Tackling Fraud with AI: banking’s secret weapon

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Each year, several banks pay the price for failing to implement counter-fraud initiatives, typically in the form of hefty fines. For example, earlier in 2017, a large global bank was fined more than £500m for its ‘anti-money laundering failings’, due to suspicious, potentially fraudulent transactions.

As fraudsters become more advanced, tackling fraud becomes an increasingly tricky challenge. Fortunately, artificial intelligence (AI), coupled with machine and deep learning, provides banks with the ability to investigate crime in real-time, addressing multiple and often complex concerns. Here are the key areas where this sophisticated intelligence can help:

One Step Ahead of Fraudsters

When it comes to uncovering fraud, deep learning is particularly useful in seeing the complex patterns hidden in the financial data. Deep learning sets itself apart from conventional machine learning techniques with its specific approach and has proven a valuable tool for detecting granular connections.

Identifying anomalies in data is key: for every transaction that is carried out, it’s crucial to assess historical data to detect which customer transactions fail to be completed and identify which are behaviourally typical, and which ones aren’t. This doesn’t allow us to learn quickly enough, however, because as soon as we identify fraudulent transactions, fraudsters adjust their approach. But now, real-time predictive capabilities are enabling banks to implement game-changing solutions in the battle against fraud.

AI Driven Real-Time Fraud Solutions

Legacy technology doesn’t deliver the insight needed to tackle suspicious activity, making many financial services organizations unable to move quickly enough to analyze transactions and identify fraudulent activity. Some banks, however are developing real-time solutions through state of the art, AI-driven detection engines: these engines use machine learning to hook advanced analytics blueprints up to incoming transactions.

Having a re-enforcement type of algorithm is of vital importance – it’s an algorithm that is capable of learning in a fast-paced environment and adjusting to fraudsters’ techniques in real-time.

AI, when in place, can give responses back to score transactions using deep learning algorithms, delivering immediately available and actionable insight in real-time. So, when a customer is trying to make a purchase using a debit or credit card, the detection engine can score transactions within 0.3 seconds, flagging fraud or approving genuine transactions without interruption to purchases.

As well as being useful for customers, AI is highly beneficial for banks. Fraud cases are still relatively rare (only approximately 0.5 percent of transactions are fraudulent), but fraud detection that isn’t sophisticated enough can flag up to 99% of transactions as potentially fraudulent. Fraud investigation teams are then tasked with the highly time-consuming task of investigating far more transactions than necessary. But with improved artificial intelligence aided detection, the number of ‘false positives’ is reduced significantly, freeing up teams to be deployed on more meaningful work.

What Does the Future Hold?

When it comes down to it, some banks are simply using analytic technologies better than others to improve fraud investigations and comply with standards. But the landscape is becoming tougher; with increasing regulation costs in the banking industry, I’m sure that we’ll see the strategic use of AI and machine learning to tackle fraud becoming an essential part of organizations’ armories. In hindsight, AI can save banks millions by eliminating complex fraud cases and protecting their brand while preventing potentially costly damages.


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