Supercharge Your Fraud Detection with An Optimized ML Pipeline

October 28, 2021

With payments going digital, online shopping skyrocketing, and more people working from home, the opportunities for fraud are bigger and better than ever. Year after year, fraudsters scam millions of customers, swipe billions from banks, and cost businesses an eye-watering amount of revenue. 

One 2020 survey found that 47% of companies have experienced fraud in the past 24 months, totaling over $42 billion in losses. This concerning figure is predicted to rise as criminals get savvier and use the latest tech to sharpen their operations. 

Corporations like Amazon, Airbnb, and Yelp have long relied on artificial intelligence to weed out fraudulent accounts, bogus rental listings, and fake reviews. Most organizations, however, don’t have the resources to build a bulletproof AI pipeline for efficient fraud detection. Instead, they’re often saddled with slow, tedious tech stacks and frustrated data science teams who struggle to productionize their machine learning (ML) models to keep up with rapidly-evolving fraud trends.

To stay ahead of these trends, here are the most common types of fraud and how organizations of any size can quickly (and affordably) optimize their ML operations for enhanced fraud detection.

Most common (and costly) types of fraud

When most people think of fraud, they typically imagine an elderly person volunteering their bank information to a complete stranger over the phone, or clicking on a glaringly obvious phishing link in an email from “Netflux”. 

In reality, fraud can be much more insidious and impacts businesses of all types in a variety of ways. Here are some common types of fraud every business needs to be aware of: 

Identity theft: Since the pandemic moved everything online, identity theft has soared, affecting a concerning 86% of consumers worldwide. Frauds can capture a customer’s personal and financial details through phishing, decoy forms, or breaching a business’ database. Then they go on a spending spree using the customer’s credit cards, take over their online accounts, and open accounts under their name for new credit cards or loans.

Payment fraud: Increased online transactions mean more opportunities for payment fraud, with global losses predicted to reach an annual $40.62 billion by 2027. For businesses, payment fraud ranges from fake payment cards to duplicate invoices sent to different branches of the same company for a double payday. A popular one is “authorized push payment fraud”, or APP, which involves tricking an employee into wiring a transfer. In 2019, even Google and Facebook employees were bamboozled into paying around $100 million in fake invoices.

Insurance fraud: The FBI estimates the total cost of insurance fraud to be more than $40 billion per year. Fraudsters can file claims using false information, submit duplicate claims, or overstate the cost of compensation. Up to 10% of U.S. health insurance claims are fraudulent, competing with other top hits like car insurance scams, intentional property damage, and fake unemployment claims. 

Money laundering: This continues to be a widespread crime that costs the world around $2 trillion every year. Criminals pass “dirty money” gained from illegal operations through a series of banking transfers or commercial transactions to “clean” it. Although most businesses have systems that sound the alarm over unjustifiably large transactions or irregular activity, it’s still notoriously difficult to detect. 

Ransomware: Possibly one of the most nightmarish forms of cybercrime. Frauds sneak their way into a business’ database then hold it ransom until the business pays to get their data back. Ransomware has spiked over 130% in 2020, mostly targeting small businesses. Not that corporations are immune, considering LG Electronics fell victim earlier this year and lost 50GB of sensitive data when they refused to pay up.

How machine learning supercharges fraud detection

Fraud is an escalating threat with climbing costs. It may seem inescapable, but in the same way that locking your front door deters thieves from breaking in – having a solid fraud detection system reduces your company’s risk of becoming a victim. 

Machine learning has become a powerful player in the fight against fraud. American Express uses ML to monitor trillions worth of transactions a year, boasting the lowest credit card fraud rates in the industry. Similarly, Chase Online Banking relies on ML to accurately pinpoint fraud attempts, reducing their losses by 50% over the last five years.

