Fight the fraud: How insurers are adopting powerful technology for fraud prevention
Indisputably, fraud is detrimental to the bottom line of insurers, with 96,000 dishonest insurance claims valued at £1.1 billion detected in 2020. That number is rising as the cost of living crisis that the UK is currently facing has led to an increase in insurance fraud. Zurich UK found that fraudulent property claims from 1 January to 31 May 2022 were 25% higher than in the previous year's period.
Enterprise fraud is another huge challenge for insurers due to the complexity of their business set-up. Products and channels managed by different teams leave weaknesses within the operating model which can be exploited by internal and external sources.
Fraud prevention is vital to an insurer’s business strategy as it can help optimise the bottom line by reducing losses and delivering a competitive advantage. Protecting genuine customers and their claims is crucial to reduce the insurer’s churn rate - a considerable factor affecting the bottom line. This can be done by ensuring that their experience and the prices they pay remain competitive (prices can be driven up by fraudulent claims). Insurance companies must stay ahead of the growing fraud curve to thrive in the digital, customer-first economy.
Cloud technology provides a safe foundation for fraud prevention
The cloud offers a secure technology infrastructure foundation for insurers, allowing them to comply with security and compliance requirements more easily and securely share and store sensitive data.
Businesses in all sectors have benefited from using the cloud to manage their modern technology stack. It makes it easier for teams to get new technologies up and running, improving a company’s ability to innovate fast.
By operating from the cloud, insurers can increase their efficiency and quickly implement technology that will deliver fast results in many areas, including fraud prevention.
Detecting and preventing fraud while reducing employee burden
Digitising the claims process
One of the most crucial processes that can affect an insurance company's performance and reduction of fraud is efficient document processing. Invoices, claims, policies, underwriting documentation, etc. are a few examples of the numerous processing duties that go into this labour-intensive, manual process.
Digitising the claims process involves using a range of technologies such as artificial intelligence (AI) and intelligent automation. With digitised processes in place, fraud can be detected more easily while providing genuine customers with a smoother, faster and more effective claims processing service.
Through the use of intelligent document processing (IDP), insurers can extract data from emails and forms and easily submit them into claims processing systems. Verification of policies, as well as documentation, estimates and invoices, can pick up anomalies that might be missed when processing manually. Using a combination of AI and automation, IDP creates a self-learning system that contextually understands data and processes documents at high speed. This can support and improve the decision making of claims handlers, leading to a more streamlined process.
Detecting small signals in datasets
Artificial intelligence is being utilised by criminals to steal critical personal information from unsuspecting customers and then can be used for all sorts of fraudulent purposes. At the same time, it is revolutionising how fraud is detected and mitigated. In particular, machine learning (ML) is an AI technology with the potential for detecting patterns in huge datasets in real-time and, over time, ‘learning’ to recognise anomalies and alert teams to suspicious behaviour.
Analysing small signals about potential fraud is difficult for humans to sift through, and the signals may not mean much on their own. But, when combined with other data and signals, it could indicate fraud and stop it in its tracks. For example, machine learning technologies can pick up incorrect personal data, unusual claims evidence, and forged documentation. It’s not efficient or easy for employees to manually detect and triage every potential anomaly, making machine learning algorithms the perfect tool for picking up on these risk factors. Algorithms can be trained to recognise anomalies in all kinds of data, such as text to speech, visual and video content, unstructured and structured data, and behavioural patterns. Once detected, relevant teams can use this actionable data to investigate further.
Insurance firms can reduce the losses associated with fraud through the ability of ML algorithms to apply hundreds of parameters to datasets. An algorithm may identify insurance claims that exhibit well-known fraud-related patterns and even suggest unconsidered risk indicators. ML continuously operates in the background and gets more sophisticated over time. Real-time data comparison and analysis can warn human operators of possibly fraudulent conduct. For instance, ML can notify call centre staff of pertinent case facts while speaking with a claimant on the telephone or suggest suspicious cases be forwarded to the relevant department. The human operator is much more likely to stay in control in such situations.
Predictive analytics for better fraud detection
Many consider predictive analytics a crucial weapon in the fight against insurance fraud. Predictive analytics, like anomaly detection, involves teaching artificial intelligence or machine learning algorithms using historical data so that they can eventually anticipate future occurrences. The potential for predicting vulnerabilities during the claims process could make a big difference to insurers as it would give them the ability to gain additional time to act and prevent rather than merely react to fraud.
Predictive analytics provides a chance to identify and prevent fraud far more successfully than ever due to the vast data sets that insurers have amassed over time and the capacity to use appropriate analytical models.
Add more power to prevent fraud
The powerful combination of intelligent document processing, machine learning and predictive analytics is set to change fraud prevention from reactive to proactive. Previously, fraud victims depended solely on human sight to detect suspicious behaviour. While data quality is still a challenging problem for businesses in all sectors, the emphasis should be on minimising employee burden managing large data volumes through digitising processes, improving data quality, and ensuring that information is always accessible. Intelligent document processing, artificial intelligence, machine learning, and predictive analytics are all examples of innovative technologies revolutionising how fraud is identified and mitigated more efficiently, accurately, and at scale.
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