Why Machine Learning is Key to Fraud Detection
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Why Machine Learning is Key to Fraud Detection

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The more the world relies on digital technology for financial transactions, the greater the industry’s risk for fraud-related losses and damages. In the first half of last year alone, £207.8 million was lost to authorised push payment fraud (APP), showing how new opportunities for financial services distribution provided by the digital space has also created an environment rife with fraudsters.

If counterfeit identification was required to steal money in the past, then it’s almost shocking to witness how a simple password hack can achieve the same result, sometimes with more devastating financial consequences. When fraud occurs, both conversions and customer loyalty dip significantly, leading to bank changes and other significant losses.

Given this digital climate, banking software solutions have become essential aspects of fraud detection and security. Most of the applications that tackle evolving technology used for fraud prevention are made possible by machine learning, which will be explained further.

 

Machine Learning and Rule-Based Systems

Fraud detection using machine learning (ML) has gained more attention in recent years, especially in comparison to rule-based fraud detection systems. The latter differs from ML by looking at on-surface and evident signals using algorithms to perform multiple fraud scenarios that were manually written by fraud analysts.

The downside is that they’re quite rigid and require the manual adjustment of scenarios, which is difficult considering real-time requirements of data today. While rule-based fraud detection systems dominate the market today, more leading financial institutions are starting to use ML technology too.

With ML-based fraud detection, detecting other layers of fraud is possible since machines create algorithms to process datasets with multiple variables and correlations between them. By drawing relationships between these events, ML is able to predict behaviour and fraudulent actions. There’s also less manual work involved with ML!

How ML Helps in Certain Fraudulent Scenarios

There are many types of financial fraud that can occur, and understanding which ones your company can be susceptible to is one way to take the proper measures for preventing it. Here are some things that ML can provide to help prevent fraud:

1. Insurance Claims Analysis

When considering insurance software solutions for fraud detection, good datasets and carefully selected models are crucial. The most common insurance claims issues are car insurance scams, property damage, and fake unemployment claims. Detecting falsified claims in the insurance industry is achieved using ML with semantic analysis capabilities, allowing the analysis of structured and unstructured texts. By analysing files for hidden textual clues, ML algorithms can identify suspicious correlations.

2. Medical Claims and Healthcare

With complex bureaucratic processes that typically require plenty of approvals, verification, and paperwork comes an opportunity for fraudsters to infiltrate. Common scams in healthcare include fake claims that use billing duplications or invalid social security numbers along with inauthentic medical records. Hospitals and insurance companies suffer as a result of such trickery.

Digital analysis is applied to detect upcoding and other abuse scams, revealing unexpected numbers in an existing dataset. This prevents the addition of fake charges on hospital billings and other abuse attempts! While ML isn’t typically required in such cases, it can augment rule-based fraud detection by digitising paper documentation for more in-depth analysis.

3. eCommerce

Many people fall victim to payment scams when shopping online, and many of the fraudulent activities that occur in these situations are related to identity theft and merchant scams. ML is used to perform behaviour analytics, which uses smart algorithms to uncover suspicious activities and identify inconsistencies in historical personal data sets.

Fraudsters can also work through the merchant, scamming customers by creating fake reviews. ML algorithms will eliminate fraudsters’ influence by running sentimental and behaviour analytics to detect suspicious activity from merchants and their products.

Conclusion

With machine learning methods available to resolve various fraudulent situations, there’s a better opportunity for businesses to protect themselves. Choosing the right ML solution will depend on the unique problem, available data, and resources, but using several models works best to ensure accuracy and streamlined assessments!

Anti-fraud systems should detect fraud in real-time, analyse user behaviour, improve data credibility, and identify hidden correlations. ML can make it easier to achieve such goals. Even though it has its drawbacks with needing copious amounts of data, it can be combined with tech consulting services to achieve excellent fraud detection and prevention results.

At Informatics, we focus on custom software development and infrastructure solutions and services. Whether you’re dealing with banking software or need basic IT consulting services, you can depend on our experience and expertise to provide the right solutions. Get in touch with us today!

Written by Daniele Paoletti