With more and more businesses moving online, fraudulent activities have grown in sophistication. As technology continues to advance, traditional security measures such as siloed CAPTCHAs and Web Application Firewalls (WAFs) are no longer sufficient to fight against the advanced methods used by fraudsters to exploit vulnerabilities and perpetrate financial crimes.
According to the recent survey conducted by PwC on Global Economic Crime and Fraud, more than 50% of organizations admitted that they experienced fraud and other economic crime in the past two years, amounting to total losses of 42 billion USD, the highest in the last 20 years of research. Beyond financial losses, companies also experienced potential erosion of the market share, brand reputation, customer trust and loyalty, and increased scrutiny from government bodies, regulators, and law enforcement agencies. Such massive losses are forcing C-suite executives to explore modern solutions to detect, prevent, and eliminate fraud.
Herein lies the promise of Machine Learning, a disruptive technology that can not only help enterprises proactively detect, prevent, and eliminate fraudulent activities but also revolutionize the global fraud detection and prevention market, which is estimated to reach 129.17 billion USD by 2029 exhibiting a CAGR of 22.8% during the forecast period. This may be the reason why CTO, CIOs, and other decision-makers are turning to Machine Learning for fraud detection. Let’s dig deeper to understand the role of Machine Learning in fraud detection.
Machine Learning vs Data Mining
Why Use Machine Learning for Fraud Detection
Machines are way better than humans and can easily detect fraud by applying cognitive computing technologies to unstructured data. Now, let’s look at some key reasons to use Machine Learning for fraud detection.
1. Better Predictions with Large Datasets
Unlike humans who struggle to handle voluminous datasets, Machine learning algorithms can process large volumes of raw data to identify potential frauds. The more data is fed into a Machine Learning model, the more it will learn and make better predictions. This way, ML algorithms improve accuracy in finding patterns and insights hidden in large datasets for better forecasting.
2. Reduced Manual Effort
By automating fraud detection using ML models, companies can not only reduce the total time spent on manually reviewing datasets but also free up seasoned data analysts to focus on more strategic tasks that drive the bottom-line. The best part is that machines can analyze all the data points round-the-clock without getting overwhelmed or burnt out, unlike humans who can work for a limited number of hours and analyze datasets to extract insights for fraud detection.
3. Real-time Detection
ML-powered fraud detection systems can empower businesses to act swiftly to detect fraudulent activities in real-time and mitigate potential losses before they escalate.
As technology continues to evolve, the battle against fraud has become more challenging since fraudsters are constantly evolving their tactics. The good news is that Machine Learning models can help enterprises safeguard their critical data in the evolving landscape of cybersecurity and financial crimes. The Machine Learning models can learn from new datasets and adapt their cognitive capabilities to keep up with potential frauds.
5. Data-Driven Insights
Using Machine Learning algorithms, enterprises can not only uncover meaningful and actionable insights into emerging threats but also bolster their security measures to identify, prevent, and eliminate fraud.
The initial setup of ML-based fraud detection systems may require some investment but it can provide significant cost savings in the long run by reducing losses caused by fraudulent activities.
Industries to Look Out for Machine Learning Disruption
Implementing Machine Learning in Fraud Detection
Given below are the strategies that enterprises may consider for successfully implementing Machine Learning in fraud detection:
I. Data Collection and Preparation
Collect historical data encompassing both legitimate and fraudulent to build a robust dataset. Always make sure that collected data is accurate, exhaustive, and properly preprocessed to train Machine Learning models and fuel the learning process.
II. Feature Engineering
Extract relevant features from the datasets that empower Machine Learning models to effectively distinguish between legitimate and fraudulent activities.
III. Model Selection
Choose the right Machine Learning algorithms based on the nature of collected data and the specific fraud scenarios that enterprises need to detect.
IV. Training and Validation
Train Machine Learning models using labeled data and validate the performance against industry-recognized metrics.
V. Real-time Integration
Enable real-time monitoring and response by seamlessly integrating the Machine Learning models into the existing fraud detection infrastructure.
VI. Continuous Monitoring and Improvement
Regularly monitor Machine Learning model performance and iteratively improve algorithms when new data is available.
Incorporating Machine Learning into fraud detection has become the need of the hour. By leveraging the potential of ML algorithms, models, and data-driven insights, businesses can not only stay ahead of emerging fraud tactics but also safeguard the company’s reputation, customer loyalty, and assets. Embrace the disruptive potential of Machine Learning in fraud detection to position your business as a stalwart against the evolving landscape of fraud.
Case in Focus
The client is a leading provider of insurance, investment, and banking solutions serving millions of customers globally. With the rise of digital transactions over the last few years, the client started facing challenges in identifying fraudulent activities. To overcome the challenge, the client partnered with Damco Solutions and decided to integrate Machine Learning into their fraud detection strategy based on the consultation provided. By embracing Machine Learning, the client improved the accuracy in identifying fraud by over 95% and increased operational efficiency by 57%.