Simply put, knowing more about your customers’ behaviour not only results in a better customer experience, it can be used to create a better more targeted set of fraud strategies. Any business can hit these two birds with one well-planned stone, and here’s how.
Formerly, marketing and sales teams have been seen as the collectors and custodians of all information about the customer. Fraud teams also have access to a tremendous amount of data about customers, but this information was used primarily to differentiate actual customers from fraudsters.
Both teams have traditionally missed a big opportunity to collaborate for a richer customer experience. Adding marketing insights around consumer behaviour to fraud strategies will not only create more focused, effective strategies, but will also help marketing teams plan, segment and deliver products and offers with greater success – giving the customer a better outcome too.
Companies try to reduce the anonymity of the online world as much as possible by verifying customers through identity challenge techniques. The reality is that sadly, most identity has been compromised, to the tune of 2.2 billion personal records being exposed as a result of data breaches. According to the Javelin 2016 Identity Fraud Report, one in five data breach victims suffered fraud in 2015 a rise from one in seven in 2014.
Experian’s Fraud Index, which looks at fraudulent applications for a range of financial products, saw an increase in card fraud during April, May and June of 2016. This reached 48 fraudulent applications in every 10,000 applications – the second highest in the 12 quarters since the analysis began. The rate of current account fraud in Q2 2016 was 128 fraudulent applications in every 10,000. This was the fifth consecutive quarter where current account frauds breached 100 in every 10,000. When the analysis first began in Q3 2013, the current account fraud rate was just 48 in every 10,000.
Knowing an individual consumer extends beyond the traditional ‘360-degree view’. It means having knowledge of an individual’s offline and online behaviour – throughout all businesses. It also requires a more expansive view and collaboration across teams within a company, across different industries and businesses within a particular industry. Having this access and insight into the universal customer – down to the transaction level – will be vital for fraud mitigation in the future – and better for providing great customer service.
Move from isolated interactions to fuller, lifecycle relationships
In addition to better cross-functional sharing of information, greater collaboration is needed across teams and processes that are highly susceptible to fraud: Account opening, account access and maintenance, and transactions.
Different internal teams are often responsible for each of these processes, and so these teams operate independently of one another, often with varied solutions and different risk mitigation philosophies. Whilst this is a simplified depiction of business processes, things get more complicated when you look across multiple product types, channels and geographic regions.
Putting the customer at the heart of your business helps to rethink business processes in terms of a customer engagement lifecycle. Adopting this holistic approach encourages the sharing of information across the business processes, which helps to proactively detect fraud earlier at the point of account opening or account access and maintenance, and reduces the vulnerability of financial loss later at the point of transaction.
Sharing customer information across the processes within the lifecycle can reduce customer friction caused by continuously verifying routine account activities, and save on capital and operational expenses caused by increased fraud investigations. That’s a win for the customer as they benefit from increasingly frictionless experiences, and a win for the business as it faces and beats the costs of managing, and the losses, from fraud.
Reduce customer disruption and manage risk: Apply right-sized fraud solutions
The ratio of disrupted legitimate traffic to actual fraud attempts is 30 to 1. As purchases (and fraud) move increasingly online, the rate of challenges to legitimate transactions will increase further, risking sever customer irritation – if nothing changes.
To reduce customer disruption and appropriately manage fraud risk, companies need to apply fraud mitigation strategies that reflect the value and level of confidence needed for each transaction. This is called right-sizing the fraud solution. This approach, when aligned with the company’s fraud rates and commercial strategy, increases the likelihood of catching fraudsters without disrupting the business of (and relationships with) legitimate customers.
Use service-based models to achieve agility and scale
Service-based fraud models offer the benefit of skilled expert analysis, regularly updated to respond to fraud threats or incidents than can often protect before fraud happens. These service-based fraud models also adapt and scale to support the business, no matter how fast the volume growing, or where businesses decide to pursue customers. Great for high growth businesses on the up!
Companies need to be as nimble as fraudsters are, with fact access to the right tools and data whenever it’s needed. The current approach of adding new tools on top of existing ones is creating complexity that is expensive to integrate and difficult to manage. Businesses need flexibility and scale to get more out of what they have and the ability to connect the best solutions available.
New technologies for fraud and customer management
Machine learning is a significant breakthrough in helping companies move from reactive to predictive one by highlighting suspicious attributes or relationships that may be invisible to the naked eye – but indicate a larger pattern of fraud when viewed in aggregate. The great value of machine learning is the sheer volume of data that computers can analyse that humans cannot, thanks to that variety of pattern recognition algorithms. With machine learning, a business can add exponentially more data to its analysis, and improve the insights into the customer base.
Unsupervised machine learning techniques, also known as anomaly detection models, look for aberrations in the patterns of a transaction flow. These deviations may indicate fraud, or may simply be a change in global behaviour (what ‘normal’ looks like). They are a more difficult tool to use, but can be layered into an integrated technology strategy for fraud and customer management.
Armed with greater knowledge and agility, shared appropriately across teams, companies can become a much tougher target for fraud AND offer a better experience to existing and new customers. Getting there requires an expanded view of the consumer, and collaboration between the product development, marketing, fraud teams and third parties. As the online world shows us, choice and collaboration can be easy within a business as it is for consumers. A collaboration driven by the shared goal of growing a sustainable business and protecting ambitions against rising fraud threats is the smartest way to hit two very tempting targets with one collaborative project, creating a double benefit for the bottom line.
By Nick Mothershaw, director of ID & fraud solutions at Experian