How to revolutionise insurance investigations using data sources industry leaders love.
Combatting insurance fraud requires insurers and investigators to make intelligent use of reliable data sources.
So, what kind of data should businesses consult to positively identify cases of insurance fraud?
Let’s examine the chinks in the armour of insurance and figure out how data can fill them.
The Essential Power Of Trust
If there’s one element that is foundational to the ideal of an insurance relationship – it’s Trust.
Unfortunately, Trust is in high demand, and short supply.
Trust needs to go both ways in Insurance. Customers need to trust that they’ll receive the requested support or compensation in line with their contract. Insurers need to be able to trust that their customers are who they say they are, and that any claims are legitimate.
It’s this last point that so often puts strain on the trust in an insurance relationship. With insurance fraud running rampant, trust is a luxury insurers cannot afford to give out blindly.
Why Is Insurance Fraud Becoming More Commonplace?
The motivations for fraudsters attempting to scam insurance companies varies. For organised fraudsters, exploiting and undermining insurer trust through any available opening is part of their day-to-day affairs.
Increasingly, insurance fraud is seen as a form of ‘soft fraud’ by the public. Due to mounting economic pressure from the cost-of-living crisis, otherwise law-abiding individuals are more likely to risk making falsified claims.
This issue has been highlighted by various thought leaders in the insurance space. Adam Winslow, Aviva’s UK and Ireland general insurance chief executive explains this behaviour as “short-term decisions as a consequence of the macroeconomic recessionary style environment”. This motivates attempts to shore up personal finances using falsified claims as a method to gain access to a bit of sorely needed cash.
Aviva is well aware of the climate around insurance fraud – in 2021 they uncovered an estimated 11,000 fraudulent claims, which added up to more than £122 million. The true number is certainly even larger, due to claims which slipped through the net, and those which are still currently under investigation.
This spike is evident across insurance as a whole, including motor, device, property and personal injury insurance.
Insurance company Zurich attributes a 25% rise in their fraudulent property claims to economic pressures in 2022. This adds up to over £40,000 per day fraudulently claimed – but prevented.
And this trend shows no signs of slowing down: financial crime is expected to become “even more prolific” in line with increasing living costs, according to the UK’s Financial Conduct Authority (FCA).
In order to weather this rise in insurance fraud, businesses need to implement robust and intelligent measures to ensure they are no longer seen as an easy target for opportunistic individuals.
So, what is the well-known best practice for preventing insurance fraud, and what hidden tips can businesses use to stay ahead of the rise in fraud?
Insurance Fraud Prevention – The Multi-Layered Approach
Traditional counter fraud measures were limited in their ability to take a preventative approach – largely focussed on identifying and resolving incidents of fraud after the fact. At a time when insurance fraud was less of a universal constant, this would suffice.
Education played a large role in this – training employees to spot patterns and suspicious signs which may indicate fraud, as well as warning customers of the characteristics of certain scams, such as Ghost Broking. This is and has been an effective component of counter-fraud strategy, and is best taken as a part of a broader strategy.
Likewise, having proactive in-house or external investigations teams is a vital element in catching instances of fraud after they have occurred, however they are only as good as the data they have access to.
Insurance companies leave themselves vulnerable when they rely on weak data throughout their fraud prevention strategies, but especially during initial KYC and screening.
It can be tempting to solely utilise credit history to verify that a potential customer is who they say they are, however, as we have highlighted in the past, credit history is less inclusive than alternative data sources.
All these practices are effective and play an important part in fraud prevention and investigations, however what was previously sufficient is increasingly becoming vulnerability in the age of constant insurance fraud. The FCA warns of this, explaining that fraudsters are a “complex and ever-evolving enemy. They will adapt to exploit new weaknesses in the financial system, and they will constantly vary their tactics when targeting the vulnerable for fraud.”
Insurers need to implement data which is just as adaptable and targeted as the approaches of the fraudsters they’re looking to repel.
The Best New Data You Can Add To Your Fraud Prevention Strategy
In the face of these challenges, sophisticated new datapoints can provide a substantial uptick in positive identifications of fraud all throughout the process.
Mobile Network Operator (MNO) data is particularly useful for this purpose.
These datapoints contain useful pieces of information about a mobile phone contract, such as who it belongs to, and recent changes to the device and number. By querying this data businesses are able to access authoritative insights about customers suspected of fraud simply by their phone number.
These insights may appear broad at first, but with intelligent incorporation within underwriting, investigations and claims processing workflows, insurers can deftly sidestep the financial losses of fraud.
While MNO data has broad functionality across verification, authentication and validation, there are a few checks in particular which are especially useful for Insurance Fraud investigations:
Matching name and address:
Using stolen or falsified personal information is one popular technique of committing insurance fraud, particularly prominent with categories of fraud such as Ghost Broking. By verifying if the personal information on the insurance matches with that assigned to the phone number, insurers will be able to identify a large quantity of fraud cases.
Expected SIM and Device ID:
This check is particularly useful for detecting phone numbers which have been compromised. SIM-swap is a method of account takeover which relies on porting a victim’s legitimate phone number to a SIM possessed by the fraudster. By checking expected SIM-ID insurers will be able to identify if a phone number has had unexpected changes which may indicate a fraud attempt.
Recent Activity, and Lost/Stolen Status:
These can be yet more useful signifiers of fraudulent activity. If a device or phone number is registered as lost or stolen, there is a good chance that the person using it may not be who you believe they are. Additionally checking for device activity alongside this is particularly useful for preventing and investigating device insurance fraud, where fraudsters may be claiming a device is lost or stolen, when it’s actually still in possession and use.
How To Implement MNO Data
This requires negotiating and contracting with mobile network providers to access their heavily guarded data. From there, you would have to extract and interpret the data within your own processes.
However, at Phronesis, we have already done this for you. We have cultivated close relationships with the largest MNOs in the UK and France and built a customisable API which allows our clients to get the most of these insights with minimal hassle.
If you’re an insurance provider looking to keep up with the rising tide of fraud in your industry, we’re here to help.
If you’re ready to explore the possibilities of MNO data, lets organise an initial call or demo, we would be more than happy to discuss your requirements and provide any further information you might need.
If you need a bit more time, feel free to ask any questions you might have on our Live Chat feature, or explore the insights on our site.
Let’s work together to safeguard your business from fraud.
