How to accurately evaluate AI’s role in your APP fraud strategy

The rise of AI in counter-fraud defines a transformative era for Financial Services. This technological revolution, driven by the rising sophistication of financial fraud, has necessitated a shift towards more advanced, intelligent tools capable of outmanoeuvring the tactics of modern fraudsters. 


As criminals employ increasingly complex methods to exploit financial ecosystems, processes and consumers, AI emerges as a potential contender: an unprecedented means of identifying and mitigating fraudulent activities with precision and efficiency. It seems that AI is more than a trend. It is a framework for the future.


Here, our experts explain why for all its benefits, AI must be treated with caution by fraud leaders – especially in APP fraud strategies. Keep reading to understand which AI features to investigate and why.



  • Why has AI become so popular in counter-fraud? [Skip ahead]
  • Where is AI growing fastest in counter-fraud? [Skip ahead]
  • Why fraud leaders must approach AI with caution [Skip ahead]
  • Five considerations for your AI fraud prevention tools [Skip ahead]


Why has AI become so popular in counter-fraud?

By harnessing the power of machine learning and data analytics, some AI tools can analyse vast amounts of transaction data in real-time. AI can identify patterns, anomalies, and “normal” baselines faster than older technologies or the human eye can, and often makes connections that would otherwise have been missed. 


These capabilities are in high demand amongst counter-fraud and AML leaders. This is because (as you may have experienced), traditional fraud prevention methods are becoming less effective. 


Organised financial criminals are rapidly shifting their tactics and behaviours, often with the help of generative AI tools. Counter-fraud leaders need a data-crunching co-pilot if they’re to keep up. 



Where is AI growing fastest in counter-fraud?


AI is becoming particularly prevalent in the area of APP fraud prevention. And its growth makes sense. 


APP fraud, a sophisticated scam where victims are deceived into authorising payments to organised fraudsters, poses a unique challenge due to its reliance on social engineering and manipulation rather than exploitation of system vulnerabilities. 


Here, purely rules-based fraud prevention strategies alone are no match for the relentless innovation of organised fraudsters. AI-driven tools, however, continuously evolve, learning from new behaviour patterns to anticipate and counteract emerging fraud tactics.


Given that 2023’s legislative clampdown on APP fraud will to trigger a wave of novel behaviours, AI’s ability to learn and adapt is invaluable for counter-fraud leaders. 



Why must fraud leaders must approach AI with caution?


AI tools are undoubtedly part of the path forward - in APP fraud defence especially. That said, they are not a panacea. 


That’s because the nuanced nature of APP fraud, with its human-centric approach, requires a multifaceted defence mechanism. One that is not only technologically advanced and streamlined, but also adept at understanding human behaviour and the context of transactions. 


Therefore, when deploying AI tools in APP fraud prevention, we recommend a cautious approach centred on five critical challenges. 



Five considerations for your AI fraud prevention tools


  1. Complexity of social engineering tactics: APP fraud heavily relies on manipulating human psychology and AI models trained to detect unusual transaction patterns may not always capture the subtlety of social engineering tactics. Fraud leaders must ensure that AI is capable of, or supplemented with, tools that can analyse contextual information and recognise red flags that precede the transaction.

  2. Rapid evolution of APP fraud: Organised criminals continuously adapt their methods to bypass detection processes. Therefore, historical data may quickly become outdated as new forms of APP fraud emerge, and strategists must invest in adaptable AI platforms that evolve in step with emerging tactics. 

  3. Biases and ethical blind spots: If AI training data contains biases, AI models may perpetuate or even amplify these biases, leading to unfair treatment of certain groups. By working with a tool like Precision, fraud leaders can be confident that biases are mitigated, and adjust models to prevent unethical APP fraud outcomes. 

  4. A friction-right customer experience: There is always a risk of AI models flagging honest transactions as suspicious, which leads to false positives. Expert (and human) intervention is necessary to ensure that models minimise friction in legitimate transactions while effectively identifying fraud.

  5. Breadth of data sources and context: For AI to excel in fraud detection, it must leverage syndicated risk and fraud intelligence. By integrated shared intelligence, AI models can understand the broader context of transactions and better identify sophisticated schemes. 

This broader context is critical in preventing money muling – a type of APP fraud. On average, it takes eight months following account opening for mule activity to begin. Therefore, transaction modelling alone cannot be relied on. 


The future of AI in APP fraud prevention is not clear-cut


From navigating the complexities of social engineering tactics to ensuring the ethical application of AI, fraud leaders must exercise due diligence and seek the input of true counter-fraud AI specialists to balance power with risk. 


To learn how Precision, Synectics Solutions’ leading AI tool, can be used in your fraud prevention strategy, please contact one of our Fraud Consultants. Or, to see how other Financial Services organisations are using Precision, click here. 



Time to connect