Fighting fraud with big data

platform fraud finance professional

The financial services industry is one of the most vulnerable sectors to fraud and economic crime. The most recent Crime and Fraud report by PwC shows that more than half of financial services companies worldwide have experienced fraud in the past two years, with an average financial impact of $1.4 million per incident. Cybercrime is the most common and disruptive form of fraud for the financial sector, but there is a new form of cyber-enabled fraud that requires increasing attention and action: platform fraud.

What is platform fraud?

Platform fraud refers to the misuse of digital platforms, such as online marketplaces, social media, peer-to-peer networks or mobile applications, to conduct or facilitate fraudulent transactions or activities. Platform fraud can have different objectives, such as stealing money or data, money laundering, evading taxes or manipulating markets. This type of fraud can have different victims, such as consumers, businesses, governments or even financial institutions themselves.

The role of big data

To fight fraud effectively, financial institutions need to use big data. Big data refers to the vast amount of structured and unstructured data generated daily from various sources, such as transactions, customers, social media, devices and sensors. Big data provides a wealth of information that can be used to detect fraud patterns, identify anomalies and assess risks.

Benefits of big data preventing platform fraud

  1. Holistic insight
    One of the main advantages of big data for preventing platform fraud is that it provides a holistic view of the behaviour and activities of customers and entities. By analysing big data, financial institutions can better understand the normal and abnormal patterns of their customers. This allows them to detect suspicious transactions or activities that deviate from the expected profile.
  2. Speed and accuracy
    Big data improves the speed and accuracy of fraud detection and prevention. Advanced analytical techniques, such as machine learning and artificial intelligence, can process big data in real-time and automatically generate alerts or take actions when a potentially fraudulent situation is detected. This reduces the number of false positives, lowering operational costs and increasing customer satisfaction.
  3. Compliance and monitoring
    Big data facilitates compliance with increasingly stringent anti-fraud regulations and reporting requirements. It enables financial institutions to proactively respond to new or emerging fraud risks by constantly adapting and improving their systems based on the latest data and insights.

Integration of technologies

Big data is a powerful tool on its own, but it can be used even more effectively when combined with other technologies in the financial sector.

RPA and intelligent automation

One such technology is Robotic Process Automation (RPA), which can automate routine and repetitive tasks using software robots or digital workers. Another technology is Intelligent Automation (IA), which combines the use of RPA with artificial intelligence (AI) and other cognitive technologies.

Strengthening big data with RPA and IA

RPA and IA can support and complement big data in preventing platform fraud in several ways:

  1. Improved data collection
    RPA and IA can help collect, extract, validate and integrate more and better data from different sources, such as internal systems, external databases, websites, emails or documents. This improves the quality and quantity of data available for big data analysis, leading to more accurate fraud detection models.
  2. Accelerated processing
    These technologies accelerate and scale big data analytics by processing, transforming, modelling and visualising data faster and more efficiently. This allows financial institutions to respond faster to fraud and reduce potential losses.
  3. Enriched analysis
    RPA and IA enrich big data analysis by generating new insights, patterns or predictions from the data. They can also enhance existing insights with additional data or feedback. This increases the effectiveness and efficiency of anti-fraud strategies.

So all in all, big data is a powerful tool that can help the financial sector fight fraud more effectively. By using big data, financial institutions can improve their anti-fraud strategies by collecting more information, detecting and preventing more quickly and accurately, and complying with regulations and supervision more easily.

Mutually-reinforcing technologies

However, deployment of big data need not stand alone. By integrating RPA and IA, financial institutions can take their anti-fraud efforts to the next level. These technologies enhance big data by improving data collection, speeding up processing and enriching analysis.
Want to know more about how RPA and IA can help your organisation fight platform fraud? Then get in touch with us, our experts are happy to talk to you.

This blog was also published by Computable.

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