such a reputation for undertaking steps in fraud prevention is not a good trade-off. This dissatisfaction caused to customers has many severe consequences on the business. That is why it is more important to reduce the False-Positives than reducing the fraud itself.

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How Can AI Reduce False-Positive Rates?

One of the main requirements to reduce the False-Positive rate is to have viable holistic data with centralized intelligence from vendors that can differentiate legitimate actions from fraudulent ones which are vital because fraudsters can mimic good customers and exploit the weakness in the system. Frauds and their methods keep changing, and the ideal solution should be able to keep up with their fast-moving pace. Further, the long-tail presence of the fraud demands a countermeasure that is capable of encountering too many unique instances.

AI fraud detection fits all these requirements more closely than any other solution as artificial intelligence can identify fraudulent activities that the most capable human analyst misses. Machine learning (which is a more advanced AI) with access to real-time data can enable you/the vendor to make well-informed decisions and lessen the incorrect alarm that leads to False-Positives. Machines are efficient when they have more input data. Machines with large sources of data are much more accurate in their detection. One of the reasons the Fraud detection system fails is because fraudulent methods are evolving while the detection systems have a stagnant understanding of what is considered fraud. The evolving Fraudulent methods can be tackled by continuous learning of the machines to prevent the system from getting stagnant and make it more dynamic in its approach to fraud detection. Machines with the existing customer data input can distinguish between anomalous customer behaviour and fraudulent customer behaviour. It is possible through artificial intelligence acquainting with the past customer behaviour or spending habits (in the case of fin-tech). AI is optimal for dealing with long-tail frauds. Plus, it is capable of adapting to fast-paced fraudulent actions when they are dynamic. Thus, machine learning can reduce False-Positives with AI-powered fraud detection identified by the rule engine.

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How to better equip your AI fraud detection system?

Machine learning is guided by algorithms on how to group the data in the best order to reveal patterns and draw overall conclusions on whether the action is fraudulent or not. Machines consume information and then label them to create “the standard” from which the rest of the incoming information will be judged for abnormalities. The two most popular algorithms that train machines are the supervised learning model and the unsupervised learning model. Supervised Machine Learning (SML) requires predefined parameters to identify what is considered fraudulent. Whereas the Unsupervised version is capable of self-learning; therefore can detect new fraudulent behaviours that were not experienced by the system before. You can choose an algorithm based on the situation that your system has encountered to reduce False-Positives with AI.

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You can create high-performing machines with relevant data supplied by algorithms. Then AI’s can accurately identify frauds, reducing the False-Positives and the Anti-Money Laundering behaviour (AML). Some of the data types that will benefit the algorithms are:

• Standard customer data

These are the data collected as KYC for your customers. Taking in additional data such as customers’ age, prevailing economic relationship with other account holders (Both family and business), and debt payment frequency can be advantageous.

• Transactional and non-transactional data

Transaction volume and frequency information, and also the preferred transaction type. While for non-transactional data, the account customization data or anomalous spending behaviour is ideal.

• Location data

Customers’ IP location and billing location, both being different, is usually a red sign. And multiple money withdrawal from a different location is also indicative of fraud.

The emphasis in risk management should not be on identifying the fraudulent activities but on preventing those activities while ensuring no customer-insult events. To reduce False-Positives with AI is an effective way of getting through these fraudulent activities without accusing legitimate customers. Artificial intelligence is much more competent in identifying the fraudulent activities that the most capable human fraud analyst would miss. These AI fraud detection systems help reduce False-Positives with AI, thereby eliminating the collateral damage such as financial loss and reputation loss for your business. To get all the latest updates and news regarding AI fraud detection and more, sign up for our social media and newsletter.

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