Fraud prevention has been lately generating a heated debate in the insurance and banking industries. Financial institutions struggle with different forms of fraud, which causes great losses for organizations. Unfortunately, the number of illegal activities is growing rapidly, fraudsters reach for whatever techniques to succeed in their goal. Organizations try to protect themselves, but these defense mechanisms should preferably not (negatively) influence the general customer experience. The challenge is to find those solutions that enable financial institutions to fight fraudsters, meanwhile ensuring even higher customer satisfaction.
Financial industry fraud trouble increasing
The Insurance Europe Federation claims that in 2014 insurance companies paid out €949 billion in claims and benefits. Taking into account the number of acts detrimental to companies dealing with insurance, the losses dealt to insurers are becoming a serious issue.
According to the Coalition Against Insurance Fraud estimate from 2014, fraud generates a loss of $80 billion a year across all line of insurance. Fraud accounts for 5-10 % of claims costs for U.S. and Canadian insurers. Nearly 30% of insurers state that fraud was as high as 20% of claims costs.
In the UK, the Association of British Insurers released a report, according to which insurers detect 350 frauds worth £3.6 million a day. What is more, in the years 2009-2014 the overall value of frauds detected has risen by 47%.
Numbers indicate that the problem of insurance fraud is rising rapidly. About half of the insurers suggest that they do not feel adequately protected against fraud. Dishonesty of some insurance clients causes great losses for insurance companies.
Fraudulent activities
The most common type of insurance scam detected in the United States and the UK are personal-property frauds. In Poland 41% of frauds occur in life insurance.
In insurance industry techniques that are most frequently used by fraudsters are:
- Making an agreement to extort benefits
- Making an agreement to extort commission
- Crime against documents
- Misappropriation of premiums
In connection with the massive falsification of the information necessary to verify the premiums and the evaluation of risks associated with insurance, insurance companies suffer losses. A special case are fees extortions. They rely on the appropriation of commissions by insurance agents and intermediaries. This applies mainly to contracts for regular savings, as fees are paid after the first payment of the customer. The value of the commission often equals the value of the first year contributions.
The insurance industry is not the only one to struggle with fraud. Scams are also widespread in the banking industry.
According to the Kaspersky Lab report from 2014, 60% of reviewed companies from 26 countries admitted that they were victims of cybercrime.
Representatives of the companies affirm that direct loss of founds due to cyber-attacks is the main security problem. Online fraud is seen as complex and difficult to prevent even by the banks. The major difficulty occurs in distinguishing fraud from legitimate transactions and often do not appoint personnel responsible for mitigating attacks.
Bank Card Phishing and Card trapping are the techniques that involve illegal data acquiring by the imposter. The data consists of credit card number with other significant information, needed to complete web transactions. Subsequently the data will be used for the theft of funds.
New technological solutions can be used for the counterstrike
Being smarter than the fraudsters is the new motto for the financial services industry. This means being able to predict potential fraudulent behavior. Increasingly, clever algorithms are dealing with this challenge. The disadvantage is that these algorithms need human intervention and adjustment. They are not sufficient to beat innovative fraudsters.
Real time analyses of big data will be needed to be able to detect potential fraud on the fly. The computing power to manage these continuous analyses in real time has been a challenge, but it isn’t any longer. Cloud based computing and machine learning will provide support in detecting fraudulent activities.
A wide range of illegal techniques used in fraud, together with complicated data verification processes in insurance and banking industries, forced the need to create a solution that will effectively detect fraud and optimize business processes operating in these sectors.
Cloud based computing and machine learning can solve the problem of the big data analysis automatization. Operations such as link analysis, anomaly detection, predictive modeling, text mining and data visualization could be performed quickly and easily.
Technology, together with constantly expanding knowledge will make it possible to create new software solutions that will deliver the biggest value to the clients and will enable development in areas that require meticulous and intensive data analysis.
Anti-fraud technology is rising
More financial organizations see a rise in ROI, and more frequently rely on technology to counter emerging threats such as underwriting scams, money-laundering and cyber fraud. Main fraud-fighting priorities are detecting fraud before claims are paid and upgrading the risk analysis during acquisition process.
The most important factor that is crucial to perform counter actions is collecting big amounts of relevant data. Sources of information that can be useful in calculating the risk are:
- Internal data
- Industry fraud-watch lists
- Public records
- Unstructured data
- Third-party data aggregators
- Social media data
- Data from connected devices
The key is to collect big data, analyze it and construct anti-fraud systems.
Anti-fraud systems drive growth in customer satisfaction
Building customer trust and loyalty is crucial for financial institutions. Customers can choose from a huge number of various offers, so it’s hard for them to gain competitive advantage.
A way to differentiate from the competitors is to ensure the safety of the customers by the use of technology against fraudsters.
According to FICO organization, anti-fraud systems are a crucial tool for customer satisfaction management. They guarantee faster and more precise risk analysis during the acquisition process and they support the security of credit card or online banking usage by the customers. Systems can be defined as a virtual fraud analyst, who is always watching customers’ back.
Virtual fraud analyst ensures the correct reaction in the right time and in a proper way of communication that is not burdensome for the customer. It consists of 5 simple to understand steps.
Firstly the process of detection occurs, in which the system measures the score of the transaction.
At this stage, the system measures the risk associated with the transaction.Secondly, the system assesses the score with the customer activity, basing on the historical data of an individual or even a group of similar clients. This ensures that the risk is calculated individually for the customer and that the case can be analyzed further.
Thirdly, all the information gathered is used to decide, whether the customer should be notified about the potential threat, or the system can cope on its own, without letting the customer know.
Furthermore, when the system decides that it needs the customer’s intervention it will intervene and contact the customer in the most suitable and convenient way.
Finally, basing on the customer’s feedback and information transferred, the system resolves the existing issue and prevents the client and the institution from losses.
The anti-fraud systems described above can satisfy customers in a number of ways. They make it possible to deal with a fraud in seconds. In some cases customers even will not be notified about the potential fraud. They will not have to waste their time reaching the fraud line in the contact center. They will be informed on the spot, and they will be able to react as quickly as possible.
To sum up, according to FICO studies, 89% of customers stated that they feel more secure after implementing security features based on anti-fraud systems.