Tackling fraud with AI and machine learning

From credit card theft to fraudulent insurance claims to personal identity theft, fraud costs financial services companies billions of dollars each year.

3 years ago   •   2 min read

By Angelique Tzanakakis

By one estimate insurance fraud cases – both detected and undetected – amount to 10% of overall claims expenditure in Europe; in the US, fraudulent claims are thought to add up to $80 billion a year. Payment card fraud losses, meanwhile, reached $29 billion worldwide in 2019.

The financial losses are staggering, but there’s also the toll that financial services companies’ attempts to prevent fraud take on the customer experience. Anti-fraud measures add friction to the payment or claims process, for example, resulting in a slow and annoying experience. And customers may be angry or embarrassed if their card is declined because of a false positive.

To combat the scourge, we’re seeing banks and insurance companies step up their investment in fraud detection and risk assessment systems that leverage artificial intelligence and machine learning (AI/ML). By crunching massive internal datasets and external databases using the scalability of the cloud, these solutions enable financial services companies to rapidly detect anomalies that may indicate the possibility of fraud or money laundering.

The algorithms can rapidly sift through millions of transactions and incidents to find and flag the anomalies – they can, within seconds, do work that may take a fraud officer days to do.

What’s more, they have a human-like capability to learn from experience, which means they will get better and better at detecting real incidents of fraud without triggering false positives. This elevates them beyond the simpler rules-driven systems companies used in the past to flag potential fraud.

Because they don’t have human biases, machines are also better at detecting new and unusual patterns than people are. They can rapidly learn and adjust in response to the ever-shifting tools and tactics that fraudsters use.

AI/ML can look at thousands of data points in real-time to assess whether, for instance, a car insurance claim is fraudulent. This can have a significant impact on the customer experience – the insurer could approve low-risk claims within seconds rather than sending an assessor to look at the car.

That’s not to say that implementing AI/ML is easy. There are significant hurdles to overcome in collecting the right data, ensuring that the data environment complies with the relevant data privacy laws, and in training AI/ML models to deliver accurate results. The rewards, however, justify the investment.

Feel free to get in touch if you’d like to learn more about how we are working with financial services companies to use AI/ML to drive down their losses to fraud.

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