BPM Analytics

The role of predictive analytics and machine learning in enhancing fraud management

According to a report from the Association of Certified Fraud Examiners (ACFE), an average fraud costs an organisation over $1.5 million.

However, financial losses are not the only thing that impact companies in cases of fraud. They also hamper customer experience, brand value, company goodwill, and operational aspects, among other things.

On that note, here’s looking at how intelligent technologies like artificial intelligence (AI), machine-learning (ML) algorithms, and predictive analytics can facilitate fraud detection, management, and prevention.

In this blog, we will discuss:

  • Why businesses find it tough to manage fraud?
  • The benefits of using ML and predictive analytics in fraud detection
  • How predictive analytics and ML improve the fraud detection process?

Why do organisations find it difficult to manage fraud?

Despite accounting for potential frauds and having quality security measures and firewalls in place, many businesses, small or big, fall prey to poor fraud management. Here are a few reasons why this happens:

  • A siloed approach to fraud management at both the regional and the global levels.
  • Ownership spread across different business functions instead of being managed by one single entity.
  • Analysis based on people’s limited knowledge and personal experience, as well as the absence of standard methods to quantify losses in cases of fraud.

These factors, along with limited data analysis, create an undesirable system that is fraught with vulnerabilities.

Automation paves the way

Did you know that 83% of North American businesses conduct manual reviews (29% of orders are reviewed manually)? Human intervention in processes can help identify fraud patterns and analyse customer behaviour. However, it can be costly, time consuming, and can lead to false negatives (or false positives).

Manually intensive processes can be difficult to scale up since processing large datasets demand more manpower and time. Instead, deploying technologies like ML, data mining, and predictive analytics in such scenarios can work efficiently to identify, predict, and act upon potential frauds.

Since the machines can take over repetitive tasks, it is possible to program them to highlight escalations and drop an alert to you in case of potential fraud. After all, you are automating almost every other aspect of your business, aren’t you?

For organizations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed on organizational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive, yet modular suite of services is doing exactly that. Equipping organizations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organizations that are innovating collaboratively for the future.

Why use ML in fraud detection?

Cognitive computing and ML technology use complex algorithms that analyse data patterns, making it easy to detect spam, recommend products, and use predictive data analytics to manage fraud.

The top benefits of using ML and predictive analytics in fraud management are:


ML can help identify anomalies in real-time, minimise security threats, and enable businesses to respond quickly to fraud occurrences.


Once you train the machines on which transactions in the past were authentic and the ones that were fraudulent, the systems can further scan through large datasets and categorise them accordingly. ML algorithms and predictive models get better with expanding datasets.


You can take selected sets of variables known to have been involved in past fraud events and place those variables into processes to determine the likelihood of future outcomes being fraudulent or not.

How predictive analytics and ML improve the fraud detection and management process?

Fraud detection is no longer static, limited to place or time. Companies now take a proactive (rather than a reactive) approach with third-party vendors to build fool-proof channels of communication and data exchange.

  • When deployed at multiple touchpoints, predictive analytics can enhance a company’s fraud detection capabilities in dealing with new transactions or interactions.
  • Using ML and predictive data analytics can help business leaders make sense of data and convert it into actionable insights or recommendations to make both reactive and pre-emptive decisions.
  • Predictive analytics can also spot structural weaknesses in the system, such as identifying loopholes in cybersecurity and fraud detection, as well as checking for vulnerabilities in a system’s performance and helping fix them.
  • A robust fraud management system that includes predictive analytics and ML offers a secure and better customer experience.
  • It maximises revenue and reduces costs.

How Infosys BPM can help?

At Infosys BPM, we use predictive intelligence and business analytics along with industry-standard platforms to help you assess risks, identify frauds, analyse patterns, and develop a pre-emptive approach to avoiding or managing frauds. Our comprehensive set of fraud detection and prevention analytics offerings bring in deep domain, data science, data, and visualisation capabilities to develop and deploy fraud solutions tailored to drive business outcomes.

Read this case study.
Learn more about our fraud solutions and capabilities.

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