The amount of companies that reported they’d been a victim of financial fraud grew 13 percent over the last year to roughly 49 percent total, according to the PwC’s 2018 Global Economic Crime and Fraud survey.
It’s unclear whether that rise is due to the fact that more businesses are aware of the emerging threat of fraud, or simply because perpetrators are committing it more often. It’s a costly trend for financial institutions: 64 percent of respondents saw losses totalling at least $1 million due to the crimes, the report found.
Regardless, companies are trying to mitigate the risk before it has a chance to develop: nearly 22 percent of organisations in developed markets and 27 percent in developing markets are adopting artificial intelligence specifically to counter fraud.
Challenges facing modern fraud
Machine learning gives financial institutions the tools to combat the traditional barriers to effective fraud detection. Namely, the wide array of sources that contribute to unbalanced data – or, in other words, information that’s incomplete because of sample size.
Humans like routine, and that extends to their purchase history. Banks are left with high volumes of data, especially if they’re following best practices and collecting information through individual customer lifecycles. But this creates a challenge; within the sea of transactions are a small number of fraud cases.
Given the irregularity of the crime in contrast to the incredibly large data set analysts are working with, it gives rise to false-positives and makes it difficult for artificial intelligence to be effectively trained to combat the issue.
Machine learning algorithms empower banks and other institutions to leverage human nature by employing the help of user-behaviour analytics.
How smart financial institutions are gaining an edge
The financial industry is moving towards using a wide array of sources to help with fraud detection, but only organisations that use powerful data science platforms will see results. Traditional practices that rely on manually setup of alerts, simply can’t keep up with the continuously changing behaviour of the fraudster, jointly the growing volume of transactions.
In response, many organisations have adopted dedicated decision-support systems (DSS) that use a series of algorithms to help analysts manage the influx in data, and make corresponding judgements from it.
Dedicated DSS that use machine learning can reduce the average workload for analysts, allowing them to focus on the most suspicious transactions. This enables them to quickly respond based on alerts sent by the platform, stopping the purchase before it can take a toll on the firm’s finances. At the end of the day, this is the only way to cut back on the amount of money that’s lost due to fraud.
Successful fraud detection projects rely on effective project managers that can help their teams tackle the following needs:
Creation of historical datasets that contain both legitimate and fraudulent transactions.
Gathering of associated data: For instance, about traffic patterns on the payment platform that can help detect abnormal behaviour.
Ability to modify machine learning algorithms to overcome challenges of unbalanced data.
Functionality of a highly effective machine learning platform.
Machine learning in action
Statwolf’s data science project managers worked with an online payment facilitator to create a dedicated DSS to help identify fraud. The team designed features that could capture historical behaviour for individual users to support informative feature extraction and data balancing.
It relied on our team building a machine learning algorithm that ranks every case of fraud based on confidence in prediction. It’s a methodology that offers analysts the ability to take the most suspicious cases first – depending on whether it was an actual crime, the models can learn and improve further as they sift through more data.
On the road to better fraud detection
As criminals continue to innovate their strategies, machine learning is helping financial institutions gain back the upper ground.
At the heart of effective strategies are two underlying components: experienced project managers and intuitive data science platforms. With so much at stake, banks and other organisations should be sure they have trustworthy and capable providers of both.