Financial crime affects institutions of all sizes from the largest, multinational investment firms to the smaller regional banks. And one of the biggest issues that all financial institutions face is fraud. Fraud affects more than just the banks’ customers; it fundamentally affects a bank’s bottom line and can nearly destroy its reputation. In the first half of 2018, there was over a million incidents of financial fraud from cards, checks, and remote banking totaling losses of £358 million in the UK alone.
As fraudsters continue to evolve in our connected digital world, banks have started to transform how they prevent fraud by making the most of advanced machine learning (ML) and streaming analytics to stem the tide of digital crime. They are using these new techniques to battle the large number of challenges that financial institutions face when it comes to financial crime, such as:
- Criminal activities: Criminal techniques are becoming ever more sophisticated
- Data growth: The growing number of customer contact channels generates huge volumes of data points that financial institutions have to continuously track
- False positives: Rules-based detection systems are slow to update and only apply a rigid filter to transactions. The result is an excess of legitimate transactions unnecessarily flagged for investigation (false positives)
- Inconvenienced customers/customer churn: Investigations take too long, leading to customer dissatisfaction and churn
- Regulatory demands: Sophisticated ML techniques can be difficult to explain to regulators so institutions may be forced to use less sophisticated (but explainable) techniques that generate more false-positives
In order to combat some of these challenges banks face in fighting fraud, it is critical for financial institutions to understand the risks and opportunities they face in real time. Organizations already have the data that makes this possible but are unable to reach or wrangle that data quickly enough. The financial services world must make better use of its real-time data to build up better defenses and reduce fraud losses by harnessing the incomparable speed and insight delivered by advanced machine learning and streaming analytics.
With the help of real-time analytics and machine learning, financial institutions can:
- Monitor transactions in real time so you can catch fraudulent transactions as they occur
- Expedite the investigation process
- Make the right decisions quickly
- Decrease the likelihood that fraudulent transactions will be approved
No matter your institution’s financial crime challenges, data science and machine learning can help you get ahead of them. Harnessing the power of real time data analysis can help you more effectively address fraud to better monitor transactions and make decisions quickly.
By Alexa Phillips -April 10, 2019