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How machine learning helps with combating financial fraud

Fraud is an ever-lasting problem for banks and other financial institutions, which only continues to persist. As we are moving toward ubiquitous digitalization, criminals are discovering new weak spots in financial digital applications.

Paradoxically, the technology works both ways: it helps firms to provide better customer experience and optimize operations and, at the same time, assists cybercriminals in carrying out more sophisticated illegal schemes. Moreover, fraudulent actors have learnt to collaborate, share data and techniques, making financial institutions understandably paranoid about the slightest deviations in their customers’ activities.

Rule-based mechanisms and manual analysis are not scalable, flexible or reliable enough to combat this nascent problem. This calls for a solution that will be adapting to fraudsters’ ever-changing tactics and increasing complexity of financial cybercrimes. For this reason, data science consultants turn to machine learning (ML) solutions to automate fraud detection. It’s time for ML to finally take center stage in helping firms to detect and prevent fraud as fast as it’s performed.

Time for a data-driven change

Although conventional rule-based algorithms for fraud identification have proved to be somewhat effective, it’s essentially just a pre-programmed set of what-if rules that can only recognize already existing fraud patterns. This makes fraud-analysis results binary: the system can label a case as fraudulent or authentic, which doesn’t allow for more in-depth analysis. However, the bigger problem is that fraudsters quickly adapt and change their methods, which makes the rule-based approach obsolete each time cybercriminals upgrade their techniques.

Nowadays, financial firms produce staggering amounts of data. This makes for a perfect playground for big data and ML tools to shine. With behavioral analytics based on historic and real-time data, it becomes possible to detect customers’ behavioral anomalies and consistently detect novel fraud patterns. ML-based systems pull data from constantly changing data sets and can find hidden connections, which makes it possible to detect often subtle fraudulent activities. Big data software allows banks to cross-reference a plethora of data points such as logging frequency, device type, location as well as more sophisticated metrics such as customers’ typing speed.

As banks are seeing the increasing importance of security, even the least suspicious activities are getting investigated. Even if it seems to be a step in the right direction, it may lead to another major problem of false positives. With erroneously set fixed threshold values and ambiguous demographic metrics, a 90% rate of false positives has unfortunately become the norm for banks, especially when it comes to anti-money laundering alerts. The outcomes are what you expect them to be: friction-heavy customer experience, business reputation damages and ineffectively allocated resources. ML can have a tremendous impact by enabling organizations to analyze customers’ activities smarter, faster and more accurately.

How exactly can ML help with fraud detection?  

Currently, software vendors offer a variety of tools, off-the-shelf but also custom ones, to streamline fraud detection and reduce false positives.

ML can contextually analyze and prioritize cases that deserve immediate attention of senior investigators, eliminating waste of human resources and, more importantly, lessening fraud scheme impact.

It’s not uncommon for banks to frequently review high-risk accounts, which however proves ineffective more often than not. Government watch-lists, news outlets, and sanction lists are constantly monitored by banks, and databases of suspicious customers have to be continuously reviewed to ensure the highest level of security. With AI and ML, most of these processes can be automated: modern NLP applications can generate insight even based on unstructured text. Deep learning-powered tools can automatically gather data about an entity that has triggered the alert. These methods allow for more accurate and faster analysis, while also saving human effort.

Many payment services don’t have access to any customer data, which is a major roadblock for investigative purposes. However, ML-powered applications can pull real-time data each time the authorization occurs and cross-check it with other financial institutions. This is exactly how IBM Safer Payments helped STET to save 100 million USD annually.

Final thoughts

Traditional methods of exposing fraudsters’ fast-changing and inventive tactics are simply outdated, time-consuming, and often inaccurate. The financial industry needs a major technological transformation to outpace fraudsters. Data-driven technologies like AI and ML are the key enablers of this change.

Interestingly enough, major government agencies and regulators are encouraging banks to experiment with innovative approaches to fraud detection. In 2018, five US financial agencies including the Federal Reserve issued the statement that supported banks in implementation of AI-based technologies for combating financial fraud.

It’s just a matter of time for these emerging technologies to be adopted by the majority of banks and financial institutions. Those who fail to implement them will become easy targets for fraudsters and won’t be able to stay afloat in the long term. It’s time for banks to collectively embrace the AI-based technological innovation to finally force scammers change their occupation.



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