#3 Machine learning is the future for anomaly detection and the detection of fraud

Governments and law enforcement agencies – as well as companies in the insurance, financial services, and legal sectors – are handling increasingly sophisticated scams and rising caseloads. In the past, victims of fraud had to rely on basic technologies and human eyes alone to spot criminal activity. But today, machine learning is revolutionising how fraud is detected and mitigated. 

The role of machine learning in anomaly detection  

Small signals in datasets – which on their own may mean nothing much – over time and when combined with other signals can provide much stronger indications of fraud. Things like missing funds, mismatched personal data, suspicious claim ‘evidence’, financial aberrations, forged legal documents – these data anomalies could mean that a scam is in progress. But given the volumes of data that organisations are handling today, it is impossible for employees to detect every anomaly by hand. Many organisations are still reliant on rigid, rule-based systems that are easily gamed by fraudsters and cannot be relied on to spot every instance of fraud, every time. Even more sophisticated analysis tools are limited in the range of data and signals that can be ingested. 

This is where machine learning (ML) steps in. ML algorithms detect patterns and anomalies in huge datasets in real-time, and with a virtually limitless number of variables and compute power. Moreover, the value of ML increases over time because an algorithm can “learn” to recognise suspicious signals and proactively hunt down aberrations. Algorithms can be trained to recognise anomalies in everything from text to speech, visual and video content, unstructured and structured data, and behavioural patterns. This actionable data is then fed to a “human-in-the-loop” for appropriate action.  

Machine learning’s role in insurance 

One sector that stands to benefit hugely from ML anomaly detection is the insurance sector. UK insurance companies alone registered an estimated 300 insurance fraud cases per day and £1.2 billion worth of insurance fraud in 2019. In the same year, fraud was estimated to have cost US organisations at least $80 billion. 

The ability of ML algorithms to apply hundreds of parameters to datasets offers a way for insurance companies to stop these losses. An algorithm can flag insurance claims that follow known patterns related to fraud and can even propose risk indicators that have not yet been considered. The beauty of ML is that it runs around-the-clock in the background; it’s also increasingly nuanced. It can analyse and compare data in real-time to alert human operators to potentially fraudulent activity. For example, ML can alert call centre employees to case details of interest as they are on the phone with a claimant, or they could recommend suspicious cases for redirection to the appropriate department. 

ML in action – keeping the roads safe 

We are also now seeing even more sophisticated use cases for ML when it comes to detecting fraudulent activity in other areas. For example, we worked with DVSA to apply ML to the management of MOT standards. DVSA manages more than 42 million MOT tests in Britain every year. To keep roads safe, the quality of MOT tests must be maintained – but detecting inadequate standards and fraud is a colossal challenge. We developed a predictive ML model to identify fraudulent activity and MOT test areas needing improvement. The Risk Ratings algorithm model was able to monitor performance and pass rate levels, as well as detect trends in testing over time. 

ML can also monitor employee behaviours to flag potentially malicious activity. In finance, for example, algorithms can collate data quickly for mortgage or account applications or could be used as a biometric or behavioural verification tool for ID checks. And in auditing or legal scenarios, data comparisons can be made to flag illegal activity or compliance issues. 

Connecting the dots through machine learning 

With each anomaly and indicator of fraud that is missed, organisations  pay the price. When you consider the vast data lakes businesses have to handle, manual anomaly checks are not cost-effective, nor efficient. ML can take the pressure off everyone from insurance claim handlers to auditors – no matter the size or technological maturity of a company. 

Kainos is the ideal partner to start you on your journey. We have expertise in intelligent technologies that allow organisations and their staff to get on with the jobs that really matter. With the right tools, the burden of fraud could be a thing of the past. We were shortlisted in the Outstanding AI ML Project Awards category of Computing AI & Machine Learning Awards for work with our previous clients.  

If you would like to know more about making machine learning work for your organisation, contact us today.