Rule-based vs. machine learning models

Legacy systems and rule-based fraud prevention models can hamstring fraud prevention efforts in the financial services industry. Learn how machine learning is changing the status quo.

Customer experience is a top priority across the financial services sector. From banking to payments and beyond, FinTechs and institutions are grappling with ways to enable faster, more secure payments without hindering the user experience. Machine learning (ML) promises to streamline and simplify customer-centricity by augmenting or entirely replacing error-prone, tedious, and slow manual processes that detract from a positive customer experience.

Fraud prevention, in particular, has room for improvement. According to the 2021 LexisNexis True Cost of Fraud Study, each dollar of fraud costs merchants $3.60— a whopping 15% increase since the pandemic. Considering that online payment fraud is predicted to exceed $206 Billion by 2025, the stakes are high.  But financial institutions also face fraud hurdles; online fraud challenges for banks include KYC/AML, email and device verification, and delay in transaction confirmation. While improving the customer experience is important, it would be misguided to say that it is at the top of any merchant’s — or banks’ — priority list. The reality is that fraud is expensive for all involved. Cutting costs related to the detection, prevention, and proliferation of fraud are headline items for those in the financial services sector. ML, however, may prove to have the silver bullet touch that FIs and FinTechs are looking for, to calm the storm.

Rule-Based vs. Machine Learning

ML affords financial organizations a novel ability: proactivity. In a complex and highly regulated industry like financial services, reactivity has long been the rule of thumb. Inability to access real-time insights or identify emerging fraud patterns has hamstrung financial service providers and merchants when it comes to being effective and efficient in fraud prevention. ML, on the other hand, enables real-time insights, identification of anomalies, and detection of subtle changes in patterns in large and dynamic data sets.

Taking it a step further, current, rules-based systems rely on pre-programmed rules to identify changes in behavior or predict outcomes. These rigid systems are not adaptable to an evolving and highly disrupted industry like financial services, which requires a more agile, flexible platform to overcome fraud challenges.

More specifically, rules-based systems fall short in many ways:

  • Rules-based approaches are time-intensive whereas an ML-based approach facilitates real-time processing
  • Rules-based requires manual work and supervision whereas ML enables automatic detection of anomalies
  • Rules-based requires multiple steps for verification that impede the user experience whereas ML reduces and simplifies the verification
  • And rules-based approaches identify obvious fraud patterns whereas ML is able to discover hidden fraud buried within subtle pattern changes

Perhaps the most important difference between rules-based systems and ML is the cost factor. ML relies on algorithms, which become more efficient and effective as data sets increase. With more data, the ML model improves and can detect similarities and differences across several different behaviors. As the ML model becomes informed on the difference between legitimate and fraudulent transactions, the system can more efficiently work through data sets to sort into one of the two categories. From there, ML models can also begin to make predictions for new transactions. Rules-based models, on the other hand, become more expensive to maintain as the data set or customer base increases. In short, they are far less scalable than ML models.

Applications of Machine Learning in Financial Services

ML is easily applicable to the prevention of online credit card fraud. Several service providers leverage the technology to identify and stop high-risk orders in real-time. Others pair machine learning with other emerging technologies like biometrics to simplify verification and authorization, both reducing fraud and improving the customer experience.

Other solutions providers are aiming to solve challenges on the issuing bank side of the equation by engineering risk assessment solutions that quickly and seamlessly analyze new online account applications for fast and accurate approvals. Other ML technologies leverage biometrics for user authentication. One company, ZOLOZ, taps ML to enable customers to use selfies for identity verification and authentication.

Even insurance and Insurtech stand to benefit from ML. All branches of insurance lose roughly $80 billion a year to fraudsters. ML has the ability to detect anomalies, flag them for review, and enable more accurate and efficient manual review processes. Some algorithms are so sophisticated that they can provide specific details about suspicious behavior and suggest remedial measures for such circumstances.

Conclusion

Electronic payments are on the rise and, unfortunately, are extremely vulnerable to fraud. ML can be extremely beneficial in payments and banking — to identify, detect, and prevent fraudulent transactions and activities without hindering the customer experience. By weighing the details of transactions and financial actions (via the accurate analysis of large volumes of data, very quickly), ML can make better decisions on behalf of humans.

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