The financial services industry is under continued pressure to create more convenient ways of paying for goods and services. This trend goes beyond common hype. It’s a cultural shift from long-standing payment methods traditionally defined by industry heavyweights to embracing market demands and delivering customer-centricity.

With digitisation, new market entrants and the creation of new channels, such as PSD2, there is increased competition to acquire new market share, retain existing business and, most importantly, to remain relevant. The volume of transactions is incidentally growing at an exponential rate worldwide, fuelled by digitisation, new fintech offerings and, on a larger scale, whole economies, such as Sweden's, opting to operate cash-free. 

Whilst change is certainly a constant in the financial services world, the pace at which it is happening is unprecedented. This is prompting many in the C-suite to re-evaluate their existing strategies and drive growth through innovation. This proactive stance towards market demand presents both an opportunity and a menace.

New payment methods will deliver key improvements with regards to speed, mobility and ease of use, but they will undeniably exert more pressure on fraud detection mechanisms. Unless updated and consolidated with new detection methodologies, these detection platforms are at risk of inviting fraud losses.

A macro view on fraud

Financial organisations have invested heavily in tools and resources to monitor their transactional flux and block fraud in real time. Most financial institutions now claim to use analytics to drive the scoring and decisioning within their fraud solutions. The use of new data sources – such as device information, geolocation or session data – is becoming standard. However, despite the deep level of scrutiny, the reality is that fraudsters are still able to circumvent many of these defences. Are these organisations, therefore, doing enough? 

Fraud practitioners often say that "the devil is in the details." There are still many cases, typically low in volume but high in value, which remain undetected. These do not trigger alerts as the underlying evidence may not seem significant enough. But when looked at through a different lens, they can uncover new modus operandi or large-scale organised fraud. Overlay techniques, such as link analysis or social networks, can help detect these new threats and uncover collusive behaviour by CPP/C (common point of purchase/compromise): fraudulent merchants, fraud around points of sale or compromised ATMs infected by malware. 

Using AI for better fraud detection

The use of artificial intelligence and robotic process automation (RPA) in the industry is still in its early stages but maturing quickly. Organisations can apply RPA to automate transactional rules-based tasks where structured data and predefined parameters are used. If correctly deployed and managed, it can be an effective means to manage operational costs and optimise human resources for more complex or uncommon cases.

Many anti-fraud professionals use AI to extract complex patterns from data to uncover a wide range of sophisticated fraud. Techniques such as ensemble methods and deep learning are gradually making their way from offline data labs into operations. As hardware infrastructure becomes increasingly commoditized, firms are considering AI for dual negative and positive scoring. This means AI can use the same data and transactional engine to promote new value-added services in real time.

Contextual awareness

Many transactional fraud systems, although equipped with analytical engines, suffer from rising volumes of false positives. For example, a transaction made on "Black Friday" out of local time-zone hours for 20,000 Hong Kong Dollars for the purchase of high-value goods could be deemed high-risk and blocked. This scenario potentially breaches several rules for its amount and foreign spending, as well as anomalies compared to common behaviour. But is it effectively fraud? Perhaps the transaction belongs to a frequent business traveller. One who often spends in foreign currencies, where the value might only be a small fraction of available credit limit or account balance. And Black Friday is a popular day for making large purchases.

Organizations use current solutions to look deep into the details and extract relevant intelligence for fraud decisions. But they often miss the bigger picture. In the example above, multiple, potentially overlapping data segments would underpin an optimal fraud solution. And the score would consider several additional factors, such as average disposable income, seasonal trends and average travel expenditure, derived from account information or merchant category codes. Market-leading solutions feature customer signatures, with intrinsic machine learning capabilities aimed at making better contextual decisions. You can use the same approach to defeat growing industry threats, such as social engineering and account takeover.

Is digitalisation a menace or opportunity for fraud detection? Tune in to this webinar to learn how Advanced Analytics and Machine Learning techniques can help banks and payment organisations to detect and prevent fraud faster and more accurately than ever.