Companies around the globe are betting on ML to detect and counter fraud faster and more precisely than any five-star team of analysts. Here’s what ML does to make that possible:

  • Recognize fraudulent patterns faster: Fraudulent transactions tend to follow certain patterns that set them apart from legitimate ones – like multiple online purchases using the same card but different IP addresses. ML integrates data scattered across dozens of sources to learn and detect patterns faster than any human possibly could. It then prioritizes suspicious cases and predicts future risks based on historical data and advanced analytics.
  • Accurately identify anomalies in behavior: ML algorithms are exceptionally good at picking up minute details in oceans of data. They can detect subtle anomalies that a human analyst might miss, like slightly higher bank transfers or misspelled information on a loan application. Using all the available data, ML can gain a deeper understanding of customer behavior, pinpoint potential fraud, and avoid false positives that might disrupt the customer experience. 
  • Process data in real-time: Fraud happens in real-time and requires real-time data analysis and detection to be effective. For example, if someone is attempting to make a purchase using a stolen credit card, you want to catch the attempt and decline the fraudulent transaction before it happens. ML instantly analyzes all incoming data to uncover new indicators of fraud, and can automatically update its models to stay ahead of novel attempts.

The challenge of slow, complex ML deployment systems

The challenge isn’t just building an effective response to fraud, but operationalizing your ML models in the first place. Most production AI platforms can’t handle the tremendous volumes of data and fast-paced analysis needed to keep pace with today’s digital environments. This creates a familiar set of restrictions: 

  • Blocks rapid experimentation: If only it were as simple as dropping ML models into production and forgetting about them. A huge part of a data scientist’s job is to experiment, retrain, and improve the accuracy of their ML algorithms to stay ahead of new fraud tactics. This goes hand-in-hand with continuous monitoring to let data scientists catch concept drift early on and avoid erroneous fraud predictions based on old data.
  • Unable to run models in real-time: New forms of attack are popping up every day, so to keep up with fraud, your ML models need to have access to the latest available data. This means processing data in real-time, except most AI platforms can only handle periodic processing (e.g. every two weeks). While it waits, your models miss vital information that could’ve detected a new fraud pattern before it became a problem. 
  • Too complex and expensive to scale: Frequently retraining and redeploying models in real-time demands a large amount of infrastructure and engineering overhead — which not every organization has the luxury of funding. Clunky ML pipelines add extra time and effort for your data science team, as well as mounting costs for your accounting department. This inevitably delays progress and keeps you dangerously behind faster-moving frauds. 

How Wallaroo can accelerate your ML fraud initiatives

When it comes to developing and training fraud models, data scientists can choose from a generous pool of powerful and open-source frameworks, such as TensorFlow, Scikit-learn, XGBoost, and others. However, to deploy those models into production against live data, organizations typically have to first invest in large data engineering teams and sprawling infrastructure – to the tune of millions of dollars a year. 

What’s more, if you’re in the early stages of your AI journey, it can take 1-2 years simply to build and develop the right teams, skills, and infrastructure that makes ML production possible. 

This is where Wallaroo, an enterprise platform for production AI, comes in. Wallaroo acts as a rocket booster for your data science projects and helps your team get ML models into production faster, with much less hassle, and at a far lower cost. 

With all the tools your team needs for simplified productionizing, experimentation, monitoring, and scaling — Wallaroo swiftly turns your ML models into just-in-time insights so you can protect your business around the clock.  

Here’s what Wallaroo can do for you:

  • Deploy ML models into production within seconds.
  • Allow data scientists to swiftly experiment, retrain, and redeploy their ML algorithms for continuously better-performing models.
  • Analyze data up to 100X faster using fewer resources to cut infrastructure and maintenance costs by up to 80%.
  • Support periodic processing and real-time data streaming to keep up with new threats.
  • Provide detailed intelligence reports and real-time analytics so you can react to fraud attempts as they happen.

Wallaroo has already earned its stripes in security, retail, IoT, and many other use cases, but it’s particularly well suited to ML fraud detection. For example, in the case of credit card fraud, financial institutions can rely on Wallaroo’s real-time processing for highly precise insights based on recent transactions and historical customer behavior (like amounts, locations, spending patterns, etc.). 

This allows their models to predict the probability of fraud in milliseconds, as well as empower their data science team and business heads with up-to-date analytics, like the predicted number of fraud transactions and their potential value in dollars versus historical actuals. For a glimpse of what this looks like, choose a time on the graph below for an example of the summaries and detailed audit information Wallaroo can provide.

Freaud demo

Fraud may be as old as humanity itself, but that doesn’t mean your business should use outdated technology to fight it. Having the right AI platform in your corner can drive down costs and risks, make way for data science innovation, and make all the difference for your bottom line.

Get in touch today to protect your business with Wallaroo.